{
"cells": [
{
"cell_type": "markdown",
"id": "34eb66e3",
"metadata": {},
"source": [
"# REINFORCE and REINFORCE with baseline for continuous action spaces \n",
"\n",
"In this notebook we implement the Monte-Carlo based Reinforcement Learning algorithms, REINFORCE and REINFORCE with baseline for **continuous** action spaces, as they are introduced in the book by Sutton & Barto, Reinforcement Learning An Introduction (Second edition). The two algorithms are extensions of the discrete ones, which are derived from the *Policy Gradient Theorem* introduced in Chapter 13 of the book. When working with the *Gymnasium* (Formerly known as Gym) package, continuous action spaces are introduced as `Box(low, high, shape, dtype)` objects (see [`Box`](https://gymnasium.farama.org/api/spaces/fundamental/#gymnasium.spaces.Box) for more details).\n",
"\n",
"Specifically in this notebook, we will work with the *LunarLander-v2* **continuous** environment illustrated below, the gif was borrowed from [LunarLander Gymnasium webpage](https://gymnasium.farama.org/environments/box2d/lunar_lander/).\n",
"\n",
"\n",
"![](\"../assets/lunar_lander.gif\")
\n",
"\n",
"So, let's start."
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "c5f83bfa",
"metadata": {},
"outputs": [],
"source": [
"## Incomment the installation commands below only if running from Google Colab\n",
"## In case running locally, all the needed packages are listed in the requirements.txt in the main directory.\n",
"\n",
"# !pip install -q torch\n",
"# !pip install -q gymnasium[box2d]"
]
},
{
"cell_type": "markdown",
"id": "f0bb430f",
"metadata": {},
"source": [
"## Imports\n",
"We will use the PyTorch package for the Policy and Value-function approximations using neural networks."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "0fd4c526",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import torch as T\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"import torch.nn.functional as F\n",
"import gymnasium as gym\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"id": "129d85c5",
"metadata": {},
"source": [
"Let's create a utility function for plotting the results of different experiments"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b83abb26",
"metadata": {},
"outputs": [],
"source": [
"def plot_results(avg_scores, std_scores):\n",
" # plot the results from a set of experiments\n",
" n_episodes = len(avg_scores[0])\n",
" n_experiments = len(avg_scores)\n",
"\n",
" avg_scores = np.array(avg_scores)\n",
" avg_avg_scores = np.mean(avg_scores, axis=0)\n",
" std_avg_scores = np.std(avg_scores, axis=0)\n",
"\n",
" upper_std_scores = avg_avg_scores + std_avg_scores\n",
" lower_std_scores = avg_avg_scores - std_avg_scores\n",
" x = range(1, n_episodes + 1)\n",
"\n",
" fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(11, 6), sharex=True)\n",
" ax[0].set_title(\"Score averaged over different experiments\")\n",
" ax[0].plot(x, avg_avg_scores, label=f'average score')\n",
" ax[0].fill_between(x, upper_std_scores, lower_std_scores, \n",
" where=(upper_std_scores > lower_std_scores), alpha=0.3)\n",
"\n",
" for i in range(n_experiments):\n",
" avg_scores_numpy = np.array(avg_scores[i])\n",
" std_scores_numpy = np.array(std_scores[i])\n",
" upper = avg_scores_numpy + std_scores_numpy\n",
" lower = avg_scores_numpy - std_scores_numpy\n",
" ax[1].plot(x, avg_scores_numpy, label=f'Exp-{i}')\n",
" ax[1].fill_between(x, upper, lower, where=(upper > lower), alpha=0.3)\n",
"\n",
" ax[1].set_xlabel('episode')\n",
" ax[0].set_ylabel('score')\n",
" ax[1].set_ylabel('score')\n",
" ax[0].legend()\n",
" ax[0].grid()\n",
" ax[1].legend()\n",
" ax[1].grid()\n",
" plt.show()\n",
" \n",
"def compare_results(data_dict):\n",
" \"\"\"\n",
" Compare the results from different experiments\n",
" :param: data_dict = {name1: [avg_scores1, std_scores1], \n",
" name2: [avg_scores2, std_scores2],\n",
" ...\n",
" nameN: [avg_scoresN, std_scoresN],}\n",
" \"\"\"\n",
" \n",
" # plot the results from a set of experiments\n",
" fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(11, 6))\n",
" ax.set_title(\"Comparison of different RL algorithms averaged score over a set of experiments\")\n",
" \n",
" for item in data_dict:\n",
" name = item\n",
" avg_scores, std_scores = data_dict[name]\n",
" \n",
" n_episodes = len(avg_scores[0])\n",
" n_experiments = len(avg_scores)\n",
"\n",
" avg_scores = np.array(avg_scores)\n",
" avg_avg_scores = np.mean(avg_scores, axis=0)\n",
" std_avg_scores = np.std(avg_scores, axis=0)\n",
"\n",
" upper_std_scores = avg_avg_scores + std_avg_scores\n",
" lower_std_scores = avg_avg_scores - std_avg_scores\n",
" x = range(1, n_episodes + 1)\n",
"\n",
"\n",
" ax.plot(x, avg_avg_scores, label=f'{name} agent')\n",
" ax.fill_between(x, upper_std_scores, lower_std_scores, \n",
" where=(upper_std_scores > lower_std_scores), alpha=0.3)\n",
"\n",
"\n",
" ax.set_ylabel('score')\n",
" ax.legend()\n",
" ax.grid()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "872a854a",
"metadata": {},
"outputs": [],
"source": [
"# define the data_dict object\n",
"results_data_dict = {}"
]
},
{
"cell_type": "markdown",
"id": "a8f87cfc",
"metadata": {},
"source": [
"## Gymnasium Random Agent Check\n",
"\n",
"First, let us try the environment using a random agent, just to get the feel of it."
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "d64a15c8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Observation shape: (8,)\n",
"\n",
"Action shape: (2,)\n"
]
}
],
"source": [
"# initiate the environment\n",
"env = gym.make(\n",
" \"LunarLander-v2\",\n",
" continuous=True,\n",
" gravity=-10.0,\n",
" enable_wind=False,\n",
" wind_power=0.0,\n",
" turbulence_power=0.)\n",
"\n",
"# reset it and collect the initial state\n",
"observation, _ = env.reset()\n",
"\n",
"# sample a random action\n",
"action = env.action_space.sample()\n",
"\n",
"print(f\"Observation shape: {observation.shape}\")\n",
"print(f\"\\nAction shape: {action.shape}\")"
]
},
{
"cell_type": "code",
"execution_count": 70,
"id": "8368de0b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 Episode: 0 | Score: -92.321 | Avg score: -92.321 |\n",
"| Experiment: 0 Episode: 1 | Score: -247.125 | Avg score: -169.723 |\n",
"| Experiment: 0 Episode: 2 | Score: -96.268 | Avg score: -145.238 |\n",
"| Experiment: 0 Episode: 3 | Score: -386.114 | Avg score: -205.457 |\n",
"| Experiment: 0 Episode: 4 | Score: -134.576 | Avg score: -191.281 |\n",
"| Experiment: 0 Episode: 5 | Score: -512.935 | Avg score: -244.890 |\n",
"| Experiment: 0 Episode: 6 | Score: -13.318 | Avg score: -211.808 |\n",
"| Experiment: 0 Episode: 7 | Score: -64.698 | Avg score: -193.419 |\n",
"| Experiment: 0 Episode: 8 | Score: -115.320 | Avg score: -184.742 |\n",
"| Experiment: 0 Episode: 9 | Score: -444.045 | Avg score: -210.672 |\n",
"| Experiment: 0 Episode: 10 | Score: -321.929 | Avg score: -220.786 |\n",
"| Experiment: 0 Episode: 11 | Score: -242.940 | Avg score: -222.632 |\n",
"| Experiment: 0 Episode: 12 | Score: -115.352 | Avg score: -214.380 |\n",
"| Experiment: 0 Episode: 13 | Score: -431.500 | Avg score: -229.889 |\n",
"| Experiment: 0 Episode: 14 | Score: -370.040 | Avg score: -239.232 |\n",
"| Experiment: 0 Episode: 15 | Score: -537.606 | Avg score: -257.880 |\n",
"| Experiment: 0 Episode: 16 | Score: -119.434 | Avg score: -249.737 |\n",
"| Experiment: 0 Episode: 17 | Score: -154.951 | Avg score: -244.471 |\n",
"| Experiment: 0 Episode: 18 | Score: -94.791 | Avg score: -236.593 |\n",
"| Experiment: 0 Episode: 19 | Score: -79.656 | Avg score: -228.746 |\n",
"| Experiment: 0 Episode: 20 | Score: -188.801 | Avg score: -226.844 |\n",
"| Experiment: 0 Episode: 21 | Score: -403.305 | Avg score: -234.865 |\n",
"| Experiment: 0 Episode: 22 | Score: -75.184 | Avg score: -227.922 |\n",
"| Experiment: 0 Episode: 23 | Score: -172.842 | Avg score: -225.627 |\n",
"| Experiment: 0 Episode: 24 | Score: -401.414 | Avg score: -232.659 |\n",
"| Experiment: 0 Episode: 25 | Score: -275.110 | Avg score: -234.291 |\n",
"| Experiment: 0 Episode: 26 | Score: -258.158 | Avg score: -235.175 |\n",
"| Experiment: 0 Episode: 27 | Score: -399.300 | Avg score: -241.037 |\n",
"| Experiment: 0 Episode: 28 | Score: -102.283 | Avg score: -236.252 |\n",
"| Experiment: 0 Episode: 29 | Score: -396.047 | Avg score: -241.579 |\n",
"| Experiment: 0 Episode: 30 | Score: -292.617 | Avg score: -243.225 |\n",
"| Experiment: 0 Episode: 31 | Score: -314.130 | Avg score: -245.441 |\n",
"| Experiment: 0 Episode: 32 | Score: -118.493 | Avg score: -241.594 |\n",
"| Experiment: 0 Episode: 33 | Score: -327.634 | Avg score: -244.125 |\n",
"| Experiment: 0 Episode: 34 | Score: -362.399 | Avg score: -247.504 |\n",
"| Experiment: 0 Episode: 35 | Score: -70.499 | Avg score: -242.587 |\n",
"| Experiment: 0 Episode: 36 | Score: -314.396 | Avg score: -244.528 |\n",
"| Experiment: 0 Episode: 37 | Score: -27.633 | Avg score: -238.820 |\n",
"| Experiment: 0 Episode: 38 | Score: -126.019 | Avg score: -235.928 |\n",
"| Experiment: 0 Episode: 39 | Score: -95.707 | Avg score: -232.422 |\n",
"| Experiment: 0 Episode: 40 | Score: -227.017 | Avg score: -232.290 |\n",
"| Experiment: 0 Episode: 41 | Score: -371.086 | Avg score: -235.595 |\n",
"| Experiment: 0 Episode: 42 | Score: -496.950 | Avg score: -241.673 |\n",
"| Experiment: 0 Episode: 43 | Score: -165.951 | Avg score: -239.952 |\n",
"| Experiment: 0 Episode: 44 | Score: -241.420 | Avg score: -239.985 |\n",
"| Experiment: 0 Episode: 45 | Score: -117.288 | Avg score: -237.317 |\n",
"| Experiment: 0 Episode: 46 | Score: -155.456 | Avg score: -235.576 |\n",
"| Experiment: 0 Episode: 47 | Score: -122.278 | Avg score: -233.215 |\n",
"| Experiment: 0 Episode: 48 | Score: -431.391 | Avg score: -237.260 |\n",
"| Experiment: 0 Episode: 49 | Score: -132.639 | Avg score: -235.167 |\n",
"| Experiment: 0 Episode: 50 | Score: -269.218 | Avg score: -235.835 |\n",
"| Experiment: 0 Episode: 51 | Score: -52.749 | Avg score: -232.314 |\n",
"| Experiment: 0 Episode: 52 | Score: -141.560 | Avg score: -230.602 |\n",
"| Experiment: 0 Episode: 53 | Score: -203.426 | Avg score: -230.099 |\n",
"| Experiment: 0 Episode: 54 | Score: -147.874 | Avg score: -228.604 |\n",
"| Experiment: 0 Episode: 55 | Score: -295.291 | Avg score: -229.794 |\n",
"| Experiment: 0 Episode: 56 | Score: -147.714 | Avg score: -228.354 |\n",
"| Experiment: 0 Episode: 57 | Score: -228.414 | Avg score: -228.355 |\n",
"| Experiment: 0 Episode: 58 | Score: -136.916 | Avg score: -226.806 |\n",
"| Experiment: 0 Episode: 59 | Score: -153.784 | Avg score: -225.589 |\n",
"| Experiment: 0 Episode: 60 | Score: -335.793 | Avg score: -227.395 |\n",
"| Experiment: 0 Episode: 61 | Score: -52.497 | Avg score: -224.574 |\n",
"| Experiment: 0 Episode: 62 | Score: -113.200 | Avg score: -222.806 |\n",
"| Experiment: 0 Episode: 63 | Score: -243.073 | Avg score: -223.123 |\n",
"| Experiment: 0 Episode: 64 | Score: -36.981 | Avg score: -220.259 |\n",
"| Experiment: 0 Episode: 65 | Score: -170.390 | Avg score: -219.504 |\n",
"| Experiment: 0 Episode: 66 | Score: -158.273 | Avg score: -218.590 |\n",
"| Experiment: 0 Episode: 67 | Score: -114.883 | Avg score: -217.065 |\n",
"| Experiment: 0 Episode: 68 | Score: -224.153 | Avg score: -217.167 |\n",
"| Experiment: 0 Episode: 69 | Score: -105.644 | Avg score: -215.574 |\n",
"| Experiment: 0 Episode: 70 | Score: -397.988 | Avg score: -218.144 |\n",
"| Experiment: 0 Episode: 71 | Score: -183.370 | Avg score: -217.661 |\n",
"| Experiment: 0 Episode: 72 | Score: -211.904 | Avg score: -217.582 |\n",
"| Experiment: 0 Episode: 73 | Score: -33.099 | Avg score: -215.089 |\n",
"| Experiment: 0 Episode: 74 | Score: -93.204 | Avg score: -213.464 |\n",
"| Experiment: 0 Episode: 75 | Score: -113.716 | Avg score: -212.151 |\n",
"| Experiment: 0 Episode: 76 | Score: -330.657 | Avg score: -213.690 |\n",
"| Experiment: 0 Episode: 77 | Score: -154.574 | Avg score: -212.932 |\n",
"| Experiment: 0 Episode: 78 | Score: -321.607 | Avg score: -214.308 |\n",
"| Experiment: 0 Episode: 79 | Score: -218.763 | Avg score: -214.364 |\n",
"| Experiment: 0 Episode: 80 | Score: -116.406 | Avg score: -213.154 |\n",
"| Experiment: 0 Episode: 81 | Score: -456.350 | Avg score: -216.120 |\n",
"| Experiment: 0 Episode: 82 | Score: -167.539 | Avg score: -215.535 |\n",
"| Experiment: 0 Episode: 83 | Score: -368.524 | Avg score: -217.356 |\n",
"| Experiment: 0 Episode: 84 | Score: -244.343 | Avg score: -217.673 |\n",
"| Experiment: 0 Episode: 85 | Score: -29.567 | Avg score: -215.486 |\n",
"| Experiment: 0 Episode: 86 | Score: -108.804 | Avg score: -214.260 |\n",
"| Experiment: 0 Episode: 87 | Score: -289.660 | Avg score: -215.117 |\n",
"| Experiment: 0 Episode: 88 | Score: -190.693 | Avg score: -214.842 |\n",
"| Experiment: 0 Episode: 89 | Score: -492.636 | Avg score: -217.929 |\n",
"| Experiment: 0 Episode: 90 | Score: -293.945 | Avg score: -218.764 |\n",
"| Experiment: 0 Episode: 91 | Score: -144.146 | Avg score: -217.953 |\n",
"| Experiment: 0 Episode: 92 | Score: -93.520 | Avg score: -216.615 |\n",
"| Experiment: 0 Episode: 93 | Score: -163.609 | Avg score: -216.051 |\n",
"| Experiment: 0 Episode: 94 | Score: -132.414 | Avg score: -215.171 |\n",
"| Experiment: 0 Episode: 95 | Score: -329.491 | Avg score: -216.362 |\n",
"| Experiment: 0 Episode: 96 | Score: -238.422 | Avg score: -216.589 |\n",
"| Experiment: 0 Episode: 97 | Score: -403.666 | Avg score: -218.498 |\n",
"| Experiment: 0 Episode: 98 | Score: -350.228 | Avg score: -219.829 |\n",
"| Experiment: 0 Episode: 99 | Score: -153.911 | Avg score: -219.170 |\n",
"| Experiment: 0 Episode: 100 | Score: -55.446 | Avg score: -218.801 |\n",
"| Experiment: 0 Episode: 101 | Score: -70.751 | Avg score: -217.037 |\n",
"| Experiment: 0 Episode: 102 | Score: -33.469 | Avg score: -216.409 |\n",
"| Experiment: 0 Episode: 103 | Score: -364.514 | Avg score: -216.193 |\n",
"| Experiment: 0 Episode: 104 | Score: -68.549 | Avg score: -215.533 |\n",
"| Experiment: 0 Episode: 105 | Score: -64.995 | Avg score: -211.053 |\n",
"| Experiment: 0 Episode: 106 | Score: -43.685 | Avg score: -211.357 |\n",
"| Experiment: 0 Episode: 107 | Score: -191.967 | Avg score: -212.630 |\n",
"| Experiment: 0 Episode: 108 | Score: -95.327 | Avg score: -212.430 |\n",
"| Experiment: 0 Episode: 109 | Score: -101.898 | Avg score: -209.008 |\n",
"| Experiment: 0 Episode: 110 | Score: -59.267 | Avg score: -206.382 |\n",
"| Experiment: 0 Episode: 111 | Score: -111.015 | Avg score: -205.063 |\n",
"| Experiment: 0 Episode: 112 | Score: -46.981 | Avg score: -204.379 |\n",
"| Experiment: 0 Episode: 113 | Score: -189.132 | Avg score: -201.955 |\n",
"| Experiment: 0 Episode: 114 | Score: -354.619 | Avg score: -201.801 |\n",
"| Experiment: 0 Episode: 115 | Score: 18.854 | Avg score: -196.236 |\n",
"| Experiment: 0 Episode: 116 | Score: -106.677 | Avg score: -196.109 |\n",
"| Experiment: 0 Episode: 117 | Score: -96.262 | Avg score: -195.522 |\n",
"| Experiment: 0 Episode: 118 | Score: -207.887 | Avg score: -196.653 |\n",
"| Experiment: 0 Episode: 119 | Score: -421.419 | Avg score: -200.070 |\n",
"| Experiment: 0 Episode: 120 | Score: -228.049 | Avg score: -200.463 |\n",
"| Experiment: 0 Episode: 121 | Score: -427.645 | Avg score: -200.706 |\n",
"| Experiment: 0 Episode: 122 | Score: -126.773 | Avg score: -201.222 |\n",
"| Experiment: 0 Episode: 123 | Score: -174.737 | Avg score: -201.241 |\n",
"| Experiment: 0 Episode: 124 | Score: -375.777 | Avg score: -200.985 |\n",
"| Experiment: 0 Episode: 125 | Score: -163.777 | Avg score: -199.871 |\n",
"| Experiment: 0 Episode: 126 | Score: -404.663 | Avg score: -201.337 |\n",
"| Experiment: 0 Episode: 127 | Score: -283.098 | Avg score: -200.175 |\n",
"| Experiment: 0 Episode: 128 | Score: -242.033 | Avg score: -201.572 |\n",
"| Experiment: 0 Episode: 129 | Score: -160.001 | Avg score: -199.212 |\n",
"| Experiment: 0 Episode: 130 | Score: -150.126 | Avg score: -197.787 |\n",
"| Experiment: 0 Episode: 131 | Score: -32.559 | Avg score: -194.971 |\n",
"| Experiment: 0 Episode: 132 | Score: -70.216 | Avg score: -194.488 |\n",
"| Experiment: 0 Episode: 133 | Score: -145.287 | Avg score: -192.665 |\n",
"| Experiment: 0 Episode: 134 | Score: -75.295 | Avg score: -189.794 |\n",
"| Experiment: 0 Episode: 135 | Score: -36.268 | Avg score: -189.451 |\n",
"| Experiment: 0 Episode: 136 | Score: -293.534 | Avg score: -189.243 |\n",
"| Experiment: 0 Episode: 137 | Score: -335.218 | Avg score: -192.319 |\n",
"| Experiment: 0 Episode: 138 | Score: -69.814 | Avg score: -191.757 |\n",
"| Experiment: 0 Episode: 139 | Score: -48.987 | Avg score: -191.289 |\n",
"| Experiment: 0 Episode: 140 | Score: -240.757 | Avg score: -191.427 |\n",
"| Experiment: 0 Episode: 141 | Score: -235.335 | Avg score: -190.069 |\n",
"| Experiment: 0 Episode: 142 | Score: -105.146 | Avg score: -186.151 |\n",
"| Experiment: 0 Episode: 143 | Score: -142.721 | Avg score: -185.919 |\n",
"| Experiment: 0 Episode: 144 | Score: -63.737 | Avg score: -184.142 |\n",
"| Experiment: 0 Episode: 145 | Score: -264.400 | Avg score: -185.613 |\n",
"| Experiment: 0 Episode: 146 | Score: -242.341 | Avg score: -186.482 |\n",
"| Experiment: 0 Episode: 147 | Score: -304.393 | Avg score: -188.303 |\n",
"| Experiment: 0 Episode: 148 | Score: -56.698 | Avg score: -184.556 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 Episode: 149 | Score: -266.835 | Avg score: -185.898 |\n",
"| Experiment: 0 Episode: 150 | Score: -62.141 | Avg score: -183.827 |\n",
"| Experiment: 0 Episode: 151 | Score: -446.428 | Avg score: -187.764 |\n",
"| Experiment: 0 Episode: 152 | Score: -82.328 | Avg score: -187.172 |\n",
"| Experiment: 0 Episode: 153 | Score: -116.809 | Avg score: -186.306 |\n",
"| Experiment: 0 Episode: 154 | Score: -132.665 | Avg score: -186.154 |\n",
"| Experiment: 0 Episode: 155 | Score: -130.258 | Avg score: -184.503 |\n",
"| Experiment: 0 Episode: 156 | Score: -358.456 | Avg score: -186.611 |\n",
"| Experiment: 0 Episode: 157 | Score: -257.204 | Avg score: -186.899 |\n",
"| Experiment: 0 Episode: 158 | Score: -98.573 | Avg score: -186.515 |\n",
"| Experiment: 0 Episode: 159 | Score: -385.133 | Avg score: -188.829 |\n",
"| Experiment: 0 Episode: 160 | Score: -340.852 | Avg score: -188.879 |\n",
"| Experiment: 0 Episode: 161 | Score: -381.347 | Avg score: -192.168 |\n",
"| Experiment: 0 Episode: 162 | Score: -137.693 | Avg score: -192.413 |\n",
"| Experiment: 0 Episode: 163 | Score: -325.941 | Avg score: -193.241 |\n",
"| Experiment: 0 Episode: 164 | Score: -39.646 | Avg score: -193.268 |\n",
"| Experiment: 0 Episode: 165 | Score: -79.894 | Avg score: -192.363 |\n",
"| Experiment: 0 Episode: 166 | Score: -194.655 | Avg score: -192.727 |\n",
"| Experiment: 0 Episode: 167 | Score: -337.649 | Avg score: -194.955 |\n",
"| Experiment: 0 Episode: 168 | Score: -274.922 | Avg score: -195.462 |\n",
"| Experiment: 0 Episode: 169 | Score: -130.055 | Avg score: -195.706 |\n",
"| Experiment: 0 Episode: 170 | Score: -259.122 | Avg score: -194.318 |\n",
"| Experiment: 0 Episode: 171 | Score: -401.081 | Avg score: -196.495 |\n",
"| Experiment: 0 Episode: 172 | Score: -354.524 | Avg score: -197.921 |\n",
"| Experiment: 0 Episode: 173 | Score: -259.440 | Avg score: -200.184 |\n",
"| Experiment: 0 Episode: 174 | Score: -297.528 | Avg score: -202.228 |\n",
"| Experiment: 0 Episode: 175 | Score: -179.584 | Avg score: -202.886 |\n",
"| Experiment: 0 Episode: 176 | Score: 28.900 | Avg score: -199.291 |\n",
"| Experiment: 0 Episode: 177 | Score: -83.579 | Avg score: -198.581 |\n",
"| Experiment: 0 Episode: 178 | Score: -63.163 | Avg score: -195.996 |\n",
"| Experiment: 0 Episode: 179 | Score: -84.568 | Avg score: -194.654 |\n",
"| Experiment: 0 Episode: 180 | Score: -68.904 | Avg score: -194.179 |\n",
"| Experiment: 0 Episode: 181 | Score: -219.102 | Avg score: -191.807 |\n",
"| Experiment: 0 Episode: 182 | Score: -296.383 | Avg score: -193.095 |\n",
"| Experiment: 0 Episode: 183 | Score: -104.304 | Avg score: -190.453 |\n",
"| Experiment: 0 Episode: 184 | Score: -124.331 | Avg score: -189.253 |\n",
"| Experiment: 0 Episode: 185 | Score: -114.042 | Avg score: -190.098 |\n",
"| Experiment: 0 Episode: 186 | Score: -70.086 | Avg score: -189.711 |\n",
"| Experiment: 0 Episode: 187 | Score: -89.826 | Avg score: -187.712 |\n",
"| Experiment: 0 Episode: 188 | Score: -433.121 | Avg score: -190.137 |\n",
"| Experiment: 0 Episode: 189 | Score: -298.125 | Avg score: -188.191 |\n",
"| Experiment: 0 Episode: 190 | Score: -324.893 | Avg score: -188.501 |\n",
"| Experiment: 0 Episode: 191 | Score: -322.780 | Avg score: -190.287 |\n",
"| Experiment: 0 Episode: 192 | Score: -129.521 | Avg score: -190.647 |\n",
"| Experiment: 0 Episode: 193 | Score: -326.103 | Avg score: -192.272 |\n",
"| Experiment: 0 Episode: 194 | Score: -351.554 | Avg score: -194.464 |\n",
"| Experiment: 0 Episode: 195 | Score: -351.308 | Avg score: -194.682 |\n",
"| Experiment: 0 Episode: 196 | Score: -72.658 | Avg score: -193.024 |\n",
"| Experiment: 0 Episode: 197 | Score: -180.639 | Avg score: -190.794 |\n",
"| Experiment: 0 Episode: 198 | Score: -377.811 | Avg score: -191.070 |\n",
"| Experiment: 0 Episode: 199 | Score: 6.878 | Avg score: -189.462 |\n",
"| Experiment: 0 Episode: 200 | Score: -169.772 | Avg score: -190.605 |\n",
"| Experiment: 0 Episode: 201 | Score: -66.804 | Avg score: -190.566 |\n",
"| Experiment: 0 Episode: 202 | Score: -84.240 | Avg score: -191.073 |\n",
"| Experiment: 0 Episode: 203 | Score: -93.420 | Avg score: -188.362 |\n",
"| Experiment: 0 Episode: 204 | Score: 8.209 | Avg score: -187.595 |\n",
"| Experiment: 0 Episode: 205 | Score: -302.212 | Avg score: -189.967 |\n",
"| Experiment: 0 Episode: 206 | Score: -256.640 | Avg score: -192.096 |\n",
"| Experiment: 0 Episode: 207 | Score: -95.515 | Avg score: -191.132 |\n",
"| Experiment: 0 Episode: 208 | Score: -431.860 | Avg score: -194.497 |\n",
"| Experiment: 0 Episode: 209 | Score: -283.807 | Avg score: -196.316 |\n",
"| Experiment: 0 Episode: 210 | Score: -44.490 | Avg score: -196.169 |\n",
"| Experiment: 0 Episode: 211 | Score: -182.502 | Avg score: -196.883 |\n",
"| Experiment: 0 Episode: 212 | Score: -399.673 | Avg score: -200.410 |\n",
"| Experiment: 0 Episode: 213 | Score: -123.472 | Avg score: -199.754 |\n",
"| Experiment: 0 Episode: 214 | Score: -215.962 | Avg score: -198.367 |\n",
"| Experiment: 0 Episode: 215 | Score: -285.439 | Avg score: -201.410 |\n",
"| Experiment: 0 Episode: 216 | Score: -289.119 | Avg score: -203.235 |\n",
"| Experiment: 0 Episode: 217 | Score: -198.585 | Avg score: -204.258 |\n",
"| Experiment: 0 Episode: 218 | Score: -159.002 | Avg score: -203.769 |\n",
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"| Experiment: 0 Episode: 220 | Score: -220.443 | Avg score: -203.527 |\n",
"| Experiment: 0 Episode: 221 | Score: -292.681 | Avg score: -202.177 |\n",
"| Experiment: 0 Episode: 222 | Score: -272.074 | Avg score: -203.630 |\n",
"| Experiment: 0 Episode: 223 | Score: -116.441 | Avg score: -203.047 |\n",
"| Experiment: 0 Episode: 224 | Score: -325.803 | Avg score: -202.547 |\n",
"| Experiment: 0 Episode: 225 | Score: -179.405 | Avg score: -202.703 |\n",
"| Experiment: 0 Episode: 226 | Score: -331.567 | Avg score: -201.973 |\n",
"| Experiment: 0 Episode: 227 | Score: -166.841 | Avg score: -200.810 |\n",
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"| Experiment: 0 Episode: 230 | Score: -70.188 | Avg score: -201.501 |\n",
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"| Experiment: 0 Episode: 235 | Score: -29.984 | Avg score: -206.910 |\n",
"| Experiment: 0 Episode: 236 | Score: -260.912 | Avg score: -206.584 |\n",
"| Experiment: 0 Episode: 237 | Score: -139.773 | Avg score: -204.629 |\n",
"| Experiment: 0 Episode: 238 | Score: -166.102 | Avg score: -205.592 |\n",
"| Experiment: 0 Episode: 239 | Score: -348.035 | Avg score: -208.583 |\n",
"| Experiment: 0 Episode: 240 | Score: -244.872 | Avg score: -208.624 |\n",
"| Experiment: 0 Episode: 241 | Score: -307.145 | Avg score: -209.342 |\n",
"| Experiment: 0 Episode: 242 | Score: -155.274 | Avg score: -209.843 |\n",
"| Experiment: 0 Episode: 243 | Score: -51.661 | Avg score: -208.933 |\n",
"| Experiment: 0 Episode: 244 | Score: -290.264 | Avg score: -211.198 |\n",
"| Experiment: 0 Episode: 245 | Score: -335.056 | Avg score: -211.904 |\n",
"| Experiment: 0 Episode: 246 | Score: -333.744 | Avg score: -212.818 |\n",
"| Experiment: 0 Episode: 247 | Score: -209.563 | Avg score: -211.870 |\n",
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"| Experiment: 0 Episode: 253 | Score: -426.852 | Avg score: -214.248 |\n",
"| Experiment: 0 Episode: 254 | Score: -331.945 | Avg score: -216.241 |\n",
"| Experiment: 0 Episode: 255 | Score: -323.418 | Avg score: -218.172 |\n",
"| Experiment: 0 Episode: 256 | Score: -125.930 | Avg score: -215.847 |\n",
"| Experiment: 0 Episode: 257 | Score: -31.540 | Avg score: -213.591 |\n",
"| Experiment: 0 Episode: 258 | Score: -322.219 | Avg score: -215.827 |\n",
"| Experiment: 0 Episode: 259 | Score: -117.532 | Avg score: -213.151 |\n",
"| Experiment: 0 Episode: 260 | Score: -193.819 | Avg score: -211.681 |\n",
"| Experiment: 0 Episode: 261 | Score: -318.592 | Avg score: -211.053 |\n",
"| Experiment: 0 Episode: 262 | Score: -315.760 | Avg score: -212.834 |\n",
"| Experiment: 0 Episode: 263 | Score: -456.056 | Avg score: -214.135 |\n",
"| Experiment: 0 Episode: 264 | Score: -226.718 | Avg score: -216.006 |\n",
"| Experiment: 0 Episode: 265 | Score: -142.094 | Avg score: -216.628 |\n",
"| Experiment: 0 Episode: 266 | Score: -184.887 | Avg score: -216.530 |\n",
"| Experiment: 0 Episode: 267 | Score: -304.058 | Avg score: -216.194 |\n",
"| Experiment: 0 Episode: 268 | Score: -169.216 | Avg score: -215.137 |\n",
"| Experiment: 0 Episode: 269 | Score: -328.578 | Avg score: -217.122 |\n",
"| Experiment: 0 Episode: 270 | Score: -392.584 | Avg score: -218.457 |\n",
"| Experiment: 0 Episode: 271 | Score: 28.660 | Avg score: -214.159 |\n",
"| Experiment: 0 Episode: 272 | Score: -113.008 | Avg score: -211.744 |\n",
"| Experiment: 0 Episode: 273 | Score: -255.961 | Avg score: -211.710 |\n",
"| Experiment: 0 Episode: 274 | Score: -290.652 | Avg score: -211.641 |\n",
"| Experiment: 0 Episode: 275 | Score: -174.484 | Avg score: -211.590 |\n",
"| Experiment: 0 Episode: 276 | Score: -465.798 | Avg score: -216.537 |\n",
"| Experiment: 0 Episode: 277 | Score: -277.864 | Avg score: -218.480 |\n",
"| Experiment: 0 Episode: 278 | Score: -199.701 | Avg score: -219.845 |\n",
"| Experiment: 0 Episode: 279 | Score: -307.786 | Avg score: -222.077 |\n",
"| Experiment: 0 Episode: 280 | Score: -407.029 | Avg score: -225.458 |\n",
"| Experiment: 0 Episode: 281 | Score: -337.536 | Avg score: -226.643 |\n",
"| Experiment: 0 Episode: 282 | Score: -285.448 | Avg score: -226.533 |\n",
"| Experiment: 0 Episode: 283 | Score: -388.141 | Avg score: -229.372 |\n",
"| Experiment: 0 Episode: 284 | Score: -92.347 | Avg score: -229.052 |\n"
]
},
{
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"output_type": "stream",
"text": [
"| Experiment: 0 Episode: 285 | Score: -299.419 | Avg score: -230.906 |\n",
"| Experiment: 0 Episode: 286 | Score: -95.080 | Avg score: -231.156 |\n",
"| Experiment: 0 Episode: 287 | Score: -380.392 | Avg score: -234.061 |\n",
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"| Experiment: 0 Episode: 298 | Score: -251.035 | Avg score: -226.113 |\n",
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"| Experiment: 0 Episode: 300 | Score: -299.567 | Avg score: -229.438 |\n",
"| Experiment: 0 Episode: 301 | Score: -350.634 | Avg score: -232.276 |\n",
"| Experiment: 0 Episode: 302 | Score: -219.183 | Avg score: -233.625 |\n",
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"| Experiment: 0 Episode: 320 | Score: -408.674 | Avg score: -228.574 |\n",
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"| Experiment: 0 Episode: 322 | Score: -406.397 | Avg score: -230.617 |\n",
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"| Experiment: 0 Episode: 324 | Score: -129.368 | Avg score: -229.456 |\n",
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"| Experiment: 0 Episode: 336 | Score: -288.181 | Avg score: -230.415 |\n",
"| Experiment: 0 Episode: 337 | Score: -315.826 | Avg score: -232.175 |\n",
"| Experiment: 0 Episode: 338 | Score: -13.670 | Avg score: -230.651 |\n",
"| Experiment: 0 Episode: 339 | Score: -398.970 | Avg score: -231.160 |\n",
"| Experiment: 0 Episode: 340 | Score: -63.464 | Avg score: -229.346 |\n",
"| Experiment: 0 Episode: 341 | Score: -280.116 | Avg score: -229.076 |\n",
"| Experiment: 0 Episode: 342 | Score: -458.816 | Avg score: -232.111 |\n",
"| Experiment: 0 Episode: 343 | Score: -233.600 | Avg score: -233.931 |\n",
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"| Experiment: 0 Episode: 348 | Score: -70.273 | Avg score: -233.511 |\n",
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"| Experiment: 0 Episode: 359 | Score: -327.744 | Avg score: -239.469 |\n",
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"| Experiment: 0 Episode: 376 | Score: -214.686 | Avg score: -225.896 |\n",
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"| Experiment: 0 Episode: 379 | Score: -217.737 | Avg score: -226.224 |\n",
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"| Experiment: 0 Episode: 391 | Score: -368.441 | Avg score: -224.765 |\n",
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"| Experiment: 0 Episode: 399 | Score: -261.387 | Avg score: -217.436 |\n",
"| Experiment: 0 Episode: 400 | Score: -119.434 | Avg score: -215.635 |\n",
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"| Experiment: 0 Episode: 404 | Score: -462.138 | Avg score: -214.216 |\n",
"| Experiment: 0 Episode: 405 | Score: -73.286 | Avg score: -214.120 |\n",
"| Experiment: 0 Episode: 406 | Score: -54.474 | Avg score: -212.449 |\n",
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"| Experiment: 0 Episode: 412 | Score: -219.853 | Avg score: -214.881 |\n",
"| Experiment: 0 Episode: 413 | Score: -251.475 | Avg score: -215.518 |\n",
"| Experiment: 0 Episode: 414 | Score: -6.107 | Avg score: -215.339 |\n",
"| Experiment: 0 Episode: 415 | Score: -295.222 | Avg score: -213.871 |\n",
"| Experiment: 0 Episode: 416 | Score: -59.660 | Avg score: -213.440 |\n",
"| Experiment: 0 Episode: 417 | Score: -204.578 | Avg score: -213.998 |\n",
"| Experiment: 0 Episode: 418 | Score: -243.120 | Avg score: -214.471 |\n",
"| Experiment: 0 Episode: 419 | Score: -151.573 | Avg score: -214.226 |\n",
"| Experiment: 0 Episode: 420 | Score: -177.011 | Avg score: -211.909 |\n",
"| Experiment: 0 Episode: 421 | Score: -210.395 | Avg score: -210.387 |\n",
"| Experiment: 0 Episode: 422 | Score: -337.242 | Avg score: -209.695 |\n",
"| Experiment: 0 Episode: 423 | Score: -287.539 | Avg score: -210.603 |\n",
"| Experiment: 0 Episode: 424 | Score: -99.625 | Avg score: -210.306 |\n",
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"| Experiment: 0 Episode: 427 | Score: -317.602 | Avg score: -211.222 |\n",
"| Experiment: 0 Episode: 428 | Score: -249.109 | Avg score: -210.057 |\n",
"| Experiment: 0 Episode: 429 | Score: -397.857 | Avg score: -213.204 |\n",
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"| Experiment: 0 Episode: 431 | Score: -328.230 | Avg score: -216.600 |\n",
"| Experiment: 0 Episode: 432 | Score: -302.882 | Avg score: -218.198 |\n",
"| Experiment: 0 Episode: 433 | Score: -39.469 | Avg score: -215.450 |\n",
"| Experiment: 0 Episode: 434 | Score: -179.372 | Avg score: -215.836 |\n",
"| Experiment: 0 Episode: 435 | Score: -182.728 | Avg score: -216.816 |\n",
"| Experiment: 0 Episode: 436 | Score: -56.871 | Avg score: -214.503 |\n",
"| Experiment: 0 Episode: 437 | Score: -313.648 | Avg score: -214.482 |\n",
"| Experiment: 0 Episode: 438 | Score: -333.870 | Avg score: -217.684 |\n",
"| Experiment: 0 Episode: 439 | Score: -160.537 | Avg score: -215.299 |\n",
"| Experiment: 0 Episode: 440 | Score: -366.386 | Avg score: -218.328 |\n",
"| Experiment: 0 Episode: 441 | Score: -63.076 | Avg score: -216.158 |\n",
"| Experiment: 0 Episode: 442 | Score: -79.156 | Avg score: -212.361 |\n",
"| Experiment: 0 Episode: 443 | Score: -368.917 | Avg score: -213.715 |\n",
"| Experiment: 0 Episode: 444 | Score: -407.177 | Avg score: -214.857 |\n",
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"| Experiment: 0 Episode: 447 | Score: -241.239 | Avg score: -211.951 |\n",
"| Experiment: 0 Episode: 448 | Score: -270.003 | Avg score: -213.949 |\n",
"| Experiment: 0 Episode: 449 | Score: -380.041 | Avg score: -217.294 |\n",
"| Experiment: 0 Episode: 450 | Score: -397.896 | Avg score: -218.202 |\n",
"| Experiment: 0 Episode: 451 | Score: -162.325 | Avg score: -218.022 |\n",
"| Experiment: 0 Episode: 452 | Score: -105.502 | Avg score: -215.242 |\n",
"| Experiment: 0 Episode: 453 | Score: -228.003 | Avg score: -214.504 |\n",
"| Experiment: 0 Episode: 454 | Score: -141.157 | Avg score: -215.504 |\n",
"| Experiment: 0 Episode: 455 | Score: -33.691 | Avg score: -213.000 |\n",
"| Experiment: 0 Episode: 456 | Score: -239.785 | Avg score: -212.336 |\n",
"| Experiment: 0 Episode: 457 | Score: -122.948 | Avg score: -208.826 |\n",
"| Experiment: 0 Episode: 458 | Score: -139.823 | Avg score: -206.964 |\n",
"| Experiment: 0 Episode: 459 | Score: -140.081 | Avg score: -205.087 |\n",
"| Experiment: 0 Episode: 460 | Score: -160.788 | Avg score: -204.293 |\n",
"| Experiment: 0 Episode: 461 | Score: -104.970 | Avg score: -202.447 |\n",
"| Experiment: 0 Episode: 462 | Score: -405.543 | Avg score: -205.405 |\n",
"| Experiment: 0 Episode: 463 | Score: -268.290 | Avg score: -206.258 |\n",
"| Experiment: 0 Episode: 464 | Score: -61.767 | Avg score: -205.842 |\n",
"| Experiment: 0 Episode: 465 | Score: -69.151 | Avg score: -205.767 |\n",
"| Experiment: 0 Episode: 466 | Score: -180.531 | Avg score: -205.559 |\n",
"| Experiment: 0 Episode: 467 | Score: -521.595 | Avg score: -209.311 |\n",
"| Experiment: 0 Episode: 468 | Score: -33.132 | Avg score: -208.138 |\n"
]
},
{
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"output_type": "stream",
"text": [
"| Experiment: 0 Episode: 469 | Score: -33.551 | Avg score: -207.855 |\n",
"| Experiment: 0 Episode: 470 | Score: -507.230 | Avg score: -209.945 |\n",
"| Experiment: 0 Episode: 471 | Score: -67.183 | Avg score: -207.636 |\n",
"| Experiment: 0 Episode: 472 | Score: -160.794 | Avg score: -206.276 |\n",
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"| Experiment: 0 Episode: 474 | Score: -121.247 | Avg score: -209.074 |\n",
"| Experiment: 0 Episode: 475 | Score: -425.665 | Avg score: -212.597 |\n",
"| Experiment: 0 Episode: 476 | Score: -283.768 | Avg score: -213.288 |\n",
"| Experiment: 0 Episode: 477 | Score: -169.797 | Avg score: -211.476 |\n",
"| Experiment: 0 Episode: 478 | Score: -183.292 | Avg score: -210.815 |\n",
"| Experiment: 0 Episode: 479 | Score: -333.186 | Avg score: -211.969 |\n",
"| Experiment: 0 Episode: 480 | Score: -285.746 | Avg score: -213.031 |\n",
"| Experiment: 0 Episode: 481 | Score: -80.406 | Avg score: -210.251 |\n",
"| Experiment: 0 Episode: 482 | Score: -125.085 | Avg score: -210.298 |\n",
"| Experiment: 0 Episode: 483 | Score: -153.463 | Avg score: -209.245 |\n",
"| Experiment: 0 Episode: 484 | Score: -389.441 | Avg score: -212.470 |\n",
"| Experiment: 0 Episode: 485 | Score: -244.090 | Avg score: -210.838 |\n",
"| Experiment: 0 Episode: 486 | Score: -345.701 | Avg score: -211.320 |\n",
"| Experiment: 0 Episode: 487 | Score: -261.165 | Avg score: -212.731 |\n",
"| Experiment: 0 Episode: 488 | Score: -281.279 | Avg score: -214.495 |\n",
"| Experiment: 0 Episode: 489 | Score: -140.009 | Avg score: -214.955 |\n",
"| Experiment: 0 Episode: 490 | Score: -186.151 | Avg score: -214.861 |\n",
"| Experiment: 0 Episode: 491 | Score: -103.299 | Avg score: -212.210 |\n",
"| Experiment: 0 Episode: 492 | Score: -318.080 | Avg score: -210.844 |\n",
"| Experiment: 0 Episode: 493 | Score: -91.231 | Avg score: -210.781 |\n",
"| Experiment: 0 Episode: 494 | Score: -301.610 | Avg score: -213.118 |\n",
"| Experiment: 0 Episode: 495 | Score: -371.411 | Avg score: -215.702 |\n",
"| Experiment: 0 Episode: 496 | Score: -128.609 | Avg score: -216.369 |\n",
"| Experiment: 0 Episode: 497 | Score: -253.612 | Avg score: -217.821 |\n",
"| Experiment: 0 Episode: 498 | Score: -411.957 | Avg score: -219.545 |\n",
"| Experiment: 0 Episode: 499 | Score: -399.662 | Avg score: -220.928 |\n",
"| Experiment: 0 Episode: 500 | Score: -330.745 | Avg score: -223.041 |\n",
"| Experiment: 0 Episode: 501 | Score: -394.779 | Avg score: -222.981 |\n",
"| Experiment: 0 Episode: 502 | Score: -71.633 | Avg score: -222.806 |\n",
"| Experiment: 0 Episode: 503 | Score: -142.388 | Avg score: -223.789 |\n",
"| Experiment: 0 Episode: 504 | Score: -437.809 | Avg score: -223.546 |\n",
"| Experiment: 0 Episode: 505 | Score: -193.382 | Avg score: -224.747 |\n",
"| Experiment: 0 Episode: 506 | Score: -149.079 | Avg score: -225.693 |\n",
"| Experiment: 0 Episode: 507 | Score: -149.538 | Avg score: -222.501 |\n",
"| Experiment: 0 Episode: 508 | Score: -70.908 | Avg score: -222.201 |\n",
"| Experiment: 0 Episode: 509 | Score: -127.935 | Avg score: -222.092 |\n",
"| Experiment: 0 Episode: 510 | Score: -124.369 | Avg score: -223.135 |\n",
"| Experiment: 0 Episode: 511 | Score: -315.329 | Avg score: -224.190 |\n",
"| Experiment: 0 Episode: 512 | Score: -102.925 | Avg score: -223.021 |\n",
"| Experiment: 0 Episode: 513 | Score: -194.688 | Avg score: -222.453 |\n",
"| Experiment: 0 Episode: 514 | Score: -48.507 | Avg score: -222.877 |\n",
"| Experiment: 0 Episode: 515 | Score: -93.931 | Avg score: -220.864 |\n",
"| Experiment: 0 Episode: 516 | Score: -203.764 | Avg score: -222.305 |\n",
"| Experiment: 0 Episode: 517 | Score: -415.122 | Avg score: -224.410 |\n",
"| Experiment: 0 Episode: 518 | Score: -98.557 | Avg score: -222.965 |\n",
"| Experiment: 0 Episode: 519 | Score: -120.426 | Avg score: -222.653 |\n",
"| Experiment: 0 Episode: 520 | Score: -120.178 | Avg score: -222.085 |\n",
"| Experiment: 0 Episode: 521 | Score: -84.864 | Avg score: -220.830 |\n",
"| Experiment: 0 Episode: 522 | Score: -260.578 | Avg score: -220.063 |\n",
"| Experiment: 0 Episode: 523 | Score: -360.776 | Avg score: -220.795 |\n",
"| Experiment: 0 Episode: 524 | Score: -146.271 | Avg score: -221.262 |\n",
"| Experiment: 0 Episode: 525 | Score: -367.020 | Avg score: -222.196 |\n",
"| Experiment: 0 Episode: 526 | Score: -79.355 | Avg score: -219.145 |\n",
"| Experiment: 0 Episode: 527 | Score: -168.608 | Avg score: -217.655 |\n",
"| Experiment: 0 Episode: 528 | Score: -79.577 | Avg score: -215.960 |\n",
"| Experiment: 0 Episode: 529 | Score: -72.116 | Avg score: -212.703 |\n",
"| Experiment: 0 Episode: 530 | Score: -233.754 | Avg score: -212.400 |\n",
"| Experiment: 0 Episode: 531 | Score: -304.722 | Avg score: -212.165 |\n",
"| Experiment: 0 Episode: 532 | Score: -94.314 | Avg score: -210.079 |\n",
"| Experiment: 0 Episode: 533 | Score: -63.352 | Avg score: -210.318 |\n",
"| Experiment: 0 Episode: 534 | Score: -143.474 | Avg score: -209.959 |\n",
"| Experiment: 0 Episode: 535 | Score: -236.463 | Avg score: -210.497 |\n",
"| Experiment: 0 Episode: 536 | Score: -62.196 | Avg score: -210.550 |\n",
"| Experiment: 0 Episode: 537 | Score: -133.535 | Avg score: -208.749 |\n",
"| Experiment: 0 Episode: 538 | Score: -113.485 | Avg score: -206.545 |\n",
"| Experiment: 0 Episode: 539 | Score: -122.716 | Avg score: -206.167 |\n",
"| Experiment: 0 Episode: 540 | Score: -235.398 | Avg score: -204.857 |\n",
"| Experiment: 0 Episode: 541 | Score: -84.876 | Avg score: -205.075 |\n",
"| Experiment: 0 Episode: 542 | Score: -82.246 | Avg score: -205.106 |\n",
"| Experiment: 0 Episode: 543 | Score: -129.265 | Avg score: -202.709 |\n",
"| Experiment: 0 Episode: 544 | Score: -330.421 | Avg score: -201.942 |\n",
"| Experiment: 0 Episode: 545 | Score: -365.858 | Avg score: -204.407 |\n",
"| Experiment: 0 Episode: 546 | Score: -248.633 | Avg score: -204.370 |\n",
"| Experiment: 0 Episode: 547 | Score: -152.188 | Avg score: -203.479 |\n",
"| Experiment: 0 Episode: 548 | Score: -43.645 | Avg score: -201.216 |\n",
"| Experiment: 0 Episode: 549 | Score: -106.190 | Avg score: -198.477 |\n",
"| Experiment: 0 Episode: 550 | Score: -351.068 | Avg score: -198.009 |\n",
"| Experiment: 0 Episode: 551 | Score: -141.869 | Avg score: -197.805 |\n",
"| Experiment: 0 Episode: 552 | Score: -78.898 | Avg score: -197.538 |\n",
"| Experiment: 0 Episode: 553 | Score: -197.534 | Avg score: -197.234 |\n",
"| Experiment: 0 Episode: 554 | Score: -254.133 | Avg score: -198.364 |\n",
"| Experiment: 0 Episode: 555 | Score: -75.997 | Avg score: -198.787 |\n",
"| Experiment: 0 Episode: 556 | Score: -64.569 | Avg score: -197.034 |\n",
"| Experiment: 0 Episode: 557 | Score: -197.774 | Avg score: -197.783 |\n",
"| Experiment: 0 Episode: 558 | Score: -68.118 | Avg score: -197.066 |\n",
"| Experiment: 0 Episode: 559 | Score: -70.925 | Avg score: -196.374 |\n",
"| Experiment: 0 Episode: 560 | Score: -257.946 | Avg score: -197.346 |\n",
"| Experiment: 0 Episode: 561 | Score: -313.094 | Avg score: -199.427 |\n",
"| Experiment: 0 Episode: 562 | Score: -110.928 | Avg score: -196.481 |\n",
"| Experiment: 0 Episode: 563 | Score: -309.382 | Avg score: -196.892 |\n",
"| Experiment: 0 Episode: 564 | Score: -130.918 | Avg score: -197.583 |\n",
"| Experiment: 0 Episode: 565 | Score: -91.002 | Avg score: -197.802 |\n",
"| Experiment: 0 Episode: 566 | Score: -53.059 | Avg score: -196.527 |\n",
"| Experiment: 0 Episode: 567 | Score: -166.594 | Avg score: -192.977 |\n",
"| Experiment: 0 Episode: 568 | Score: -66.640 | Avg score: -193.312 |\n",
"| Experiment: 0 Episode: 569 | Score: -314.731 | Avg score: -196.124 |\n",
"| Experiment: 0 Episode: 570 | Score: -312.554 | Avg score: -194.177 |\n",
"| Experiment: 0 Episode: 571 | Score: -122.512 | Avg score: -194.730 |\n",
"| Experiment: 0 Episode: 572 | Score: -285.786 | Avg score: -195.980 |\n",
"| Experiment: 0 Episode: 573 | Score: -192.825 | Avg score: -194.297 |\n",
"| Experiment: 0 Episode: 574 | Score: -251.239 | Avg score: -195.597 |\n",
"| Experiment: 0 Episode: 575 | Score: -185.588 | Avg score: -193.196 |\n",
"| Experiment: 0 Episode: 576 | Score: -144.533 | Avg score: -191.804 |\n",
"| Experiment: 0 Episode: 577 | Score: -304.582 | Avg score: -193.152 |\n",
"| Experiment: 0 Episode: 578 | Score: -230.943 | Avg score: -193.628 |\n",
"| Experiment: 0 Episode: 579 | Score: -279.636 | Avg score: -193.093 |\n",
"| Experiment: 0 Episode: 580 | Score: -127.534 | Avg score: -191.511 |\n",
"| Experiment: 0 Episode: 581 | Score: -175.017 | Avg score: -192.457 |\n",
"| Experiment: 0 Episode: 582 | Score: -281.159 | Avg score: -194.017 |\n",
"| Experiment: 0 Episode: 583 | Score: -266.360 | Avg score: -195.146 |\n",
"| Experiment: 0 Episode: 584 | Score: -90.862 | Avg score: -192.161 |\n",
"| Experiment: 0 Episode: 585 | Score: -332.267 | Avg score: -193.042 |\n",
"| Experiment: 0 Episode: 586 | Score: -396.396 | Avg score: -193.549 |\n",
"| Experiment: 0 Episode: 587 | Score: -233.877 | Avg score: -193.276 |\n",
"| Experiment: 0 Episode: 588 | Score: -405.716 | Avg score: -194.521 |\n",
"| Experiment: 0 Episode: 589 | Score: -202.761 | Avg score: -195.148 |\n",
"| Experiment: 0 Episode: 590 | Score: -353.432 | Avg score: -196.821 |\n",
"| Experiment: 0 Episode: 591 | Score: -85.933 | Avg score: -196.648 |\n",
"| Experiment: 0 Episode: 592 | Score: -97.417 | Avg score: -194.441 |\n",
"| Experiment: 0 Episode: 593 | Score: -92.047 | Avg score: -194.449 |\n",
"| Experiment: 0 Episode: 594 | Score: -451.137 | Avg score: -195.944 |\n",
"| Experiment: 0 Episode: 595 | Score: 3.964 | Avg score: -192.191 |\n",
"| Experiment: 0 Episode: 596 | Score: -62.895 | Avg score: -191.533 |\n",
"| Experiment: 0 Episode: 597 | Score: -214.113 | Avg score: -191.138 |\n",
"| Experiment: 0 Episode: 598 | Score: -111.740 | Avg score: -188.136 |\n",
"| Experiment: 0 Episode: 599 | Score: -141.625 | Avg score: -185.556 |\n",
"| Experiment: 0 Episode: 600 | Score: -349.401 | Avg score: -185.742 |\n",
"| Experiment: 0 Episode: 601 | Score: -382.352 | Avg score: -185.618 |\n",
"| Experiment: 0 Episode: 602 | Score: -176.908 | Avg score: -186.671 |\n",
"| Experiment: 0 Episode: 603 | Score: -139.732 | Avg score: -186.644 |\n",
"| Experiment: 0 Episode: 604 | Score: -179.561 | Avg score: -184.062 |\n",
"| Experiment: 0 Episode: 605 | Score: -102.246 | Avg score: -183.151 |\n",
"| Experiment: 0 Episode: 606 | Score: -345.825 | Avg score: -185.118 |\n",
"| Experiment: 0 Episode: 607 | Score: -346.012 | Avg score: -187.083 |\n",
"| Experiment: 0 Episode: 608 | Score: -92.069 | Avg score: -187.294 |\n",
"| Experiment: 0 Episode: 609 | Score: -438.659 | Avg score: -190.402 |\n",
"| Experiment: 0 Episode: 610 | Score: -347.477 | Avg score: -192.633 |\n",
"| Experiment: 0 Episode: 611 | Score: -193.250 | Avg score: -191.412 |\n",
"| Experiment: 0 Episode: 612 | Score: -99.793 | Avg score: -191.381 |\n",
"| Experiment: 0 Episode: 613 | Score: -126.522 | Avg score: -190.699 |\n",
"| Experiment: 0 Episode: 614 | Score: -224.724 | Avg score: -192.461 |\n",
"| Experiment: 0 Episode: 615 | Score: -113.998 | Avg score: -192.662 |\n",
"| Experiment: 0 Episode: 616 | Score: -477.019 | Avg score: -195.394 |\n",
"| Experiment: 0 Episode: 617 | Score: -419.134 | Avg score: -195.434 |\n",
"| Experiment: 0 Episode: 618 | Score: -446.011 | Avg score: -198.909 |\n",
"| Experiment: 0 Episode: 619 | Score: -175.941 | Avg score: -199.464 |\n",
"| Experiment: 0 Episode: 620 | Score: -367.281 | Avg score: -201.935 |\n",
"| Experiment: 0 Episode: 621 | Score: -299.602 | Avg score: -204.082 |\n",
"| Experiment: 0 Episode: 622 | Score: -356.112 | Avg score: -205.038 |\n",
"| Experiment: 0 Episode: 623 | Score: -89.830 | Avg score: -202.328 |\n",
"| Experiment: 0 Episode: 624 | Score: -136.490 | Avg score: -202.231 |\n",
"| Experiment: 0 Episode: 625 | Score: -117.354 | Avg score: -199.734 |\n",
"| Experiment: 0 Episode: 626 | Score: -198.661 | Avg score: -200.927 |\n",
"| Experiment: 0 Episode: 627 | Score: -55.719 | Avg score: -199.798 |\n",
"| Experiment: 0 Episode: 628 | Score: -90.168 | Avg score: -199.904 |\n",
"| Experiment: 0 Episode: 629 | Score: -95.105 | Avg score: -200.134 |\n",
"| Experiment: 0 Episode: 630 | Score: -240.625 | Avg score: -200.203 |\n",
"| Experiment: 0 Episode: 631 | Score: -60.157 | Avg score: -197.757 |\n",
"| Experiment: 0 Episode: 632 | Score: -42.040 | Avg score: -197.234 |\n",
"| Experiment: 0 Episode: 633 | Score: -475.060 | Avg score: -201.351 |\n",
"| Experiment: 0 Episode: 634 | Score: -240.559 | Avg score: -202.322 |\n",
"| Experiment: 0 Episode: 635 | Score: -196.207 | Avg score: -201.920 |\n",
"| Experiment: 0 Episode: 636 | Score: -188.050 | Avg score: -203.178 |\n",
"| Experiment: 0 Episode: 637 | Score: -335.707 | Avg score: -205.200 |\n",
"| Experiment: 0 Episode: 638 | Score: -318.125 | Avg score: -207.246 |\n",
"| Experiment: 0 Episode: 639 | Score: -107.460 | Avg score: -207.094 |\n",
"| Experiment: 0 Episode: 640 | Score: -386.314 | Avg score: -208.603 |\n",
"| Experiment: 0 Episode: 641 | Score: -355.770 | Avg score: -211.312 |\n",
"| Experiment: 0 Episode: 642 | Score: -328.071 | Avg score: -213.770 |\n",
"| Experiment: 0 Episode: 643 | Score: -270.696 | Avg score: -215.184 |\n",
"| Experiment: 0 Episode: 644 | Score: -112.937 | Avg score: -213.009 |\n",
"| Experiment: 0 Episode: 645 | Score: -570.360 | Avg score: -215.054 |\n",
"| Experiment: 0 Episode: 646 | Score: 0.013 | Avg score: -212.568 |\n",
"| Experiment: 0 Episode: 647 | Score: -67.027 | Avg score: -211.716 |\n",
"| Experiment: 0 Episode: 648 | Score: -399.093 | Avg score: -215.271 |\n",
"| Experiment: 0 Episode: 649 | Score: -408.026 | Avg score: -218.289 |\n",
"| Experiment: 0 Episode: 650 | Score: -133.429 | Avg score: -216.113 |\n",
"| Experiment: 0 Episode: 651 | Score: -338.768 | Avg score: -218.082 |\n",
"| Experiment: 0 Episode: 652 | Score: -274.490 | Avg score: -220.038 |\n",
"| Experiment: 0 Episode: 653 | Score: -181.125 | Avg score: -219.874 |\n",
"| Experiment: 0 Episode: 654 | Score: -2.898 | Avg score: -217.361 |\n",
"| Experiment: 0 Episode: 655 | Score: -129.512 | Avg score: -217.896 |\n",
"| Experiment: 0 Episode: 656 | Score: -140.901 | Avg score: -218.660 |\n",
"| Experiment: 0 Episode: 657 | Score: -50.116 | Avg score: -217.183 |\n",
"| Experiment: 0 Episode: 658 | Score: -410.555 | Avg score: -220.608 |\n",
"| Experiment: 0 Episode: 659 | Score: -378.402 | Avg score: -223.682 |\n",
"| Experiment: 0 Episode: 660 | Score: -32.384 | Avg score: -221.427 |\n",
"| Experiment: 0 Episode: 661 | Score: -298.173 | Avg score: -221.278 |\n",
"| Experiment: 0 Episode: 662 | Score: -336.934 | Avg score: -223.538 |\n",
"| Experiment: 0 Episode: 663 | Score: -203.047 | Avg score: -222.474 |\n",
"| Experiment: 0 Episode: 664 | Score: -408.896 | Avg score: -225.254 |\n",
"| Experiment: 0 Episode: 665 | Score: -83.483 | Avg score: -225.179 |\n",
"| Experiment: 0 Episode: 666 | Score: -79.072 | Avg score: -225.439 |\n",
"| Experiment: 0 Episode: 667 | Score: -393.396 | Avg score: -227.707 |\n",
"| Experiment: 0 Episode: 668 | Score: -67.311 | Avg score: -227.714 |\n",
"| Experiment: 0 Episode: 669 | Score: -298.676 | Avg score: -227.553 |\n",
"| Experiment: 0 Episode: 670 | Score: -168.470 | Avg score: -226.112 |\n",
"| Experiment: 0 Episode: 671 | Score: -194.398 | Avg score: -226.831 |\n",
"| Experiment: 0 Episode: 672 | Score: -299.807 | Avg score: -226.971 |\n",
"| Experiment: 0 Episode: 673 | Score: -75.658 | Avg score: -225.800 |\n",
"| Experiment: 0 Episode: 674 | Score: -209.383 | Avg score: -225.381 |\n",
"| Experiment: 0 Episode: 675 | Score: -33.280 | Avg score: -223.858 |\n",
"| Experiment: 0 Episode: 676 | Score: -187.654 | Avg score: -224.289 |\n",
"| Experiment: 0 Episode: 677 | Score: -236.113 | Avg score: -223.605 |\n",
"| Experiment: 0 Episode: 678 | Score: -241.863 | Avg score: -223.714 |\n",
"| Experiment: 0 Episode: 679 | Score: -414.043 | Avg score: -225.058 |\n",
"| Experiment: 0 Episode: 680 | Score: -335.960 | Avg score: -227.142 |\n",
"| Experiment: 0 Episode: 681 | Score: -384.267 | Avg score: -229.235 |\n",
"| Experiment: 0 Episode: 682 | Score: -381.884 | Avg score: -230.242 |\n",
"| Experiment: 0 Episode: 683 | Score: -134.793 | Avg score: -228.926 |\n",
"| Experiment: 0 Episode: 684 | Score: -62.980 | Avg score: -228.647 |\n",
"| Experiment: 0 Episode: 685 | Score: -183.228 | Avg score: -227.157 |\n",
"| Experiment: 0 Episode: 686 | Score: -97.037 | Avg score: -224.163 |\n",
"| Experiment: 0 Episode: 687 | Score: -392.982 | Avg score: -225.754 |\n",
"| Experiment: 0 Episode: 688 | Score: -255.242 | Avg score: -224.250 |\n",
"| Experiment: 0 Episode: 689 | Score: -298.047 | Avg score: -225.203 |\n",
"| Experiment: 0 Episode: 690 | Score: -197.964 | Avg score: -223.648 |\n",
"| Experiment: 0 Episode: 691 | Score: -29.975 | Avg score: -223.088 |\n",
"| Experiment: 0 Episode: 692 | Score: -62.773 | Avg score: -222.742 |\n",
"| Experiment: 0 Episode: 693 | Score: -277.520 | Avg score: -224.597 |\n",
"| Experiment: 0 Episode: 694 | Score: -128.148 | Avg score: -221.367 |\n",
"| Experiment: 0 Episode: 695 | Score: -194.483 | Avg score: -223.351 |\n",
"| Experiment: 0 Episode: 696 | Score: -116.963 | Avg score: -223.892 |\n",
"| Experiment: 0 Episode: 697 | Score: -203.392 | Avg score: -223.785 |\n",
"| Experiment: 0 Episode: 698 | Score: -285.772 | Avg score: -225.525 |\n",
"| Experiment: 0 Episode: 699 | Score: -181.307 | Avg score: -225.922 |\n",
"| Experiment: 0 Episode: 700 | Score: -92.324 | Avg score: -223.351 |\n",
"| Experiment: 0 Episode: 701 | Score: -298.587 | Avg score: -222.513 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 Episode: 702 | Score: -249.750 | Avg score: -223.242 |\n",
"| Experiment: 0 Episode: 703 | Score: -391.588 | Avg score: -225.760 |\n",
"| Experiment: 0 Episode: 704 | Score: -89.959 | Avg score: -224.864 |\n",
"| Experiment: 0 Episode: 705 | Score: -72.758 | Avg score: -224.569 |\n",
"| Experiment: 0 Episode: 706 | Score: -34.315 | Avg score: -221.454 |\n",
"| Experiment: 0 Episode: 707 | Score: -232.873 | Avg score: -220.323 |\n",
"| Experiment: 0 Episode: 708 | Score: -400.435 | Avg score: -223.407 |\n",
"| Experiment: 0 Episode: 709 | Score: -260.497 | Avg score: -221.625 |\n",
"| Experiment: 0 Episode: 710 | Score: -61.404 | Avg score: -218.764 |\n",
"| Experiment: 0 Episode: 711 | Score: -88.976 | Avg score: -217.721 |\n",
"| Experiment: 0 Episode: 712 | Score: -402.085 | Avg score: -220.744 |\n",
"| Experiment: 0 Episode: 713 | Score: -318.349 | Avg score: -222.663 |\n",
"| Experiment: 0 Episode: 714 | Score: -316.118 | Avg score: -223.577 |\n",
"| Experiment: 0 Episode: 715 | Score: -106.389 | Avg score: -223.501 |\n",
"| Experiment: 0 Episode: 716 | Score: -111.800 | Avg score: -219.848 |\n",
"| Experiment: 0 Episode: 717 | Score: -378.870 | Avg score: -219.446 |\n",
"| Experiment: 0 Episode: 718 | Score: -391.051 | Avg score: -218.896 |\n",
"| Experiment: 0 Episode: 719 | Score: -134.760 | Avg score: -218.484 |\n",
"| Experiment: 0 Episode: 720 | Score: -252.269 | Avg score: -217.334 |\n",
"| Experiment: 0 Episode: 721 | Score: -81.225 | Avg score: -215.150 |\n",
"| Experiment: 0 Episode: 722 | Score: -275.153 | Avg score: -214.341 |\n",
"| Experiment: 0 Episode: 723 | Score: -382.037 | Avg score: -217.263 |\n",
"| Experiment: 0 Episode: 724 | Score: -299.908 | Avg score: -218.897 |\n",
"| Experiment: 0 Episode: 725 | Score: -154.596 | Avg score: -219.269 |\n",
"| Experiment: 0 Episode: 726 | Score: -408.537 | Avg score: -221.368 |\n",
"| Experiment: 0 Episode: 727 | Score: -310.685 | Avg score: -223.918 |\n",
"| Experiment: 0 Episode: 728 | Score: -374.127 | Avg score: -226.757 |\n",
"| Experiment: 0 Episode: 729 | Score: -107.127 | Avg score: -226.878 |\n",
"| Experiment: 0 Episode: 730 | Score: -267.348 | Avg score: -227.145 |\n",
"| Experiment: 0 Episode: 731 | Score: -452.663 | Avg score: -231.070 |\n",
"| Experiment: 0 Episode: 732 | Score: -120.627 | Avg score: -231.856 |\n",
"| Experiment: 0 Episode: 733 | Score: -353.573 | Avg score: -230.641 |\n",
"| Experiment: 0 Episode: 734 | Score: -163.261 | Avg score: -229.868 |\n",
"| Experiment: 0 Episode: 735 | Score: -136.800 | Avg score: -229.274 |\n",
"| Experiment: 0 Episode: 736 | Score: -246.296 | Avg score: -229.856 |\n",
"| Experiment: 0 Episode: 737 | Score: -116.824 | Avg score: -227.668 |\n",
"| Experiment: 0 Episode: 738 | Score: -273.512 | Avg score: -227.221 |\n",
"| Experiment: 0 Episode: 739 | Score: -21.869 | Avg score: -226.366 |\n",
"| Experiment: 0 Episode: 740 | Score: -127.092 | Avg score: -223.773 |\n",
"| Experiment: 0 Episode: 741 | Score: -77.481 | Avg score: -220.990 |\n",
"| Experiment: 0 Episode: 742 | Score: -70.721 | Avg score: -218.417 |\n",
"| Experiment: 0 Episode: 743 | Score: -50.067 | Avg score: -216.211 |\n",
"| Experiment: 0 Episode: 744 | Score: -91.606 | Avg score: -215.997 |\n",
"| Experiment: 0 Episode: 745 | Score: -130.337 | Avg score: -211.597 |\n",
"| Experiment: 0 Episode: 746 | Score: -374.269 | Avg score: -215.340 |\n",
"| Experiment: 0 Episode: 747 | Score: -392.232 | Avg score: -218.592 |\n",
"| Experiment: 0 Episode: 748 | Score: -100.233 | Avg score: -215.603 |\n",
"| Experiment: 0 Episode: 749 | Score: -134.835 | Avg score: -212.871 |\n",
"| Experiment: 0 Episode: 750 | Score: -345.659 | Avg score: -214.994 |\n",
"| Experiment: 0 Episode: 751 | Score: -77.857 | Avg score: -212.385 |\n",
"| Experiment: 0 Episode: 752 | Score: -309.456 | Avg score: -212.734 |\n",
"| Experiment: 0 Episode: 753 | Score: -121.061 | Avg score: -212.134 |\n",
"| Experiment: 0 Episode: 754 | Score: -116.496 | Avg score: -213.270 |\n",
"| Experiment: 0 Episode: 755 | Score: -104.150 | Avg score: -213.016 |\n",
"| Experiment: 0 Episode: 756 | Score: -429.717 | Avg score: -215.904 |\n",
"| Experiment: 0 Episode: 757 | Score: -274.966 | Avg score: -218.153 |\n",
"| Experiment: 0 Episode: 758 | Score: -348.628 | Avg score: -217.533 |\n",
"| Experiment: 0 Episode: 759 | Score: -194.073 | Avg score: -215.690 |\n",
"| Experiment: 0 Episode: 760 | Score: -468.539 | Avg score: -220.052 |\n",
"| Experiment: 0 Episode: 761 | Score: -261.638 | Avg score: -219.686 |\n",
"| Experiment: 0 Episode: 762 | Score: -150.027 | Avg score: -217.817 |\n",
"| Experiment: 0 Episode: 763 | Score: -117.585 | Avg score: -216.963 |\n",
"| Experiment: 0 Episode: 764 | Score: -141.932 | Avg score: -214.293 |\n",
"| Experiment: 0 Episode: 765 | Score: -342.191 | Avg score: -216.880 |\n",
"| Experiment: 0 Episode: 766 | Score: -93.021 | Avg score: -217.020 |\n",
"| Experiment: 0 Episode: 767 | Score: -351.601 | Avg score: -216.602 |\n",
"| Experiment: 0 Episode: 768 | Score: -130.401 | Avg score: -217.233 |\n",
"| Experiment: 0 Episode: 769 | Score: -49.996 | Avg score: -214.746 |\n",
"| Experiment: 0 Episode: 770 | Score: -67.938 | Avg score: -213.740 |\n",
"| Experiment: 0 Episode: 771 | Score: -260.260 | Avg score: -214.399 |\n",
"| Experiment: 0 Episode: 772 | Score: -332.138 | Avg score: -214.722 |\n",
"| Experiment: 0 Episode: 773 | Score: -319.777 | Avg score: -217.164 |\n",
"| Experiment: 0 Episode: 774 | Score: -389.985 | Avg score: -218.970 |\n",
"| Experiment: 0 Episode: 775 | Score: -224.337 | Avg score: -220.880 |\n",
"| Experiment: 0 Episode: 776 | Score: -215.178 | Avg score: -221.155 |\n",
"| Experiment: 0 Episode: 777 | Score: -165.960 | Avg score: -220.454 |\n",
"| Experiment: 0 Episode: 778 | Score: -166.172 | Avg score: -219.697 |\n",
"| Experiment: 0 Episode: 779 | Score: -122.291 | Avg score: -216.779 |\n",
"| Experiment: 0 Episode: 780 | Score: -425.192 | Avg score: -217.672 |\n",
"| Experiment: 0 Episode: 781 | Score: -104.175 | Avg score: -214.871 |\n",
"| Experiment: 0 Episode: 782 | Score: -54.531 | Avg score: -211.597 |\n",
"| Experiment: 0 Episode: 783 | Score: -55.461 | Avg score: -210.804 |\n",
"| Experiment: 0 Episode: 784 | Score: -162.332 | Avg score: -211.798 |\n",
"| Experiment: 0 Episode: 785 | Score: -202.307 | Avg score: -211.988 |\n",
"| Experiment: 0 Episode: 786 | Score: -52.082 | Avg score: -211.539 |\n",
"| Experiment: 0 Episode: 787 | Score: -427.498 | Avg score: -211.884 |\n",
"| Experiment: 0 Episode: 788 | Score: -446.978 | Avg score: -213.801 |\n",
"| Experiment: 0 Episode: 789 | Score: -188.689 | Avg score: -212.708 |\n",
"| Experiment: 0 Episode: 790 | Score: -68.557 | Avg score: -211.414 |\n",
"| Experiment: 0 Episode: 791 | Score: -340.837 | Avg score: -214.522 |\n",
"| Experiment: 0 Episode: 792 | Score: -473.364 | Avg score: -218.628 |\n",
"| Experiment: 0 Episode: 793 | Score: -105.624 | Avg score: -216.909 |\n",
"| Experiment: 0 Episode: 794 | Score: -318.789 | Avg score: -218.816 |\n",
"| Experiment: 0 Episode: 795 | Score: -77.436 | Avg score: -217.645 |\n",
"| Experiment: 0 Episode: 796 | Score: -263.092 | Avg score: -219.106 |\n",
"| Experiment: 0 Episode: 797 | Score: -244.043 | Avg score: -219.513 |\n",
"| Experiment: 0 Episode: 798 | Score: -86.242 | Avg score: -217.518 |\n",
"| Experiment: 0 Episode: 799 | Score: -399.973 | Avg score: -219.704 |\n",
"| Experiment: 0 Episode: 800 | Score: -452.670 | Avg score: -223.308 |\n",
"| Experiment: 0 Episode: 801 | Score: -204.134 | Avg score: -222.363 |\n",
"| Experiment: 0 Episode: 802 | Score: -42.143 | Avg score: -220.287 |\n",
"| Experiment: 0 Episode: 803 | Score: -113.564 | Avg score: -217.507 |\n",
"| Experiment: 0 Episode: 804 | Score: -72.762 | Avg score: -217.335 |\n",
"| Experiment: 0 Episode: 805 | Score: -167.099 | Avg score: -218.278 |\n",
"| Experiment: 0 Episode: 806 | Score: -33.049 | Avg score: -218.266 |\n",
"| Experiment: 0 Episode: 807 | Score: -413.839 | Avg score: -220.075 |\n",
"| Experiment: 0 Episode: 808 | Score: -67.338 | Avg score: -216.744 |\n",
"| Experiment: 0 Episode: 809 | Score: -110.666 | Avg score: -215.246 |\n",
"| Experiment: 0 Episode: 810 | Score: -302.897 | Avg score: -217.661 |\n",
"| Experiment: 0 Episode: 811 | Score: -44.761 | Avg score: -217.219 |\n",
"| Experiment: 0 Episode: 812 | Score: -347.373 | Avg score: -216.672 |\n",
"| Experiment: 0 Episode: 813 | Score: -89.105 | Avg score: -214.379 |\n",
"| Experiment: 0 Episode: 814 | Score: -97.833 | Avg score: -212.196 |\n",
"| Experiment: 0 Episode: 815 | Score: -571.340 | Avg score: -216.846 |\n",
"| Experiment: 0 Episode: 816 | Score: -364.613 | Avg score: -219.374 |\n",
"| Experiment: 0 Episode: 817 | Score: -304.565 | Avg score: -218.631 |\n",
"| Experiment: 0 Episode: 818 | Score: -166.690 | Avg score: -216.387 |\n",
"| Experiment: 0 Episode: 819 | Score: -92.708 | Avg score: -215.967 |\n",
"| Experiment: 0 Episode: 820 | Score: -180.788 | Avg score: -215.252 |\n",
"| Experiment: 0 Episode: 821 | Score: -87.683 | Avg score: -215.317 |\n",
"| Experiment: 0 Episode: 822 | Score: -56.996 | Avg score: -213.135 |\n",
"| Experiment: 0 Episode: 823 | Score: -104.686 | Avg score: -210.362 |\n",
"| Experiment: 0 Episode: 824 | Score: -358.159 | Avg score: -210.944 |\n",
"| Experiment: 0 Episode: 825 | Score: -115.986 | Avg score: -210.558 |\n",
"| Experiment: 0 Episode: 826 | Score: -142.820 | Avg score: -207.901 |\n",
"| Experiment: 0 Episode: 827 | Score: -276.388 | Avg score: -207.558 |\n",
"| Experiment: 0 Episode: 828 | Score: -149.229 | Avg score: -205.309 |\n",
"| Experiment: 0 Episode: 829 | Score: -132.319 | Avg score: -205.561 |\n",
"| Experiment: 0 Episode: 830 | Score: -57.587 | Avg score: -203.463 |\n",
"| Experiment: 0 Episode: 831 | Score: -293.707 | Avg score: -201.874 |\n",
"| Experiment: 0 Episode: 832 | Score: -391.107 | Avg score: -204.578 |\n",
"| Experiment: 0 Episode: 833 | Score: -78.037 | Avg score: -201.823 |\n",
"| Experiment: 0 Episode: 834 | Score: -105.161 | Avg score: -201.242 |\n",
"| Experiment: 0 Episode: 835 | Score: -513.001 | Avg score: -205.004 |\n",
"| Experiment: 0 Episode: 836 | Score: -331.806 | Avg score: -205.859 |\n",
"| Experiment: 0 Episode: 837 | Score: -43.263 | Avg score: -205.124 |\n",
"| Experiment: 0 Episode: 838 | Score: -6.954 | Avg score: -202.458 |\n",
"| Experiment: 0 Episode: 839 | Score: -318.502 | Avg score: -205.424 |\n",
"| Experiment: 0 Episode: 840 | Score: 2.876 | Avg score: -204.125 |\n",
"| Experiment: 0 Episode: 841 | Score: -93.681 | Avg score: -204.287 |\n",
"| Experiment: 0 Episode: 842 | Score: -157.017 | Avg score: -205.150 |\n",
"| Experiment: 0 Episode: 843 | Score: -270.683 | Avg score: -207.356 |\n",
"| Experiment: 0 Episode: 844 | Score: -52.367 | Avg score: -206.963 |\n",
"| Experiment: 0 Episode: 845 | Score: -66.583 | Avg score: -206.326 |\n",
"| Experiment: 0 Episode: 846 | Score: -396.395 | Avg score: -206.547 |\n",
"| Experiment: 0 Episode: 847 | Score: 43.498 | Avg score: -202.190 |\n",
"| Experiment: 0 Episode: 848 | Score: -46.909 | Avg score: -201.657 |\n",
"| Experiment: 0 Episode: 849 | Score: -118.002 | Avg score: -201.488 |\n",
"| Experiment: 0 Episode: 850 | Score: -96.931 | Avg score: -199.001 |\n",
"| Experiment: 0 Episode: 851 | Score: -189.816 | Avg score: -200.121 |\n",
"| Experiment: 0 Episode: 852 | Score: -67.354 | Avg score: -197.700 |\n",
"| Experiment: 0 Episode: 853 | Score: -176.252 | Avg score: -198.251 |\n",
"| Experiment: 0 Episode: 854 | Score: -237.385 | Avg score: -199.460 |\n",
"| Experiment: 0 Episode: 855 | Score: -254.300 | Avg score: -200.962 |\n",
"| Experiment: 0 Episode: 856 | Score: -84.756 | Avg score: -197.512 |\n",
"| Experiment: 0 Episode: 857 | Score: -152.816 | Avg score: -196.291 |\n",
"| Experiment: 0 Episode: 858 | Score: -85.454 | Avg score: -193.659 |\n",
"| Experiment: 0 Episode: 859 | Score: -6.023 | Avg score: -191.778 |\n",
"| Experiment: 0 Episode: 860 | Score: -60.993 | Avg score: -187.703 |\n",
"| Experiment: 0 Episode: 861 | Score: -152.470 | Avg score: -186.611 |\n",
"| Experiment: 0 Episode: 862 | Score: -277.598 | Avg score: -187.887 |\n",
"| Experiment: 0 Episode: 863 | Score: -187.645 | Avg score: -188.588 |\n",
"| Experiment: 0 Episode: 864 | Score: -88.585 | Avg score: -188.054 |\n",
"| Experiment: 0 Episode: 865 | Score: -468.653 | Avg score: -189.319 |\n",
"| Experiment: 0 Episode: 866 | Score: -296.431 | Avg score: -191.353 |\n",
"| Experiment: 0 Episode: 867 | Score: -116.993 | Avg score: -189.007 |\n",
"| Experiment: 0 Episode: 868 | Score: -91.560 | Avg score: -188.618 |\n",
"| Experiment: 0 Episode: 869 | Score: -277.578 | Avg score: -190.894 |\n",
"| Experiment: 0 Episode: 870 | Score: -276.379 | Avg score: -192.979 |\n",
"| Experiment: 0 Episode: 871 | Score: -98.413 | Avg score: -191.360 |\n",
"| Experiment: 0 Episode: 872 | Score: -110.687 | Avg score: -189.146 |\n",
"| Experiment: 0 Episode: 873 | Score: -315.931 | Avg score: -189.107 |\n",
"| Experiment: 0 Episode: 874 | Score: -107.089 | Avg score: -186.278 |\n",
"| Experiment: 0 Episode: 875 | Score: -132.206 | Avg score: -185.357 |\n",
"| Experiment: 0 Episode: 876 | Score: -175.259 | Avg score: -184.958 |\n",
"| Experiment: 0 Episode: 877 | Score: -412.144 | Avg score: -187.420 |\n",
"| Experiment: 0 Episode: 878 | Score: -262.310 | Avg score: -188.381 |\n",
"| Experiment: 0 Episode: 879 | Score: -29.491 | Avg score: -187.453 |\n",
"| Experiment: 0 Episode: 880 | Score: -23.494 | Avg score: -183.436 |\n",
"| Experiment: 0 Episode: 881 | Score: -101.110 | Avg score: -183.405 |\n",
"| Experiment: 0 Episode: 882 | Score: -441.868 | Avg score: -187.279 |\n",
"| Experiment: 0 Episode: 883 | Score: -271.403 | Avg score: -189.438 |\n",
"| Experiment: 0 Episode: 884 | Score: -192.415 | Avg score: -189.739 |\n",
"| Experiment: 0 Episode: 885 | Score: -186.738 | Avg score: -189.583 |\n",
"| Experiment: 0 Episode: 886 | Score: -226.690 | Avg score: -191.329 |\n",
"| Experiment: 0 Episode: 887 | Score: -130.449 | Avg score: -188.359 |\n",
"| Experiment: 0 Episode: 888 | Score: -340.279 | Avg score: -187.292 |\n",
"| Experiment: 0 Episode: 889 | Score: -53.702 | Avg score: -185.942 |\n",
"| Experiment: 0 Episode: 890 | Score: -542.357 | Avg score: -190.680 |\n",
"| Experiment: 0 Episode: 891 | Score: -77.774 | Avg score: -188.049 |\n",
"| Experiment: 0 Episode: 892 | Score: -133.056 | Avg score: -184.646 |\n",
"| Experiment: 0 Episode: 893 | Score: -116.671 | Avg score: -184.757 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 Episode: 894 | Score: -101.942 | Avg score: -182.588 |\n",
"| Experiment: 0 Episode: 895 | Score: -287.519 | Avg score: -184.689 |\n",
"| Experiment: 0 Episode: 896 | Score: -205.259 | Avg score: -184.111 |\n",
"| Experiment: 0 Episode: 897 | Score: -366.669 | Avg score: -185.337 |\n",
"| Experiment: 0 Episode: 898 | Score: -126.904 | Avg score: -185.744 |\n",
"| Experiment: 0 Episode: 899 | Score: -348.196 | Avg score: -185.226 |\n",
"| Experiment: 0 Episode: 900 | Score: -183.683 | Avg score: -182.536 |\n",
"| Experiment: 0 Episode: 901 | Score: -148.485 | Avg score: -181.980 |\n",
"| Experiment: 0 Episode: 902 | Score: -32.070 | Avg score: -181.879 |\n",
"| Experiment: 0 Episode: 903 | Score: -246.624 | Avg score: -183.209 |\n",
"| Experiment: 0 Episode: 904 | Score: -435.153 | Avg score: -186.833 |\n",
"| Experiment: 0 Episode: 905 | Score: -24.890 | Avg score: -185.411 |\n",
"| Experiment: 0 Episode: 906 | Score: -109.352 | Avg score: -186.174 |\n",
"| Experiment: 0 Episode: 907 | Score: -164.924 | Avg score: -183.685 |\n",
"| Experiment: 0 Episode: 908 | Score: -213.080 | Avg score: -185.142 |\n",
"| Experiment: 0 Episode: 909 | Score: -268.655 | Avg score: -186.722 |\n",
"| Experiment: 0 Episode: 910 | Score: -62.963 | Avg score: -184.323 |\n",
"| Experiment: 0 Episode: 911 | Score: -386.344 | Avg score: -187.739 |\n",
"| Experiment: 0 Episode: 912 | Score: -302.765 | Avg score: -187.293 |\n",
"| Experiment: 0 Episode: 913 | Score: -159.635 | Avg score: -187.998 |\n",
"| Experiment: 0 Episode: 914 | Score: -374.975 | Avg score: -190.770 |\n",
"| Experiment: 0 Episode: 915 | Score: -360.158 | Avg score: -188.658 |\n",
"| Experiment: 0 Episode: 916 | Score: -154.834 | Avg score: -186.560 |\n",
"| Experiment: 0 Episode: 917 | Score: -128.968 | Avg score: -184.804 |\n",
"| Experiment: 0 Episode: 918 | Score: -345.669 | Avg score: -186.594 |\n",
"| Experiment: 0 Episode: 919 | Score: -174.873 | Avg score: -187.415 |\n",
"| Experiment: 0 Episode: 920 | Score: -375.760 | Avg score: -189.365 |\n",
"| Experiment: 0 Episode: 921 | Score: -143.637 | Avg score: -189.925 |\n",
"| Experiment: 0 Episode: 922 | Score: -33.712 | Avg score: -189.692 |\n",
"| Experiment: 0 Episode: 923 | Score: -100.747 | Avg score: -189.652 |\n",
"| Experiment: 0 Episode: 924 | Score: -148.643 | Avg score: -187.557 |\n",
"| Experiment: 0 Episode: 925 | Score: -148.368 | Avg score: -187.881 |\n",
"| Experiment: 0 Episode: 926 | Score: -313.335 | Avg score: -189.586 |\n",
"| Experiment: 0 Episode: 927 | Score: -376.802 | Avg score: -190.590 |\n",
"| Experiment: 0 Episode: 928 | Score: -238.064 | Avg score: -191.479 |\n",
"| Experiment: 0 Episode: 929 | Score: -103.914 | Avg score: -191.195 |\n",
"| Experiment: 0 Episode: 930 | Score: -280.815 | Avg score: -193.427 |\n",
"| Experiment: 0 Episode: 931 | Score: -81.950 | Avg score: -191.309 |\n",
"| Experiment: 0 Episode: 932 | Score: -94.350 | Avg score: -188.342 |\n",
"| Experiment: 0 Episode: 933 | Score: -354.052 | Avg score: -191.102 |\n",
"| Experiment: 0 Episode: 934 | Score: -260.102 | Avg score: -192.651 |\n",
"| Experiment: 0 Episode: 935 | Score: -78.394 | Avg score: -188.305 |\n",
"| Experiment: 0 Episode: 936 | Score: -390.955 | Avg score: -188.897 |\n",
"| Experiment: 0 Episode: 937 | Score: -267.049 | Avg score: -191.135 |\n",
"| Experiment: 0 Episode: 938 | Score: -71.873 | Avg score: -191.784 |\n",
"| Experiment: 0 Episode: 939 | Score: -296.726 | Avg score: -191.566 |\n",
"| Experiment: 0 Episode: 940 | Score: -84.088 | Avg score: -192.436 |\n",
"| Experiment: 0 Episode: 941 | Score: -83.931 | Avg score: -192.338 |\n",
"| Experiment: 0 Episode: 942 | Score: -393.737 | Avg score: -194.705 |\n",
"| Experiment: 0 Episode: 943 | Score: -73.669 | Avg score: -192.735 |\n",
"| Experiment: 0 Episode: 944 | Score: -285.971 | Avg score: -195.071 |\n",
"| Experiment: 0 Episode: 945 | Score: -65.907 | Avg score: -195.065 |\n",
"| Experiment: 0 Episode: 946 | Score: -78.757 | Avg score: -191.888 |\n",
"| Experiment: 0 Episode: 947 | Score: -436.341 | Avg score: -196.687 |\n",
"| Experiment: 0 Episode: 948 | Score: -65.298 | Avg score: -196.870 |\n",
"| Experiment: 0 Episode: 949 | Score: -84.885 | Avg score: -196.539 |\n",
"| Experiment: 0 Episode: 950 | Score: -369.909 | Avg score: -199.269 |\n",
"| Experiment: 0 Episode: 951 | Score: -297.992 | Avg score: -200.351 |\n",
"| Experiment: 0 Episode: 952 | Score: -199.420 | Avg score: -201.671 |\n",
"| Experiment: 0 Episode: 953 | Score: -147.402 | Avg score: -201.383 |\n",
"| Experiment: 0 Episode: 954 | Score: -91.283 | Avg score: -199.922 |\n",
"| Experiment: 0 Episode: 955 | Score: -103.157 | Avg score: -198.411 |\n",
"| Experiment: 0 Episode: 956 | Score: -22.070 | Avg score: -197.784 |\n",
"| Experiment: 0 Episode: 957 | Score: -153.750 | Avg score: -197.793 |\n",
"| Experiment: 0 Episode: 958 | Score: -97.793 | Avg score: -197.916 |\n",
"| Experiment: 0 Episode: 959 | Score: -277.448 | Avg score: -200.631 |\n",
"| Experiment: 0 Episode: 960 | Score: -44.877 | Avg score: -200.469 |\n",
"| Experiment: 0 Episode: 961 | Score: -161.828 | Avg score: -200.563 |\n",
"| Experiment: 0 Episode: 962 | Score: -109.390 | Avg score: -198.881 |\n",
"| Experiment: 0 Episode: 963 | Score: -379.327 | Avg score: -200.798 |\n",
"| Experiment: 0 Episode: 964 | Score: -386.102 | Avg score: -203.773 |\n",
"| Experiment: 0 Episode: 965 | Score: -275.081 | Avg score: -201.837 |\n",
"| Experiment: 0 Episode: 966 | Score: -299.886 | Avg score: -201.872 |\n",
"| Experiment: 0 Episode: 967 | Score: -96.393 | Avg score: -201.666 |\n",
"| Experiment: 0 Episode: 968 | Score: -506.902 | Avg score: -205.819 |\n",
"| Experiment: 0 Episode: 969 | Score: -231.063 | Avg score: -205.354 |\n",
"| Experiment: 0 Episode: 970 | Score: -155.652 | Avg score: -204.147 |\n",
"| Experiment: 0 Episode: 971 | Score: -333.629 | Avg score: -206.499 |\n",
"| Experiment: 0 Episode: 972 | Score: -320.756 | Avg score: -208.600 |\n",
"| Experiment: 0 Episode: 973 | Score: -194.366 | Avg score: -207.384 |\n",
"| Experiment: 0 Episode: 974 | Score: -77.125 | Avg score: -207.084 |\n",
"| Experiment: 0 Episode: 975 | Score: -496.314 | Avg score: -210.725 |\n",
"| Experiment: 0 Episode: 976 | Score: -201.535 | Avg score: -210.988 |\n",
"| Experiment: 0 Episode: 977 | Score: -124.296 | Avg score: -208.110 |\n",
"| Experiment: 0 Episode: 978 | Score: -343.193 | Avg score: -208.919 |\n",
"| Experiment: 0 Episode: 979 | Score: -198.222 | Avg score: -210.606 |\n",
"| Experiment: 0 Episode: 980 | Score: -444.668 | Avg score: -214.818 |\n",
"| Experiment: 0 Episode: 981 | Score: -385.640 | Avg score: -217.663 |\n",
"| Experiment: 0 Episode: 982 | Score: -245.125 | Avg score: -215.695 |\n",
"| Experiment: 0 Episode: 983 | Score: -120.497 | Avg score: -214.186 |\n",
"| Experiment: 0 Episode: 984 | Score: -399.896 | Avg score: -216.261 |\n",
"| Experiment: 0 Episode: 985 | Score: -168.979 | Avg score: -216.084 |\n",
"| Experiment: 0 Episode: 986 | Score: -218.831 | Avg score: -216.005 |\n",
"| Experiment: 0 Episode: 987 | Score: -107.337 | Avg score: -215.774 |\n",
"| Experiment: 0 Episode: 988 | Score: -257.434 | Avg score: -214.945 |\n",
"| Experiment: 0 Episode: 989 | Score: -307.292 | Avg score: -217.481 |\n",
"| Experiment: 0 Episode: 990 | Score: -146.799 | Avg score: -213.526 |\n",
"| Experiment: 0 Episode: 991 | Score: -135.975 | Avg score: -214.108 |\n",
"| Experiment: 0 Episode: 992 | Score: -211.591 | Avg score: -214.893 |\n",
"| Experiment: 0 Episode: 993 | Score: -366.403 | Avg score: -217.390 |\n",
"| Experiment: 0 Episode: 994 | Score: -48.701 | Avg score: -216.858 |\n",
"| Experiment: 0 Episode: 995 | Score: -151.449 | Avg score: -215.497 |\n",
"| Experiment: 0 Episode: 996 | Score: 1.566 | Avg score: -213.429 |\n",
"| Experiment: 0 Episode: 997 | Score: -107.896 | Avg score: -210.841 |\n",
"| Experiment: 0 Episode: 998 | Score: -226.459 | Avg score: -211.837 |\n",
"| Experiment: 0 Episode: 999 | Score: -372.236 | Avg score: -212.077 |\n",
"| Experiment: 0 Episode: 1000 | Score: -364.314 | Avg score: -213.884 |\n",
"| Experiment: 0 Episode: 1001 | Score: -89.384 | Avg score: -213.293 |\n",
"| Experiment: 0 Episode: 1002 | Score: -255.131 | Avg score: -215.523 |\n",
"| Experiment: 0 Episode: 1003 | Score: -373.550 | Avg score: -216.793 |\n",
"| Experiment: 0 Episode: 1004 | Score: -133.516 | Avg score: -213.776 |\n",
"| Experiment: 0 Episode: 1005 | Score: -94.358 | Avg score: -214.471 |\n",
"| Experiment: 0 Episode: 1006 | Score: -466.913 | Avg score: -218.046 |\n",
"| Experiment: 0 Episode: 1007 | Score: -352.694 | Avg score: -219.924 |\n",
"| Experiment: 0 Episode: 1008 | Score: -371.630 | Avg score: -221.510 |\n",
"| Experiment: 0 Episode: 1009 | Score: -207.074 | Avg score: -220.894 |\n",
"| Experiment: 0 Episode: 1010 | Score: -168.344 | Avg score: -221.948 |\n",
"| Experiment: 0 Episode: 1011 | Score: -412.396 | Avg score: -222.208 |\n",
"| Experiment: 0 Episode: 1012 | Score: -278.051 | Avg score: -221.961 |\n",
"| Experiment: 0 Episode: 1013 | Score: -382.825 | Avg score: -224.193 |\n",
"| Experiment: 0 Episode: 1014 | Score: -99.029 | Avg score: -221.433 |\n",
"| Experiment: 0 Episode: 1015 | Score: -349.052 | Avg score: -221.322 |\n",
"| Experiment: 0 Episode: 1016 | Score: -314.518 | Avg score: -222.919 |\n",
"| Experiment: 0 Episode: 1017 | Score: -226.818 | Avg score: -223.898 |\n",
"| Experiment: 0 Episode: 1018 | Score: -315.169 | Avg score: -223.593 |\n",
"| Experiment: 0 Episode: 1019 | Score: -26.654 | Avg score: -222.111 |\n",
"| Experiment: 0 Episode: 1020 | Score: -41.058 | Avg score: -218.764 |\n",
"| Experiment: 0 Episode: 1021 | Score: -126.162 | Avg score: -218.589 |\n",
"| Experiment: 0 Episode: 1022 | Score: -190.355 | Avg score: -220.155 |\n",
"| Experiment: 0 Episode: 1023 | Score: -68.233 | Avg score: -219.830 |\n",
"| Experiment: 0 Episode: 1024 | Score: -399.469 | Avg score: -222.338 |\n",
"| Experiment: 0 Episode: 1025 | Score: -88.364 | Avg score: -221.738 |\n",
"| Experiment: 0 Episode: 1026 | Score: -371.240 | Avg score: -222.317 |\n",
"| Experiment: 0 Episode: 1027 | Score: -173.484 | Avg score: -220.284 |\n",
"| Experiment: 0 Episode: 1028 | Score: -134.259 | Avg score: -219.246 |\n",
"| Experiment: 0 Episode: 1029 | Score: -93.635 | Avg score: -219.143 |\n",
"| Experiment: 0 Episode: 1030 | Score: -75.694 | Avg score: -217.092 |\n",
"| Experiment: 0 Episode: 1031 | Score: -298.814 | Avg score: -219.261 |\n",
"| Experiment: 0 Episode: 1032 | Score: -379.918 | Avg score: -222.116 |\n",
"| Experiment: 0 Episode: 1033 | Score: -405.329 | Avg score: -222.629 |\n",
"| Experiment: 0 Episode: 1034 | Score: -196.002 | Avg score: -221.988 |\n",
"| Experiment: 0 Episode: 1035 | Score: -105.130 | Avg score: -222.256 |\n",
"| Experiment: 0 Episode: 1036 | Score: -390.213 | Avg score: -222.248 |\n",
"| Experiment: 0 Episode: 1037 | Score: -400.660 | Avg score: -223.584 |\n",
"| Experiment: 0 Episode: 1038 | Score: -276.364 | Avg score: -225.629 |\n",
"| Experiment: 0 Episode: 1039 | Score: -204.317 | Avg score: -224.705 |\n",
"| Experiment: 0 Episode: 1040 | Score: -163.098 | Avg score: -225.495 |\n",
"| Experiment: 0 Episode: 1041 | Score: -198.088 | Avg score: -226.637 |\n",
"| Experiment: 0 Episode: 1042 | Score: 13.629 | Avg score: -222.563 |\n",
"| Experiment: 0 Episode: 1043 | Score: -251.211 | Avg score: -224.339 |\n",
"| Experiment: 0 Episode: 1044 | Score: -251.947 | Avg score: -223.998 |\n",
"| Experiment: 0 Episode: 1045 | Score: -380.617 | Avg score: -227.145 |\n",
"| Experiment: 0 Episode: 1046 | Score: -240.987 | Avg score: -228.768 |\n",
"| Experiment: 0 Episode: 1047 | Score: -267.348 | Avg score: -227.078 |\n",
"| Experiment: 0 Episode: 1048 | Score: -79.425 | Avg score: -227.219 |\n",
"| Experiment: 0 Episode: 1049 | Score: -134.545 | Avg score: -227.716 |\n",
"| Experiment: 0 Episode: 1050 | Score: -419.262 | Avg score: -228.209 |\n",
"| Experiment: 0 Episode: 1051 | Score: -374.677 | Avg score: -228.976 |\n",
"| Experiment: 0 Episode: 1052 | Score: -65.881 | Avg score: -227.641 |\n",
"| Experiment: 0 Episode: 1053 | Score: -77.748 | Avg score: -226.944 |\n",
"| Experiment: 0 Episode: 1054 | Score: -336.990 | Avg score: -229.401 |\n",
"| Experiment: 0 Episode: 1055 | Score: -284.105 | Avg score: -231.211 |\n",
"| Experiment: 0 Episode: 1056 | Score: -414.521 | Avg score: -235.135 |\n",
"| Experiment: 0 Episode: 1057 | Score: -89.066 | Avg score: -234.488 |\n",
"| Experiment: 0 Episode: 1058 | Score: -90.869 | Avg score: -234.419 |\n",
"| Experiment: 0 Episode: 1059 | Score: -124.869 | Avg score: -232.893 |\n",
"| Experiment: 0 Episode: 1060 | Score: -296.389 | Avg score: -235.408 |\n",
"| Experiment: 0 Episode: 1061 | Score: -331.475 | Avg score: -237.105 |\n",
"| Experiment: 0 Episode: 1062 | Score: -112.593 | Avg score: -237.137 |\n",
"| Experiment: 0 Episode: 1063 | Score: -353.041 | Avg score: -236.874 |\n",
"| Experiment: 0 Episode: 1064 | Score: -329.796 | Avg score: -236.311 |\n",
"| Experiment: 0 Episode: 1065 | Score: -156.322 | Avg score: -235.123 |\n",
"| Experiment: 0 Episode: 1066 | Score: -208.749 | Avg score: -234.212 |\n",
"| Experiment: 0 Episode: 1067 | Score: -82.370 | Avg score: -234.072 |\n",
"| Experiment: 0 Episode: 1068 | Score: -94.525 | Avg score: -229.948 |\n",
"| Experiment: 0 Episode: 1069 | Score: -168.671 | Avg score: -229.324 |\n",
"| Experiment: 0 Episode: 1070 | Score: -195.282 | Avg score: -229.720 |\n",
"| Experiment: 0 Episode: 1071 | Score: -431.159 | Avg score: -230.696 |\n",
"| Experiment: 0 Episode: 1072 | Score: -196.411 | Avg score: -229.452 |\n",
"| Experiment: 0 Episode: 1073 | Score: -118.396 | Avg score: -228.693 |\n",
"| Experiment: 0 Episode: 1074 | Score: -180.558 | Avg score: -229.727 |\n",
"| Experiment: 0 Episode: 1075 | Score: -142.982 | Avg score: -226.194 |\n",
"| Experiment: 0 Episode: 1076 | Score: -93.516 | Avg score: -225.113 |\n",
"| Experiment: 0 Episode: 1077 | Score: -89.429 | Avg score: -224.765 |\n",
"| Experiment: 0 Episode: 1078 | Score: -186.460 | Avg score: -223.197 |\n",
"| Experiment: 0 Episode: 1079 | Score: -78.600 | Avg score: -222.001 |\n",
"| Experiment: 0 Episode: 1080 | Score: -399.688 | Avg score: -221.551 |\n",
"| Experiment: 0 Episode: 1081 | Score: -280.492 | Avg score: -220.500 |\n",
"| Experiment: 0 Episode: 1082 | Score: -194.655 | Avg score: -219.995 |\n",
"| Experiment: 0 Episode: 1083 | Score: -293.054 | Avg score: -221.721 |\n",
"| Experiment: 0 Episode: 1084 | Score: -152.510 | Avg score: -219.247 |\n",
"| Experiment: 0 Episode: 1085 | Score: -92.597 | Avg score: -218.483 |\n",
"| Experiment: 0 Episode: 1086 | Score: -338.639 | Avg score: -219.681 |\n",
"| Experiment: 0 Episode: 1087 | Score: -106.808 | Avg score: -219.676 |\n",
"| Experiment: 0 Episode: 1088 | Score: -96.140 | Avg score: -218.063 |\n",
"| Experiment: 0 Episode: 1089 | Score: -98.196 | Avg score: -215.972 |\n",
"| Experiment: 0 Episode: 1090 | Score: -392.247 | Avg score: -218.426 |\n",
"| Experiment: 0 Episode: 1091 | Score: -126.999 | Avg score: -218.337 |\n",
"| Experiment: 0 Episode: 1092 | Score: -260.401 | Avg score: -218.825 |\n",
"| Experiment: 0 Episode: 1093 | Score: -289.611 | Avg score: -218.057 |\n",
"| Experiment: 0 Episode: 1094 | Score: -47.193 | Avg score: -218.042 |\n",
"| Experiment: 0 Episode: 1095 | Score: -270.494 | Avg score: -219.232 |\n",
"| Experiment: 0 Episode: 1096 | Score: -450.280 | Avg score: -223.751 |\n",
"| Experiment: 0 Episode: 1097 | Score: -226.169 | Avg score: -224.933 |\n",
"| Experiment: 0 Episode: 1098 | Score: -77.186 | Avg score: -223.441 |\n",
"| Experiment: 0 Episode: 1099 | Score: -248.898 | Avg score: -222.207 |\n",
"| Experiment: 0 Episode: 1100 | Score: -33.977 | Avg score: -218.904 |\n",
"| Experiment: 0 Episode: 1101 | Score: 1.091 | Avg score: -217.999 |\n",
"| Experiment: 0 Episode: 1102 | Score: -262.810 | Avg score: -218.076 |\n",
"| Experiment: 0 Episode: 1103 | Score: -139.131 | Avg score: -215.732 |\n",
"| Experiment: 0 Episode: 1104 | Score: -76.018 | Avg score: -215.157 |\n",
"| Experiment: 0 Episode: 1105 | Score: -284.721 | Avg score: -217.060 |\n",
"| Experiment: 0 Episode: 1106 | Score: -110.491 | Avg score: -213.496 |\n",
"| Experiment: 0 Episode: 1107 | Score: 18.586 | Avg score: -209.783 |\n",
"| Experiment: 0 Episode: 1108 | Score: 65.150 | Avg score: -205.416 |\n",
"| Experiment: 0 Episode: 1109 | Score: -246.727 | Avg score: -205.812 |\n",
"| Experiment: 0 Episode: 1110 | Score: -257.403 | Avg score: -206.703 |\n",
"| Experiment: 0 Episode: 1111 | Score: -97.161 | Avg score: -203.550 |\n",
"| Experiment: 0 Episode: 1112 | Score: -220.591 | Avg score: -202.976 |\n",
"| Experiment: 0 Episode: 1113 | Score: -186.110 | Avg score: -201.009 |\n",
"| Experiment: 0 Episode: 1114 | Score: -239.233 | Avg score: -202.411 |\n",
"| Experiment: 0 Episode: 1115 | Score: -56.034 | Avg score: -199.480 |\n",
"| Experiment: 0 Episode: 1116 | Score: -79.613 | Avg score: -197.131 |\n",
"| Experiment: 0 Episode: 1117 | Score: -162.116 | Avg score: -196.484 |\n",
"| Experiment: 0 Episode: 1118 | Score: -130.069 | Avg score: -194.633 |\n",
"| Experiment: 0 Episode: 1119 | Score: -201.182 | Avg score: -196.379 |\n",
"| Experiment: 0 Episode: 1120 | Score: -111.275 | Avg score: -197.081 |\n",
"| Experiment: 0 Episode: 1121 | Score: -84.726 | Avg score: -196.666 |\n",
"| Experiment: 0 Episode: 1122 | Score: -416.446 | Avg score: -198.927 |\n",
"| Experiment: 0 Episode: 1123 | Score: -331.215 | Avg score: -201.557 |\n",
"| Experiment: 0 Episode: 1124 | Score: -378.018 | Avg score: -201.343 |\n",
"| Experiment: 0 Episode: 1125 | Score: -42.067 | Avg score: -200.880 |\n",
"| Experiment: 0 Episode: 1126 | Score: -211.434 | Avg score: -199.282 |\n",
"| Experiment: 0 Episode: 1127 | Score: -71.551 | Avg score: -198.262 |\n",
"| Experiment: 0 Episode: 1128 | Score: -301.278 | Avg score: -199.933 |\n",
"| Experiment: 0 Episode: 1129 | Score: -41.760 | Avg score: -199.414 |\n",
"| Experiment: 0 Episode: 1130 | Score: -283.204 | Avg score: -201.489 |\n",
"| Experiment: 0 Episode: 1131 | Score: -430.499 | Avg score: -202.806 |\n",
"| Experiment: 0 Episode: 1132 | Score: -68.068 | Avg score: -199.687 |\n",
"| Experiment: 0 Episode: 1133 | Score: -451.337 | Avg score: -200.147 |\n",
"| Experiment: 0 Episode: 1134 | Score: -333.416 | Avg score: -201.521 |\n",
"| Experiment: 0 Episode: 1135 | Score: -187.284 | Avg score: -202.343 |\n",
"| Experiment: 0 Episode: 1136 | Score: -401.914 | Avg score: -202.460 |\n",
"| Experiment: 0 Episode: 1137 | Score: -499.390 | Avg score: -203.447 |\n",
"| Experiment: 0 Episode: 1138 | Score: -221.206 | Avg score: -202.896 |\n",
"| Experiment: 0 Episode: 1139 | Score: -272.836 | Avg score: -203.581 |\n",
"| Experiment: 0 Episode: 1140 | Score: -216.305 | Avg score: -204.113 |\n",
"| Experiment: 0 Episode: 1141 | Score: -39.679 | Avg score: -202.529 |\n",
"| Experiment: 0 Episode: 1142 | Score: -141.281 | Avg score: -204.078 |\n",
"| Experiment: 0 Episode: 1143 | Score: -539.395 | Avg score: -206.960 |\n",
"| Experiment: 0 Episode: 1144 | Score: -539.983 | Avg score: -209.840 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 Episode: 1145 | Score: -89.356 | Avg score: -206.928 |\n",
"| Experiment: 0 Episode: 1146 | Score: -45.040 | Avg score: -204.968 |\n",
"| Experiment: 0 Episode: 1147 | Score: -162.948 | Avg score: -203.924 |\n",
"| Experiment: 0 Episode: 1148 | Score: -71.343 | Avg score: -203.843 |\n",
"| Experiment: 0 Episode: 1149 | Score: -68.903 | Avg score: -203.187 |\n",
"| Experiment: 0 Episode: 1150 | Score: -147.810 | Avg score: -200.472 |\n",
"| Experiment: 0 Episode: 1151 | Score: -52.604 | Avg score: -197.252 |\n",
"| Experiment: 0 Episode: 1152 | Score: -115.640 | Avg score: -197.749 |\n",
"| Experiment: 0 Episode: 1153 | Score: -143.470 | Avg score: -198.406 |\n",
"| Experiment: 0 Episode: 1154 | Score: -366.620 | Avg score: -198.703 |\n",
"| Experiment: 0 Episode: 1155 | Score: -238.310 | Avg score: -198.245 |\n",
"| Experiment: 0 Episode: 1156 | Score: -332.547 | Avg score: -197.425 |\n",
"| Experiment: 0 Episode: 1157 | Score: -151.416 | Avg score: -198.049 |\n",
"| Experiment: 0 Episode: 1158 | Score: -374.363 | Avg score: -200.883 |\n",
"| Experiment: 0 Episode: 1159 | Score: -259.572 | Avg score: -202.231 |\n",
"| Experiment: 0 Episode: 1160 | Score: -275.297 | Avg score: -202.020 |\n",
"| Experiment: 0 Episode: 1161 | Score: -102.198 | Avg score: -199.727 |\n",
"| Experiment: 0 Episode: 1162 | Score: -433.768 | Avg score: -202.939 |\n",
"| Experiment: 0 Episode: 1163 | Score: -349.577 | Avg score: -202.904 |\n",
"| Experiment: 0 Episode: 1164 | Score: -19.591 | Avg score: -199.802 |\n",
"| Experiment: 0 Episode: 1165 | Score: -132.516 | Avg score: -199.564 |\n",
"| Experiment: 0 Episode: 1166 | Score: -221.884 | Avg score: -199.695 |\n",
"| Experiment: 0 Episode: 1167 | Score: -272.678 | Avg score: -201.598 |\n",
"| Experiment: 0 Episode: 1168 | Score: -69.570 | Avg score: -201.349 |\n",
"| Experiment: 0 Episode: 1169 | Score: -213.996 | Avg score: -201.802 |\n",
"| Experiment: 0 Episode: 1170 | Score: -484.706 | Avg score: -204.696 |\n",
"| Experiment: 0 Episode: 1171 | Score: -107.025 | Avg score: -201.455 |\n",
"| Experiment: 0 Episode: 1172 | Score: -64.601 | Avg score: -200.137 |\n",
"| Experiment: 0 Episode: 1173 | Score: -217.358 | Avg score: -201.126 |\n",
"| Experiment: 0 Episode: 1174 | Score: -386.682 | Avg score: -203.188 |\n",
"| Experiment: 0 Episode: 1175 | Score: -217.373 | Avg score: -203.931 |\n",
"| Experiment: 0 Episode: 1176 | Score: -200.102 | Avg score: -204.997 |\n",
"| Experiment: 0 Episode: 1177 | Score: 2.511 | Avg score: -204.078 |\n",
"| Experiment: 0 Episode: 1178 | Score: -245.914 | Avg score: -204.672 |\n",
"| Experiment: 0 Episode: 1179 | Score: -141.639 | Avg score: -205.303 |\n",
"| Experiment: 0 Episode: 1180 | Score: -297.545 | Avg score: -204.281 |\n",
"| Experiment: 0 Episode: 1181 | Score: -310.795 | Avg score: -204.584 |\n",
"| Experiment: 0 Episode: 1182 | Score: -361.151 | Avg score: -206.249 |\n",
"| Experiment: 0 Episode: 1183 | Score: -150.743 | Avg score: -204.826 |\n",
"| Experiment: 0 Episode: 1184 | Score: -4.520 | Avg score: -203.346 |\n",
"| Experiment: 0 Episode: 1185 | Score: -79.451 | Avg score: -203.215 |\n",
"| Experiment: 0 Episode: 1186 | Score: -315.993 | Avg score: -202.988 |\n",
"| Experiment: 0 Episode: 1187 | Score: -195.226 | Avg score: -203.873 |\n",
"| Experiment: 0 Episode: 1188 | Score: -180.610 | Avg score: -204.717 |\n",
"| Experiment: 0 Episode: 1189 | Score: -308.738 | Avg score: -206.823 |\n",
"| Experiment: 0 Episode: 1190 | Score: -87.275 | Avg score: -203.773 |\n",
"| Experiment: 0 Episode: 1191 | Score: -329.199 | Avg score: -205.795 |\n",
"| Experiment: 0 Episode: 1192 | Score: -225.694 | Avg score: -205.448 |\n",
"| Experiment: 0 Episode: 1193 | Score: -331.703 | Avg score: -205.869 |\n",
"| Experiment: 0 Episode: 1194 | Score: -371.960 | Avg score: -209.117 |\n",
"| Experiment: 0 Episode: 1195 | Score: -377.927 | Avg score: -210.191 |\n",
"| Experiment: 0 Episode: 1196 | Score: -387.540 | Avg score: -209.564 |\n",
"| Experiment: 0 Episode: 1197 | Score: -423.840 | Avg score: -211.540 |\n",
"| Experiment: 0 Episode: 1198 | Score: -183.367 | Avg score: -212.602 |\n",
"| Experiment: 0 Episode: 1199 | Score: -61.690 | Avg score: -210.730 |\n",
"| Experiment: 0 Episode: 1200 | Score: -121.708 | Avg score: -211.607 |\n",
"| Experiment: 0 Episode: 1201 | Score: -498.866 | Avg score: -216.607 |\n",
"| Experiment: 0 Episode: 1202 | Score: -71.509 | Avg score: -214.694 |\n",
"| Experiment: 0 Episode: 1203 | Score: -241.513 | Avg score: -215.718 |\n",
"| Experiment: 0 Episode: 1204 | Score: -125.453 | Avg score: -216.212 |\n",
"| Experiment: 0 Episode: 1205 | Score: -134.835 | Avg score: -214.713 |\n",
"| Experiment: 0 Episode: 1206 | Score: -89.853 | Avg score: -214.507 |\n",
"| Experiment: 0 Episode: 1207 | Score: -435.356 | Avg score: -219.046 |\n",
"| Experiment: 0 Episode: 1208 | Score: -107.594 | Avg score: -220.774 |\n",
"| Experiment: 0 Episode: 1209 | Score: -83.762 | Avg score: -219.144 |\n",
"| Experiment: 0 Episode: 1210 | Score: -161.997 | Avg score: -218.190 |\n",
"| Experiment: 0 Episode: 1211 | Score: -356.487 | Avg score: -220.783 |\n",
"| Experiment: 0 Episode: 1212 | Score: -166.166 | Avg score: -220.239 |\n",
"| Experiment: 0 Episode: 1213 | Score: -111.450 | Avg score: -219.492 |\n",
"| Experiment: 0 Episode: 1214 | Score: -234.767 | Avg score: -219.448 |\n",
"| Experiment: 0 Episode: 1215 | Score: -119.714 | Avg score: -220.084 |\n",
"| Experiment: 0 Episode: 1216 | Score: -157.123 | Avg score: -220.860 |\n",
"| Experiment: 0 Episode: 1217 | Score: 22.692 | Avg score: -219.011 |\n",
"| Experiment: 0 Episode: 1218 | Score: -141.368 | Avg score: -219.124 |\n",
"| Experiment: 0 Episode: 1219 | Score: -305.793 | Avg score: -220.171 |\n",
"| Experiment: 0 Episode: 1220 | Score: 5.893 | Avg score: -218.999 |\n",
"| Experiment: 0 Episode: 1221 | Score: -165.499 | Avg score: -219.807 |\n",
"| Experiment: 0 Episode: 1222 | Score: -68.200 | Avg score: -216.324 |\n",
"| Experiment: 0 Episode: 1223 | Score: -189.311 | Avg score: -214.905 |\n",
"| Experiment: 0 Episode: 1224 | Score: -55.890 | Avg score: -211.684 |\n",
"| Experiment: 0 Episode: 1225 | Score: -26.251 | Avg score: -211.526 |\n",
"| Experiment: 0 Episode: 1226 | Score: -280.915 | Avg score: -212.220 |\n",
"| Experiment: 0 Episode: 1227 | Score: -73.382 | Avg score: -212.239 |\n",
"| Experiment: 0 Episode: 1228 | Score: -168.431 | Avg score: -210.910 |\n",
"| Experiment: 0 Episode: 1229 | Score: -107.627 | Avg score: -211.569 |\n",
"| Experiment: 0 Episode: 1230 | Score: 32.971 | Avg score: -208.407 |\n",
"| Experiment: 0 Episode: 1231 | Score: -344.519 | Avg score: -207.547 |\n",
"| Experiment: 0 Episode: 1232 | Score: -99.411 | Avg score: -207.861 |\n",
"| Experiment: 0 Episode: 1233 | Score: -378.247 | Avg score: -207.130 |\n",
"| Experiment: 0 Episode: 1234 | Score: -489.313 | Avg score: -208.689 |\n",
"| Experiment: 0 Episode: 1235 | Score: -407.672 | Avg score: -210.893 |\n",
"| Experiment: 0 Episode: 1236 | Score: -173.027 | Avg score: -208.604 |\n",
"| Experiment: 0 Episode: 1237 | Score: -104.947 | Avg score: -204.660 |\n",
"| Experiment: 0 Episode: 1238 | Score: -188.714 | Avg score: -204.335 |\n",
"| Experiment: 0 Episode: 1239 | Score: -174.607 | Avg score: -203.352 |\n",
"| Experiment: 0 Episode: 1240 | Score: -110.241 | Avg score: -202.292 |\n",
"| Experiment: 0 Episode: 1241 | Score: -225.601 | Avg score: -204.151 |\n",
"| Experiment: 0 Episode: 1242 | Score: -317.252 | Avg score: -205.911 |\n",
"| Experiment: 0 Episode: 1243 | Score: -315.515 | Avg score: -203.672 |\n",
"| Experiment: 0 Episode: 1244 | Score: -340.804 | Avg score: -201.680 |\n",
"| Experiment: 0 Episode: 1245 | Score: -343.959 | Avg score: -204.226 |\n",
"| Experiment: 0 Episode: 1246 | Score: -225.579 | Avg score: -206.031 |\n",
"| Experiment: 0 Episode: 1247 | Score: -173.680 | Avg score: -206.139 |\n",
"| Experiment: 0 Episode: 1248 | Score: -254.278 | Avg score: -207.968 |\n",
"| Experiment: 0 Episode: 1249 | Score: -290.926 | Avg score: -210.188 |\n",
"| Experiment: 0 Episode: 1250 | Score: -40.978 | Avg score: -209.120 |\n",
"| Experiment: 0 Episode: 1251 | Score: -108.146 | Avg score: -209.675 |\n",
"| Experiment: 0 Episode: 1252 | Score: -193.572 | Avg score: -210.455 |\n",
"| Experiment: 0 Episode: 1253 | Score: -88.855 | Avg score: -209.909 |\n",
"| Experiment: 0 Episode: 1254 | Score: 0.949 | Avg score: -206.233 |\n",
"| Experiment: 0 Episode: 1255 | Score: -330.452 | Avg score: -207.154 |\n",
"| Experiment: 0 Episode: 1256 | Score: -400.244 | Avg score: -207.831 |\n",
"| Experiment: 0 Episode: 1257 | Score: -104.427 | Avg score: -207.361 |\n",
"| Experiment: 0 Episode: 1258 | Score: -171.465 | Avg score: -205.332 |\n",
"| Experiment: 0 Episode: 1259 | Score: -469.731 | Avg score: -207.434 |\n",
"| Experiment: 0 Episode: 1260 | Score: -106.155 | Avg score: -205.743 |\n",
"| Experiment: 0 Episode: 1261 | Score: -270.235 | Avg score: -207.423 |\n",
"| Experiment: 0 Episode: 1262 | Score: -179.620 | Avg score: -204.882 |\n",
"| Experiment: 0 Episode: 1263 | Score: -392.951 | Avg score: -205.315 |\n",
"| Experiment: 0 Episode: 1264 | Score: -133.079 | Avg score: -206.450 |\n",
"| Experiment: 0 Episode: 1265 | Score: -461.783 | Avg score: -209.743 |\n",
"| Experiment: 0 Episode: 1266 | Score: -401.166 | Avg score: -211.536 |\n",
"| Experiment: 0 Episode: 1267 | Score: -104.767 | Avg score: -209.857 |\n",
"| Experiment: 0 Episode: 1268 | Score: -262.265 | Avg score: -211.783 |\n",
"| Experiment: 0 Episode: 1269 | Score: -171.341 | Avg score: -211.357 |\n",
"| Experiment: 0 Episode: 1270 | Score: -207.253 | Avg score: -208.582 |\n",
"| Experiment: 0 Episode: 1271 | Score: -35.483 | Avg score: -207.867 |\n",
"| Experiment: 0 Episode: 1272 | Score: -365.450 | Avg score: -210.875 |\n",
"| Experiment: 0 Episode: 1273 | Score: -65.022 | Avg score: -209.352 |\n",
"| Experiment: 0 Episode: 1274 | Score: -289.912 | Avg score: -208.384 |\n",
"| Experiment: 0 Episode: 1275 | Score: -290.010 | Avg score: -209.111 |\n",
"| Experiment: 0 Episode: 1276 | Score: -189.679 | Avg score: -209.007 |\n",
"| Experiment: 0 Episode: 1277 | Score: -108.154 | Avg score: -210.113 |\n",
"| Experiment: 0 Episode: 1278 | Score: -260.418 | Avg score: -210.258 |\n",
"| Experiment: 0 Episode: 1279 | Score: -313.813 | Avg score: -211.980 |\n",
"| Experiment: 0 Episode: 1280 | Score: -332.589 | Avg score: -212.330 |\n",
"| Experiment: 0 Episode: 1281 | Score: -187.749 | Avg score: -211.100 |\n",
"| Experiment: 0 Episode: 1282 | Score: -270.760 | Avg score: -210.196 |\n",
"| Experiment: 0 Episode: 1283 | Score: -71.439 | Avg score: -209.403 |\n",
"| Experiment: 0 Episode: 1284 | Score: -298.258 | Avg score: -212.340 |\n",
"| Experiment: 0 Episode: 1285 | Score: -94.638 | Avg score: -212.492 |\n",
"| Experiment: 0 Episode: 1286 | Score: -123.026 | Avg score: -210.563 |\n",
"| Experiment: 0 Episode: 1287 | Score: -37.092 | Avg score: -208.981 |\n",
"| Experiment: 0 Episode: 1288 | Score: -335.004 | Avg score: -210.525 |\n",
"| Experiment: 0 Episode: 1289 | Score: -120.406 | Avg score: -208.642 |\n",
"| Experiment: 0 Episode: 1290 | Score: -319.474 | Avg score: -210.964 |\n",
"| Experiment: 0 Episode: 1291 | Score: -97.246 | Avg score: -208.644 |\n",
"| Experiment: 0 Episode: 1292 | Score: -157.973 | Avg score: -207.967 |\n",
"| Experiment: 0 Episode: 1293 | Score: -375.336 | Avg score: -208.403 |\n",
"| Experiment: 0 Episode: 1294 | Score: -59.624 | Avg score: -205.280 |\n",
"| Experiment: 0 Episode: 1295 | Score: -162.543 | Avg score: -203.126 |\n",
"| Experiment: 0 Episode: 1296 | Score: -388.328 | Avg score: -203.134 |\n",
"| Experiment: 0 Episode: 1297 | Score: -46.037 | Avg score: -199.356 |\n",
"| Experiment: 0 Episode: 1298 | Score: -159.964 | Avg score: -199.122 |\n",
"| Experiment: 0 Episode: 1299 | Score: -316.698 | Avg score: -201.672 |\n",
"| Experiment: 0 Episode: 1300 | Score: -71.695 | Avg score: -201.172 |\n",
"| Experiment: 0 Episode: 1301 | Score: -296.538 | Avg score: -199.149 |\n",
"| Experiment: 0 Episode: 1302 | Score: -158.936 | Avg score: -200.023 |\n",
"| Experiment: 0 Episode: 1303 | Score: -229.409 | Avg score: -199.902 |\n",
"| Experiment: 0 Episode: 1304 | Score: -134.598 | Avg score: -199.993 |\n",
"| Experiment: 0 Episode: 1305 | Score: -456.652 | Avg score: -203.212 |\n",
"| Experiment: 0 Episode: 1306 | Score: -325.792 | Avg score: -205.571 |\n",
"| Experiment: 0 Episode: 1307 | Score: -151.555 | Avg score: -202.733 |\n",
"| Experiment: 0 Episode: 1308 | Score: -251.773 | Avg score: -204.175 |\n",
"| Experiment: 0 Episode: 1309 | Score: -349.996 | Avg score: -206.837 |\n",
"| Experiment: 0 Episode: 1310 | Score: -331.925 | Avg score: -208.536 |\n",
"| Experiment: 0 Episode: 1311 | Score: -445.347 | Avg score: -209.425 |\n",
"| Experiment: 0 Episode: 1312 | Score: -265.783 | Avg score: -210.421 |\n",
"| Experiment: 0 Episode: 1313 | Score: -227.256 | Avg score: -211.579 |\n",
"| Experiment: 0 Episode: 1314 | Score: -121.510 | Avg score: -210.447 |\n",
"| Experiment: 0 Episode: 1315 | Score: -51.705 | Avg score: -209.767 |\n",
"| Experiment: 0 Episode: 1316 | Score: -373.751 | Avg score: -211.933 |\n",
"| Experiment: 0 Episode: 1317 | Score: -240.796 | Avg score: -214.568 |\n",
"| Experiment: 0 Episode: 1318 | Score: -33.767 | Avg score: -213.492 |\n",
"| Experiment: 0 Episode: 1319 | Score: -163.245 | Avg score: -212.066 |\n",
"| Experiment: 0 Episode: 1320 | Score: -92.356 | Avg score: -213.049 |\n",
"| Experiment: 0 Episode: 1321 | Score: -63.129 | Avg score: -212.025 |\n",
"| Experiment: 0 Episode: 1322 | Score: -402.523 | Avg score: -215.368 |\n",
"| Experiment: 0 Episode: 1323 | Score: -73.687 | Avg score: -214.212 |\n",
"| Experiment: 0 Episode: 1324 | Score: -82.672 | Avg score: -214.480 |\n",
"| Experiment: 0 Episode: 1325 | Score: -366.723 | Avg score: -217.885 |\n",
"| Experiment: 0 Episode: 1326 | Score: -430.935 | Avg score: -219.385 |\n",
"| Experiment: 0 Episode: 1327 | Score: -75.687 | Avg score: -219.408 |\n",
"| Experiment: 0 Episode: 1328 | Score: -92.781 | Avg score: -218.651 |\n",
"| Experiment: 0 Episode: 1329 | Score: -66.023 | Avg score: -218.235 |\n",
"| Experiment: 0 Episode: 1330 | Score: -51.052 | Avg score: -219.075 |\n",
"| Experiment: 0 Episode: 1331 | Score: -401.183 | Avg score: -219.642 |\n",
"| Experiment: 0 Episode: 1332 | Score: -139.589 | Avg score: -220.044 |\n",
"| Experiment: 0 Episode: 1333 | Score: -57.050 | Avg score: -216.832 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 Episode: 1334 | Score: -116.323 | Avg score: -213.102 |\n",
"| Experiment: 0 Episode: 1335 | Score: -116.963 | Avg score: -210.195 |\n",
"| Experiment: 0 Episode: 1336 | Score: -190.504 | Avg score: -210.370 |\n",
"| Experiment: 0 Episode: 1337 | Score: -412.324 | Avg score: -213.443 |\n",
"| Experiment: 0 Episode: 1338 | Score: -104.863 | Avg score: -212.605 |\n",
"| Experiment: 0 Episode: 1339 | Score: -307.347 | Avg score: -213.932 |\n",
"| Experiment: 0 Episode: 1340 | Score: -238.281 | Avg score: -215.213 |\n",
"| Experiment: 0 Episode: 1341 | Score: -130.406 | Avg score: -214.261 |\n",
"| Experiment: 0 Episode: 1342 | Score: -418.627 | Avg score: -215.275 |\n",
"| Experiment: 0 Episode: 1343 | Score: -95.286 | Avg score: -213.072 |\n",
"| Experiment: 0 Episode: 1344 | Score: -317.486 | Avg score: -212.839 |\n",
"| Experiment: 0 Episode: 1345 | Score: -50.064 | Avg score: -209.900 |\n",
"| Experiment: 0 Episode: 1346 | Score: -338.070 | Avg score: -211.025 |\n",
"| Experiment: 0 Episode: 1347 | Score: -326.164 | Avg score: -212.550 |\n",
"| Experiment: 0 Episode: 1348 | Score: -86.821 | Avg score: -210.875 |\n",
"| Experiment: 0 Episode: 1349 | Score: -62.063 | Avg score: -208.587 |\n",
"| Experiment: 0 Episode: 1350 | Score: -71.493 | Avg score: -208.892 |\n",
"| Experiment: 0 Episode: 1351 | Score: -56.819 | Avg score: -208.379 |\n",
"| Experiment: 0 Episode: 1352 | Score: -309.801 | Avg score: -209.541 |\n",
"| Experiment: 0 Episode: 1353 | Score: -94.621 | Avg score: -209.599 |\n",
"| Experiment: 0 Episode: 1354 | Score: -118.203 | Avg score: -210.790 |\n",
"| Experiment: 0 Episode: 1355 | Score: -309.195 | Avg score: -210.577 |\n",
"| Experiment: 0 Episode: 1356 | Score: -137.638 | Avg score: -207.951 |\n",
"| Experiment: 0 Episode: 1357 | Score: -306.437 | Avg score: -209.972 |\n",
"| Experiment: 0 Episode: 1358 | Score: -64.348 | Avg score: -208.900 |\n",
"| Experiment: 0 Episode: 1359 | Score: -335.253 | Avg score: -207.556 |\n",
"| Experiment: 0 Episode: 1360 | Score: -255.279 | Avg score: -209.047 |\n",
"| Experiment: 0 Episode: 1361 | Score: -35.575 | Avg score: -206.700 |\n",
"| Experiment: 0 Episode: 1362 | Score: -277.390 | Avg score: -207.678 |\n",
"| Experiment: 0 Episode: 1363 | Score: -256.508 | Avg score: -206.313 |\n",
"| Experiment: 0 Episode: 1364 | Score: 4.065 | Avg score: -204.942 |\n",
"| Experiment: 0 Episode: 1365 | Score: -275.342 | Avg score: -203.078 |\n",
"| Experiment: 0 Episode: 1366 | Score: -392.784 | Avg score: -202.994 |\n",
"| Experiment: 0 Episode: 1367 | Score: -89.051 | Avg score: -202.837 |\n",
"| Experiment: 0 Episode: 1368 | Score: -128.458 | Avg score: -201.499 |\n",
"| Experiment: 0 Episode: 1369 | Score: -53.503 | Avg score: -200.320 |\n",
"| Experiment: 0 Episode: 1370 | Score: -365.355 | Avg score: -201.901 |\n",
"| Experiment: 0 Episode: 1371 | Score: -148.237 | Avg score: -203.029 |\n",
"| Experiment: 0 Episode: 1372 | Score: -231.351 | Avg score: -201.688 |\n",
"| Experiment: 0 Episode: 1373 | Score: -94.322 | Avg score: -201.981 |\n",
"| Experiment: 0 Episode: 1374 | Score: -493.677 | Avg score: -204.018 |\n",
"| Experiment: 0 Episode: 1375 | Score: -246.545 | Avg score: -203.584 |\n",
"| Experiment: 0 Episode: 1376 | Score: -96.994 | Avg score: -202.657 |\n",
"| Experiment: 0 Episode: 1377 | Score: -346.472 | Avg score: -205.040 |\n",
"| Experiment: 0 Episode: 1378 | Score: -117.015 | Avg score: -203.606 |\n",
"| Experiment: 0 Episode: 1379 | Score: -133.933 | Avg score: -201.807 |\n",
"| Experiment: 0 Episode: 1380 | Score: -347.768 | Avg score: -201.959 |\n",
"| Experiment: 0 Episode: 1381 | Score: -297.945 | Avg score: -203.061 |\n",
"| Experiment: 0 Episode: 1382 | Score: -396.960 | Avg score: -204.323 |\n",
"| Experiment: 0 Episode: 1383 | Score: -287.135 | Avg score: -206.480 |\n",
"| Experiment: 0 Episode: 1384 | Score: -330.786 | Avg score: -206.805 |\n",
"| Experiment: 0 Episode: 1385 | Score: -61.480 | Avg score: -206.474 |\n",
"| Experiment: 0 Episode: 1386 | Score: -219.022 | Avg score: -207.434 |\n",
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"| Experiment: 0 Episode: 1394 | Score: -59.666 | Avg score: -210.066 |\n",
"| Experiment: 0 Episode: 1395 | Score: -417.428 | Avg score: -212.615 |\n",
"| Experiment: 0 Episode: 1396 | Score: -251.093 | Avg score: -211.243 |\n",
"| Experiment: 0 Episode: 1397 | Score: -294.264 | Avg score: -213.725 |\n",
"| Experiment: 0 Episode: 1398 | Score: -121.878 | Avg score: -213.344 |\n",
"| Experiment: 0 Episode: 1399 | Score: 165.389 | Avg score: -208.523 |\n",
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"| Experiment: 0 Episode: 1404 | Score: -20.603 | Avg score: -205.466 |\n",
"| Experiment: 0 Episode: 1405 | Score: -158.987 | Avg score: -202.489 |\n",
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"| Experiment: 0 Episode: 1416 | Score: -257.928 | Avg score: -203.356 |\n",
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"| Experiment: 0 Episode: 1418 | Score: -353.994 | Avg score: -206.386 |\n",
"| Experiment: 0 Episode: 1419 | Score: -25.754 | Avg score: -205.011 |\n",
"| Experiment: 0 Episode: 1420 | Score: -299.071 | Avg score: -207.078 |\n",
"| Experiment: 0 Episode: 1421 | Score: -440.358 | Avg score: -210.850 |\n",
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"| Experiment: 0 Episode: 1428 | Score: -115.960 | Avg score: -209.549 |\n",
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"| Experiment: 0 Episode: 1455 | Score: -137.995 | Avg score: -213.563 |\n",
"| Experiment: 0 Episode: 1456 | Score: -72.136 | Avg score: -212.908 |\n",
"| Experiment: 0 Episode: 1457 | Score: -179.954 | Avg score: -211.643 |\n",
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"| Experiment: 0 Episode: 1459 | Score: -432.106 | Avg score: -214.449 |\n",
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"| Experiment: 0 Episode: 1476 | Score: -78.198 | Avg score: -205.381 |\n",
"| Experiment: 0 Episode: 1477 | Score: -107.717 | Avg score: -202.994 |\n",
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"| Experiment: 0 Episode: 1480 | Score: -249.044 | Avg score: -206.676 |\n",
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"| Experiment: 0 Episode: 1483 | Score: -113.882 | Avg score: -203.397 |\n",
"| Experiment: 0 Episode: 1484 | Score: -360.119 | Avg score: -203.690 |\n",
"| Experiment: 0 Episode: 1485 | Score: -261.868 | Avg score: -205.694 |\n",
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"| Experiment: 0 Episode: 1489 | Score: -38.003 | Avg score: -202.774 |\n",
"| Experiment: 0 Episode: 1490 | Score: -186.133 | Avg score: -201.571 |\n",
"| Experiment: 0 Episode: 1491 | Score: -71.705 | Avg score: -200.724 |\n",
"| Experiment: 0 Episode: 1492 | Score: -264.439 | Avg score: -200.768 |\n",
"| Experiment: 0 Episode: 1493 | Score: -508.753 | Avg score: -202.047 |\n",
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"| Experiment: 0 Episode: 1495 | Score: -78.721 | Avg score: -201.990 |\n",
"| Experiment: 0 Episode: 1496 | Score: -372.247 | Avg score: -203.201 |\n",
"| Experiment: 0 Episode: 1497 | Score: -275.210 | Avg score: -203.011 |\n",
"| Experiment: 0 Episode: 1498 | Score: -261.663 | Avg score: -204.408 |\n",
"| Experiment: 0 Episode: 1499 | Score: -422.712 | Avg score: -210.289 |\n",
"| Experiment: 0 Episode: 1500 | Score: -96.169 | Avg score: -210.700 |\n",
"| Experiment: 0 Episode: 1501 | Score: -87.950 | Avg score: -211.007 |\n",
"| Experiment: 0 Episode: 1502 | Score: -563.987 | Avg score: -215.718 |\n",
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"| Experiment: 0 Episode: 1504 | Score: -296.600 | Avg score: -217.163 |\n",
"| Experiment: 0 Episode: 1505 | Score: -258.635 | Avg score: -218.160 |\n",
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"| Experiment: 0 Episode: 1508 | Score: -50.767 | Avg score: -214.439 |\n",
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"| Experiment: 0 Episode: 1510 | Score: -364.721 | Avg score: -213.255 |\n",
"| Experiment: 0 Episode: 1511 | Score: -324.866 | Avg score: -213.019 |\n",
"| Experiment: 0 Episode: 1512 | Score: -202.823 | Avg score: -210.714 |\n",
"| Experiment: 0 Episode: 1513 | Score: -117.819 | Avg score: -210.842 |\n",
"| Experiment: 0 Episode: 1514 | Score: -58.166 | Avg score: -207.961 |\n",
"| Experiment: 0 Episode: 1515 | Score: -127.438 | Avg score: -207.157 |\n",
"| Experiment: 0 Episode: 1516 | Score: -115.744 | Avg score: -205.736 |\n",
"| Experiment: 0 Episode: 1517 | Score: -123.015 | Avg score: -204.730 |\n",
"| Experiment: 0 Episode: 1518 | Score: -57.452 | Avg score: -201.764 |\n",
"| Experiment: 0 Episode: 1519 | Score: -516.957 | Avg score: -206.676 |\n",
"| Experiment: 0 Episode: 1520 | Score: -113.748 | Avg score: -204.823 |\n",
"| Experiment: 0 Episode: 1521 | Score: -246.788 | Avg score: -202.888 |\n",
"| Experiment: 0 Episode: 1522 | Score: -386.850 | Avg score: -205.699 |\n",
"| Experiment: 0 Episode: 1523 | Score: -316.427 | Avg score: -205.255 |\n",
"| Experiment: 0 Episode: 1524 | Score: -291.162 | Avg score: -204.954 |\n",
"| Experiment: 0 Episode: 1525 | Score: -225.358 | Avg score: -207.028 |\n",
"| Experiment: 0 Episode: 1526 | Score: -358.079 | Avg score: -209.580 |\n",
"| Experiment: 0 Episode: 1527 | Score: -226.661 | Avg score: -208.144 |\n",
"| Experiment: 0 Episode: 1528 | Score: -406.026 | Avg score: -211.044 |\n",
"| Experiment: 0 Episode: 1529 | Score: -183.134 | Avg score: -211.828 |\n",
"| Experiment: 0 Episode: 1530 | Score: -485.341 | Avg score: -213.363 |\n",
"| Experiment: 0 Episode: 1531 | Score: -166.180 | Avg score: -212.884 |\n",
"| Experiment: 0 Episode: 1532 | Score: -253.795 | Avg score: -214.984 |\n",
"| Experiment: 0 Episode: 1533 | Score: -68.046 | Avg score: -213.690 |\n",
"| Experiment: 0 Episode: 1534 | Score: -328.913 | Avg score: -216.019 |\n",
"| Experiment: 0 Episode: 1535 | Score: -222.976 | Avg score: -215.465 |\n",
"| Experiment: 0 Episode: 1536 | Score: -451.468 | Avg score: -217.621 |\n",
"| Experiment: 0 Episode: 1537 | Score: -244.213 | Avg score: -217.724 |\n",
"| Experiment: 0 Episode: 1538 | Score: -304.874 | Avg score: -217.448 |\n",
"| Experiment: 0 Episode: 1539 | Score: -289.100 | Avg score: -220.233 |\n",
"| Experiment: 0 Episode: 1540 | Score: -122.789 | Avg score: -220.735 |\n",
"| Experiment: 0 Episode: 1541 | Score: -425.508 | Avg score: -221.925 |\n",
"| Experiment: 0 Episode: 1542 | Score: -117.293 | Avg score: -221.701 |\n",
"| Experiment: 0 Episode: 1543 | Score: -106.451 | Avg score: -219.113 |\n",
"| Experiment: 0 Episode: 1544 | Score: -102.472 | Avg score: -218.284 |\n",
"| Experiment: 0 Episode: 1545 | Score: -160.582 | Avg score: -217.001 |\n",
"| Experiment: 0 Episode: 1546 | Score: -215.971 | Avg score: -215.813 |\n",
"| Experiment: 0 Episode: 1547 | Score: -239.675 | Avg score: -213.878 |\n",
"| Experiment: 0 Episode: 1548 | Score: -18.160 | Avg score: -212.840 |\n",
"| Experiment: 0 Episode: 1549 | Score: -267.181 | Avg score: -214.467 |\n",
"| Experiment: 0 Episode: 1550 | Score: -240.909 | Avg score: -216.266 |\n",
"| Experiment: 0 Episode: 1551 | Score: -58.304 | Avg score: -214.412 |\n",
"| Experiment: 0 Episode: 1552 | Score: -268.710 | Avg score: -213.540 |\n",
"| Experiment: 0 Episode: 1553 | Score: -419.750 | Avg score: -217.248 |\n",
"| Experiment: 0 Episode: 1554 | Score: -442.453 | Avg score: -220.586 |\n",
"| Experiment: 0 Episode: 1555 | Score: -151.541 | Avg score: -220.722 |\n",
"| Experiment: 0 Episode: 1556 | Score: -323.792 | Avg score: -223.238 |\n",
"| Experiment: 0 Episode: 1557 | Score: -96.140 | Avg score: -222.400 |\n",
"| Experiment: 0 Episode: 1558 | Score: -385.103 | Avg score: -223.771 |\n",
"| Experiment: 0 Episode: 1559 | Score: -381.844 | Avg score: -223.268 |\n",
"| Experiment: 0 Episode: 1560 | Score: -343.992 | Avg score: -225.233 |\n",
"| Experiment: 0 Episode: 1561 | Score: -230.020 | Avg score: -226.477 |\n",
"| Experiment: 0 Episode: 1562 | Score: -23.236 | Avg score: -225.457 |\n",
"| Experiment: 0 Episode: 1563 | Score: -420.764 | Avg score: -228.502 |\n",
"| Experiment: 0 Episode: 1564 | Score: -252.183 | Avg score: -229.507 |\n",
"| Experiment: 0 Episode: 1565 | Score: -327.924 | Avg score: -231.996 |\n",
"| Experiment: 0 Episode: 1566 | Score: -325.038 | Avg score: -231.532 |\n",
"| Experiment: 0 Episode: 1567 | Score: -241.489 | Avg score: -231.393 |\n",
"| Experiment: 0 Episode: 1568 | Score: -109.286 | Avg score: -230.931 |\n",
"| Experiment: 0 Episode: 1569 | Score: -493.334 | Avg score: -234.125 |\n",
"| Experiment: 0 Episode: 1570 | Score: -119.081 | Avg score: -234.734 |\n",
"| Experiment: 0 Episode: 1571 | Score: -260.741 | Avg score: -233.371 |\n",
"| Experiment: 0 Episode: 1572 | Score: -347.836 | Avg score: -236.487 |\n",
"| Experiment: 0 Episode: 1573 | Score: 31.131 | Avg score: -235.812 |\n",
"| Experiment: 0 Episode: 1574 | Score: -70.186 | Avg score: -235.373 |\n",
"| Experiment: 0 Episode: 1575 | Score: -425.629 | Avg score: -238.351 |\n",
"| Experiment: 0 Episode: 1576 | Score: -328.428 | Avg score: -240.853 |\n",
"| Experiment: 0 Episode: 1577 | Score: -438.874 | Avg score: -244.165 |\n",
"| Experiment: 0 Episode: 1578 | Score: -109.972 | Avg score: -243.081 |\n",
"| Experiment: 0 Episode: 1579 | Score: -123.369 | Avg score: -239.319 |\n",
"| Experiment: 0 Episode: 1580 | Score: -543.985 | Avg score: -242.268 |\n",
"| Experiment: 0 Episode: 1581 | Score: -216.067 | Avg score: -240.276 |\n",
"| Experiment: 0 Episode: 1582 | Score: -396.773 | Avg score: -242.994 |\n",
"| Experiment: 0 Episode: 1583 | Score: -89.834 | Avg score: -242.754 |\n",
"| Experiment: 0 Episode: 1584 | Score: -154.155 | Avg score: -240.694 |\n",
"| Experiment: 0 Episode: 1585 | Score: -101.483 | Avg score: -239.090 |\n",
"| Experiment: 0 Episode: 1586 | Score: -423.091 | Avg score: -242.323 |\n",
"| Experiment: 0 Episode: 1587 | Score: -395.472 | Avg score: -243.865 |\n",
"| Experiment: 0 Episode: 1588 | Score: -108.621 | Avg score: -243.452 |\n",
"| Experiment: 0 Episode: 1589 | Score: -88.719 | Avg score: -243.959 |\n",
"| Experiment: 0 Episode: 1590 | Score: -389.283 | Avg score: -245.991 |\n",
"| Experiment: 0 Episode: 1591 | Score: -72.027 | Avg score: -245.994 |\n",
"| Experiment: 0 Episode: 1592 | Score: -163.762 | Avg score: -244.987 |\n",
"| Experiment: 0 Episode: 1593 | Score: -78.005 | Avg score: -240.680 |\n",
"| Experiment: 0 Episode: 1594 | Score: -181.266 | Avg score: -238.566 |\n",
"| Experiment: 0 Episode: 1595 | Score: -116.898 | Avg score: -238.948 |\n",
"| Experiment: 0 Episode: 1596 | Score: -406.881 | Avg score: -239.294 |\n",
"| Experiment: 0 Episode: 1597 | Score: -80.128 | Avg score: -237.344 |\n",
"| Experiment: 0 Episode: 1598 | Score: -113.438 | Avg score: -235.861 |\n",
"| Experiment: 0 Episode: 1599 | Score: -401.623 | Avg score: -235.650 |\n",
"| Experiment: 0 Episode: 1600 | Score: -87.375 | Avg score: -235.563 |\n",
"| Experiment: 0 Episode: 1601 | Score: -314.325 | Avg score: -237.826 |\n",
"| Experiment: 0 Episode: 1602 | Score: -442.381 | Avg score: -236.610 |\n",
"| Experiment: 0 Episode: 1603 | Score: -206.113 | Avg score: -236.390 |\n",
"| Experiment: 0 Episode: 1604 | Score: -335.324 | Avg score: -236.777 |\n",
"| Experiment: 0 Episode: 1605 | Score: -207.285 | Avg score: -236.264 |\n",
"| Experiment: 0 Episode: 1606 | Score: -131.878 | Avg score: -236.702 |\n",
"| Experiment: 0 Episode: 1607 | Score: -278.607 | Avg score: -236.960 |\n",
"| Experiment: 0 Episode: 1608 | Score: -58.518 | Avg score: -237.038 |\n",
"| Experiment: 0 Episode: 1609 | Score: -171.105 | Avg score: -238.374 |\n",
"| Experiment: 0 Episode: 1610 | Score: -399.027 | Avg score: -238.718 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 Episode: 1611 | Score: -69.105 | Avg score: -236.160 |\n",
"| Experiment: 0 Episode: 1612 | Score: -241.775 | Avg score: -236.549 |\n",
"| Experiment: 0 Episode: 1613 | Score: -404.982 | Avg score: -239.421 |\n",
"| Experiment: 0 Episode: 1614 | Score: -292.151 | Avg score: -241.761 |\n",
"| Experiment: 0 Episode: 1615 | Score: -395.987 | Avg score: -244.446 |\n",
"| Experiment: 0 Episode: 1616 | Score: -157.987 | Avg score: -244.869 |\n",
"| Experiment: 0 Episode: 1617 | Score: -201.290 | Avg score: -245.652 |\n",
"| Experiment: 0 Episode: 1618 | Score: -271.350 | Avg score: -247.791 |\n",
"| Experiment: 0 Episode: 1619 | Score: -89.747 | Avg score: -243.518 |\n",
"| Experiment: 0 Episode: 1620 | Score: -39.059 | Avg score: -242.772 |\n",
"| Experiment: 0 Episode: 1621 | Score: -503.655 | Avg score: -245.340 |\n",
"| Experiment: 0 Episode: 1622 | Score: -82.259 | Avg score: -242.294 |\n",
"| Experiment: 0 Episode: 1623 | Score: -141.727 | Avg score: -240.547 |\n",
"| Experiment: 0 Episode: 1624 | Score: -357.108 | Avg score: -241.207 |\n",
"| Experiment: 0 Episode: 1625 | Score: -85.622 | Avg score: -239.809 |\n",
"| Experiment: 0 Episode: 1626 | Score: -75.002 | Avg score: -236.979 |\n",
"| Experiment: 0 Episode: 1627 | Score: -414.243 | Avg score: -238.854 |\n",
"| Experiment: 0 Episode: 1628 | Score: -120.064 | Avg score: -235.995 |\n",
"| Experiment: 0 Episode: 1629 | Score: -247.063 | Avg score: -236.634 |\n",
"| Experiment: 0 Episode: 1630 | Score: -380.156 | Avg score: -235.582 |\n",
"| Experiment: 0 Episode: 1631 | Score: -112.620 | Avg score: -235.047 |\n",
"| Experiment: 0 Episode: 1632 | Score: -179.660 | Avg score: -234.305 |\n",
"| Experiment: 0 Episode: 1633 | Score: -422.636 | Avg score: -237.851 |\n",
"| Experiment: 0 Episode: 1634 | Score: -416.968 | Avg score: -238.732 |\n",
"| Experiment: 0 Episode: 1635 | Score: -366.598 | Avg score: -240.168 |\n",
"| Experiment: 0 Episode: 1636 | Score: -451.654 | Avg score: -240.170 |\n",
"| Experiment: 0 Episode: 1637 | Score: -114.656 | Avg score: -238.874 |\n",
"| Experiment: 0 Episode: 1638 | Score: -81.874 | Avg score: -236.644 |\n",
"| Experiment: 0 Episode: 1639 | Score: -312.854 | Avg score: -236.882 |\n",
"| Experiment: 0 Episode: 1640 | Score: -260.761 | Avg score: -238.262 |\n",
"| Experiment: 0 Episode: 1641 | Score: -311.230 | Avg score: -237.119 |\n",
"| Experiment: 0 Episode: 1642 | Score: -38.812 | Avg score: -236.334 |\n",
"| Experiment: 0 Episode: 1643 | Score: -563.128 | Avg score: -240.901 |\n",
"| Experiment: 0 Episode: 1644 | Score: -288.704 | Avg score: -242.763 |\n",
"| Experiment: 0 Episode: 1645 | Score: -285.989 | Avg score: -244.017 |\n",
"| Experiment: 0 Episode: 1646 | Score: -339.380 | Avg score: -245.251 |\n",
"| Experiment: 0 Episode: 1647 | Score: -98.465 | Avg score: -243.839 |\n",
"| Experiment: 0 Episode: 1648 | Score: -43.345 | Avg score: -244.091 |\n",
"| Experiment: 0 Episode: 1649 | Score: -318.838 | Avg score: -244.608 |\n",
"| Experiment: 0 Episode: 1650 | Score: -344.880 | Avg score: -245.647 |\n",
"| Experiment: 0 Episode: 1651 | Score: -187.121 | Avg score: -246.935 |\n",
"| Experiment: 0 Episode: 1652 | Score: -85.308 | Avg score: -245.101 |\n",
"| Experiment: 0 Episode: 1653 | Score: -443.051 | Avg score: -245.334 |\n",
"| Experiment: 0 Episode: 1654 | Score: -493.734 | Avg score: -245.847 |\n",
"| Experiment: 0 Episode: 1655 | Score: -391.017 | Avg score: -248.242 |\n",
"| Experiment: 0 Episode: 1656 | Score: -112.636 | Avg score: -246.130 |\n",
"| Experiment: 0 Episode: 1657 | Score: -56.301 | Avg score: -245.732 |\n",
"| Experiment: 0 Episode: 1658 | Score: -378.255 | Avg score: -245.664 |\n",
"| Experiment: 0 Episode: 1659 | Score: -336.744 | Avg score: -245.213 |\n",
"| Experiment: 0 Episode: 1660 | Score: -265.151 | Avg score: -244.424 |\n",
"| Experiment: 0 Episode: 1661 | Score: -374.068 | Avg score: -245.865 |\n",
"| Experiment: 0 Episode: 1662 | Score: -261.181 | Avg score: -248.244 |\n",
"| Experiment: 0 Episode: 1663 | Score: -116.357 | Avg score: -245.200 |\n",
"| Experiment: 0 Episode: 1664 | Score: -76.522 | Avg score: -243.443 |\n",
"| Experiment: 0 Episode: 1665 | Score: -350.274 | Avg score: -243.667 |\n",
"| Experiment: 0 Episode: 1666 | Score: -83.825 | Avg score: -241.255 |\n",
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"| Experiment: 0 Episode: 1669 | Score: -452.837 | Avg score: -241.656 |\n",
"| Experiment: 0 Episode: 1670 | Score: -58.245 | Avg score: -241.047 |\n",
"| Experiment: 0 Episode: 1671 | Score: -357.226 | Avg score: -242.012 |\n",
"| Experiment: 0 Episode: 1672 | Score: -246.130 | Avg score: -240.995 |\n",
"| Experiment: 0 Episode: 1673 | Score: -258.018 | Avg score: -243.886 |\n",
"| Experiment: 0 Episode: 1674 | Score: -135.091 | Avg score: -244.536 |\n",
"| Experiment: 0 Episode: 1675 | Score: -199.453 | Avg score: -242.274 |\n",
"| Experiment: 0 Episode: 1676 | Score: -326.773 | Avg score: -242.257 |\n",
"| Experiment: 0 Episode: 1677 | Score: -70.344 | Avg score: -238.572 |\n",
"| Experiment: 0 Episode: 1678 | Score: -20.695 | Avg score: -237.679 |\n",
"| Experiment: 0 Episode: 1679 | Score: -211.570 | Avg score: -238.561 |\n",
"| Experiment: 0 Episode: 1680 | Score: -68.450 | Avg score: -233.806 |\n",
"| Experiment: 0 Episode: 1681 | Score: -343.664 | Avg score: -235.082 |\n",
"| Experiment: 0 Episode: 1682 | Score: -207.211 | Avg score: -233.186 |\n",
"| Experiment: 0 Episode: 1683 | Score: -293.482 | Avg score: -235.223 |\n",
"| Experiment: 0 Episode: 1684 | Score: -91.362 | Avg score: -234.595 |\n",
"| Experiment: 0 Episode: 1685 | Score: -172.501 | Avg score: -235.305 |\n",
"| Experiment: 0 Episode: 1686 | Score: -284.271 | Avg score: -233.917 |\n",
"| Experiment: 0 Episode: 1687 | Score: -268.764 | Avg score: -232.650 |\n",
"| Experiment: 0 Episode: 1688 | Score: -189.366 | Avg score: -233.457 |\n",
"| Experiment: 0 Episode: 1689 | Score: -54.736 | Avg score: -233.117 |\n",
"| Experiment: 0 Episode: 1690 | Score: -113.495 | Avg score: -230.359 |\n",
"| Experiment: 0 Episode: 1691 | Score: -84.210 | Avg score: -230.481 |\n",
"| Experiment: 0 Episode: 1692 | Score: -294.713 | Avg score: -231.791 |\n",
"| Experiment: 0 Episode: 1693 | Score: -553.254 | Avg score: -236.543 |\n",
"| Experiment: 0 Episode: 1694 | Score: -219.561 | Avg score: -236.926 |\n",
"| Experiment: 0 Episode: 1695 | Score: -158.160 | Avg score: -237.339 |\n",
"| Experiment: 0 Episode: 1696 | Score: -340.508 | Avg score: -236.675 |\n",
"| Experiment: 0 Episode: 1697 | Score: -210.869 | Avg score: -237.982 |\n",
"| Experiment: 0 Episode: 1698 | Score: -216.814 | Avg score: -239.016 |\n",
"| Experiment: 0 Episode: 1699 | Score: -83.519 | Avg score: -235.835 |\n",
"| Experiment: 0 Episode: 1700 | Score: -348.204 | Avg score: -238.443 |\n",
"| Experiment: 0 Episode: 1701 | Score: -349.975 | Avg score: -238.800 |\n",
"| Experiment: 0 Episode: 1702 | Score: -245.089 | Avg score: -236.827 |\n",
"| Experiment: 0 Episode: 1703 | Score: -324.670 | Avg score: -238.013 |\n",
"| Experiment: 0 Episode: 1704 | Score: -482.832 | Avg score: -239.488 |\n",
"| Experiment: 0 Episode: 1705 | Score: -48.506 | Avg score: -237.900 |\n",
"| Experiment: 0 Episode: 1706 | Score: -317.901 | Avg score: -239.760 |\n",
"| Experiment: 0 Episode: 1707 | Score: -161.871 | Avg score: -238.593 |\n",
"| Experiment: 0 Episode: 1708 | Score: -230.743 | Avg score: -240.315 |\n",
"| Experiment: 0 Episode: 1709 | Score: -64.948 | Avg score: -239.253 |\n",
"| Experiment: 0 Episode: 1710 | Score: -312.831 | Avg score: -238.391 |\n",
"| Experiment: 0 Episode: 1711 | Score: -82.954 | Avg score: -238.530 |\n",
"| Experiment: 0 Episode: 1712 | Score: -165.484 | Avg score: -237.767 |\n",
"| Experiment: 0 Episode: 1713 | Score: -413.696 | Avg score: -237.854 |\n",
"| Experiment: 0 Episode: 1714 | Score: -95.760 | Avg score: -235.890 |\n",
"| Experiment: 0 Episode: 1715 | Score: -103.521 | Avg score: -232.966 |\n",
"| Experiment: 0 Episode: 1716 | Score: -339.883 | Avg score: -234.785 |\n",
"| Experiment: 0 Episode: 1717 | Score: -387.102 | Avg score: -236.643 |\n",
"| Experiment: 0 Episode: 1718 | Score: -216.800 | Avg score: -236.097 |\n",
"| Experiment: 0 Episode: 1719 | Score: 88.663 | Avg score: -234.313 |\n",
"| Experiment: 0 Episode: 1720 | Score: -151.212 | Avg score: -235.435 |\n",
"| Experiment: 0 Episode: 1721 | Score: -204.189 | Avg score: -232.440 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 Episode: 1722 | Score: -278.784 | Avg score: -234.405 |\n",
"| Experiment: 0 Episode: 1723 | Score: -255.735 | Avg score: -235.545 |\n",
"| Experiment: 0 Episode: 1724 | Score: -406.441 | Avg score: -236.039 |\n",
"| Experiment: 0 Episode: 1725 | Score: -187.330 | Avg score: -237.056 |\n",
"| Experiment: 0 Episode: 1726 | Score: -266.917 | Avg score: -238.975 |\n",
"| Experiment: 0 Episode: 1727 | Score: -281.999 | Avg score: -237.652 |\n",
"| Experiment: 0 Episode: 1728 | Score: -55.759 | Avg score: -237.009 |\n",
"| Experiment: 0 Episode: 1729 | Score: -58.828 | Avg score: -235.127 |\n",
"| Experiment: 0 Episode: 1730 | Score: -489.379 | Avg score: -236.219 |\n",
"| Experiment: 0 Episode: 1731 | Score: -350.518 | Avg score: -238.598 |\n",
"| Experiment: 0 Episode: 1732 | Score: -115.988 | Avg score: -237.961 |\n",
"| Experiment: 0 Episode: 1733 | Score: -145.764 | Avg score: -235.193 |\n",
"| Experiment: 0 Episode: 1734 | Score: -417.725 | Avg score: -235.200 |\n",
"| Experiment: 0 Episode: 1735 | Score: -262.834 | Avg score: -234.163 |\n",
"| Experiment: 0 Episode: 1736 | Score: -113.423 | Avg score: -230.780 |\n",
"| Experiment: 0 Episode: 1737 | Score: -352.796 | Avg score: -233.162 |\n",
"| Experiment: 0 Episode: 1738 | Score: -109.601 | Avg score: -233.439 |\n",
"| Experiment: 0 Episode: 1739 | Score: -262.853 | Avg score: -232.939 |\n",
"| Experiment: 0 Episode: 1740 | Score: -54.907 | Avg score: -230.880 |\n",
"| Experiment: 0 Episode: 1741 | Score: -186.831 | Avg score: -229.637 |\n",
"| Experiment: 0 Episode: 1742 | Score: -477.377 | Avg score: -234.022 |\n",
"| Experiment: 0 Episode: 1743 | Score: -189.261 | Avg score: -230.283 |\n",
"| Experiment: 0 Episode: 1744 | Score: -132.658 | Avg score: -228.723 |\n",
"| Experiment: 0 Episode: 1745 | Score: -329.081 | Avg score: -229.154 |\n",
"| Experiment: 0 Episode: 1746 | Score: -449.717 | Avg score: -230.257 |\n",
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"| Experiment: 0 Episode: 1748 | Score: -35.074 | Avg score: -231.677 |\n",
"| Experiment: 0 Episode: 1749 | Score: -384.136 | Avg score: -232.330 |\n",
"| Experiment: 0 Episode: 1750 | Score: -376.962 | Avg score: -232.651 |\n",
"| Experiment: 0 Episode: 1751 | Score: -204.745 | Avg score: -232.827 |\n",
"| Experiment: 0 Episode: 1752 | Score: -399.377 | Avg score: -235.968 |\n",
"| Experiment: 0 Episode: 1753 | Score: -321.879 | Avg score: -234.756 |\n",
"| Experiment: 0 Episode: 1754 | Score: -325.576 | Avg score: -233.075 |\n",
"| Experiment: 0 Episode: 1755 | Score: -81.285 | Avg score: -229.977 |\n",
"| Experiment: 0 Episode: 1756 | Score: -201.068 | Avg score: -230.862 |\n",
"| Experiment: 0 Episode: 1757 | Score: -45.117 | Avg score: -230.750 |\n",
"| Experiment: 0 Episode: 1758 | Score: -284.599 | Avg score: -229.813 |\n",
"| Experiment: 0 Episode: 1759 | Score: -139.186 | Avg score: -227.838 |\n",
"| Experiment: 0 Episode: 1760 | Score: -277.122 | Avg score: -227.958 |\n",
"| Experiment: 0 Episode: 1761 | Score: -325.276 | Avg score: -227.470 |\n",
"| Experiment: 0 Episode: 1762 | Score: -154.166 | Avg score: -226.399 |\n",
"| Experiment: 0 Episode: 1763 | Score: -152.269 | Avg score: -226.759 |\n",
"| Experiment: 0 Episode: 1764 | Score: -256.720 | Avg score: -228.561 |\n",
"| Experiment: 0 Episode: 1765 | Score: -182.060 | Avg score: -226.878 |\n",
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"| Experiment: 0 Episode: 1767 | Score: -246.935 | Avg score: -228.900 |\n",
"| Experiment: 0 Episode: 1768 | Score: -120.563 | Avg score: -229.073 |\n",
"| Experiment: 0 Episode: 1769 | Score: -334.163 | Avg score: -227.886 |\n",
"| Experiment: 0 Episode: 1770 | Score: -167.203 | Avg score: -228.976 |\n",
"| Experiment: 0 Episode: 1771 | Score: -120.014 | Avg score: -226.604 |\n",
"| Experiment: 0 Episode: 1772 | Score: 17.194 | Avg score: -223.971 |\n",
"| Experiment: 0 Episode: 1773 | Score: -325.705 | Avg score: -224.647 |\n",
"| Experiment: 0 Episode: 1774 | Score: -131.990 | Avg score: -224.616 |\n",
"| Experiment: 0 Episode: 1775 | Score: -238.077 | Avg score: -225.003 |\n",
"| Experiment: 0 Episode: 1776 | Score: -310.697 | Avg score: -224.842 |\n",
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"| Experiment: 0 Episode: 1779 | Score: -319.330 | Avg score: -229.030 |\n",
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"| Experiment: 0 Episode: 1782 | Score: -262.072 | Avg score: -228.982 |\n",
"| Experiment: 0 Episode: 1783 | Score: -274.325 | Avg score: -228.791 |\n",
"| Experiment: 0 Episode: 1784 | Score: 81.579 | Avg score: -227.061 |\n",
"| Experiment: 0 Episode: 1785 | Score: -119.173 | Avg score: -226.528 |\n",
"| Experiment: 0 Episode: 1786 | Score: -397.725 | Avg score: -227.663 |\n",
"| Experiment: 0 Episode: 1787 | Score: -328.999 | Avg score: -228.265 |\n",
"| Experiment: 0 Episode: 1788 | Score: -103.400 | Avg score: -227.405 |\n",
"| Experiment: 0 Episode: 1789 | Score: -223.203 | Avg score: -229.090 |\n",
"| Experiment: 0 Episode: 1790 | Score: -62.396 | Avg score: -228.579 |\n",
"| Experiment: 0 Episode: 1791 | Score: -66.820 | Avg score: -228.405 |\n",
"| Experiment: 0 Episode: 1792 | Score: -276.551 | Avg score: -228.223 |\n",
"| Experiment: 0 Episode: 1793 | Score: -38.764 | Avg score: -223.079 |\n",
"| Experiment: 0 Episode: 1794 | Score: -85.689 | Avg score: -221.740 |\n",
"| Experiment: 0 Episode: 1795 | Score: -158.808 | Avg score: -221.746 |\n",
"| Experiment: 0 Episode: 1796 | Score: -226.743 | Avg score: -220.609 |\n",
"| Experiment: 0 Episode: 1797 | Score: -438.734 | Avg score: -222.887 |\n",
"| Experiment: 0 Episode: 1798 | Score: -281.685 | Avg score: -223.536 |\n",
"| Experiment: 0 Episode: 1799 | Score: -309.061 | Avg score: -225.791 |\n",
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"| Experiment: 0 Episode: 1801 | Score: -264.618 | Avg score: -225.894 |\n",
"| Experiment: 0 Episode: 1802 | Score: -166.027 | Avg score: -225.103 |\n",
"| Experiment: 0 Episode: 1803 | Score: -266.882 | Avg score: -224.525 |\n",
"| Experiment: 0 Episode: 1804 | Score: -406.136 | Avg score: -223.758 |\n",
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"| Experiment: 0 Episode: 1806 | Score: -226.220 | Avg score: -223.749 |\n",
"| Experiment: 0 Episode: 1807 | Score: -377.385 | Avg score: -225.904 |\n",
"| Experiment: 0 Episode: 1808 | Score: -54.263 | Avg score: -224.140 |\n",
"| Experiment: 0 Episode: 1809 | Score: -215.954 | Avg score: -225.650 |\n",
"| Experiment: 0 Episode: 1810 | Score: -268.908 | Avg score: -225.210 |\n",
"| Experiment: 0 Episode: 1811 | Score: -50.725 | Avg score: -224.888 |\n",
"| Experiment: 0 Episode: 1812 | Score: -93.037 | Avg score: -224.164 |\n",
"| Experiment: 0 Episode: 1813 | Score: -413.165 | Avg score: -224.158 |\n",
"| Experiment: 0 Episode: 1814 | Score: -98.217 | Avg score: -224.183 |\n",
"| Experiment: 0 Episode: 1815 | Score: -66.115 | Avg score: -223.809 |\n",
"| Experiment: 0 Episode: 1816 | Score: -216.264 | Avg score: -222.573 |\n",
"| Experiment: 0 Episode: 1817 | Score: -408.125 | Avg score: -222.783 |\n",
"| Experiment: 0 Episode: 1818 | Score: -434.323 | Avg score: -224.958 |\n",
"| Experiment: 0 Episode: 1819 | Score: -268.890 | Avg score: -228.534 |\n",
"| Experiment: 0 Episode: 1820 | Score: -331.403 | Avg score: -230.336 |\n",
"| Experiment: 0 Episode: 1821 | Score: -240.645 | Avg score: -230.700 |\n",
"| Experiment: 0 Episode: 1822 | Score: -506.892 | Avg score: -232.981 |\n",
"| Experiment: 0 Episode: 1823 | Score: -80.528 | Avg score: -231.229 |\n",
"| Experiment: 0 Episode: 1824 | Score: -287.879 | Avg score: -230.043 |\n",
"| Experiment: 0 Episode: 1825 | Score: -405.702 | Avg score: -232.227 |\n",
"| Experiment: 0 Episode: 1826 | Score: -161.179 | Avg score: -231.170 |\n",
"| Experiment: 0 Episode: 1827 | Score: -137.078 | Avg score: -229.721 |\n",
"| Experiment: 0 Episode: 1828 | Score: -476.026 | Avg score: -233.923 |\n",
"| Experiment: 0 Episode: 1829 | Score: -93.205 | Avg score: -234.267 |\n",
"| Experiment: 0 Episode: 1830 | Score: -74.704 | Avg score: -230.120 |\n",
"| Experiment: 0 Episode: 1831 | Score: -321.589 | Avg score: -229.831 |\n",
"| Experiment: 0 Episode: 1832 | Score: -93.287 | Avg score: -229.604 |\n",
"| Experiment: 0 Episode: 1833 | Score: -141.824 | Avg score: -229.565 |\n",
"| Experiment: 0 Episode: 1834 | Score: -175.911 | Avg score: -227.146 |\n",
"| Experiment: 0 Episode: 1835 | Score: -116.165 | Avg score: -225.680 |\n",
"| Experiment: 0 Episode: 1836 | Score: -317.205 | Avg score: -227.718 |\n",
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"| Experiment: 0 Episode: 1838 | Score: -71.607 | Avg score: -226.927 |\n",
"| Experiment: 0 Episode: 1839 | Score: -368.535 | Avg score: -227.984 |\n",
"| Experiment: 0 Episode: 1840 | Score: -388.572 | Avg score: -231.321 |\n",
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"| Experiment: 0 Episode: 1842 | Score: -385.260 | Avg score: -231.482 |\n",
"| Experiment: 0 Episode: 1843 | Score: -186.764 | Avg score: -231.457 |\n",
"| Experiment: 0 Episode: 1844 | Score: -485.571 | Avg score: -234.986 |\n",
"| Experiment: 0 Episode: 1845 | Score: -262.459 | Avg score: -234.320 |\n",
"| Experiment: 0 Episode: 1846 | Score: -116.378 | Avg score: -230.986 |\n",
"| Experiment: 0 Episode: 1847 | Score: -368.863 | Avg score: -232.188 |\n",
"| Experiment: 0 Episode: 1848 | Score: -169.457 | Avg score: -233.531 |\n",
"| Experiment: 0 Episode: 1849 | Score: -352.862 | Avg score: -233.219 |\n",
"| Experiment: 0 Episode: 1850 | Score: -106.702 | Avg score: -230.516 |\n",
"| Experiment: 0 Episode: 1851 | Score: -249.636 | Avg score: -230.965 |\n",
"| Experiment: 0 Episode: 1852 | Score: -636.483 | Avg score: -233.336 |\n",
"| Experiment: 0 Episode: 1853 | Score: -158.373 | Avg score: -231.701 |\n",
"| Experiment: 0 Episode: 1854 | Score: -78.007 | Avg score: -229.225 |\n",
"| Experiment: 0 Episode: 1855 | Score: -159.598 | Avg score: -230.008 |\n",
"| Experiment: 0 Episode: 1856 | Score: -74.113 | Avg score: -228.739 |\n",
"| Experiment: 0 Episode: 1857 | Score: -92.760 | Avg score: -229.215 |\n",
"| Experiment: 0 Episode: 1858 | Score: -14.924 | Avg score: -226.519 |\n",
"| Experiment: 0 Episode: 1859 | Score: -381.355 | Avg score: -228.940 |\n",
"| Experiment: 0 Episode: 1860 | Score: -19.831 | Avg score: -226.367 |\n",
"| Experiment: 0 Episode: 1861 | Score: -312.571 | Avg score: -226.240 |\n",
"| Experiment: 0 Episode: 1862 | Score: -394.574 | Avg score: -228.644 |\n",
"| Experiment: 0 Episode: 1863 | Score: -208.006 | Avg score: -229.202 |\n",
"| Experiment: 0 Episode: 1864 | Score: -55.931 | Avg score: -227.194 |\n",
"| Experiment: 0 Episode: 1865 | Score: -431.294 | Avg score: -229.686 |\n",
"| Experiment: 0 Episode: 1866 | Score: -335.324 | Avg score: -229.368 |\n",
"| Experiment: 0 Episode: 1867 | Score: -219.835 | Avg score: -229.097 |\n",
"| Experiment: 0 Episode: 1868 | Score: -214.803 | Avg score: -230.039 |\n",
"| Experiment: 0 Episode: 1869 | Score: -397.691 | Avg score: -230.675 |\n",
"| Experiment: 0 Episode: 1870 | Score: -66.004 | Avg score: -229.663 |\n",
"| Experiment: 0 Episode: 1871 | Score: -225.489 | Avg score: -230.717 |\n",
"| Experiment: 0 Episode: 1872 | Score: -201.622 | Avg score: -232.906 |\n",
"| Experiment: 0 Episode: 1873 | Score: -284.473 | Avg score: -232.493 |\n",
"| Experiment: 0 Episode: 1874 | Score: -288.459 | Avg score: -234.058 |\n",
"| Experiment: 0 Episode: 1875 | Score: -112.536 | Avg score: -232.803 |\n",
"| Experiment: 0 Episode: 1876 | Score: -166.918 | Avg score: -231.365 |\n",
"| Experiment: 0 Episode: 1877 | Score: -120.074 | Avg score: -231.262 |\n",
"| Experiment: 0 Episode: 1878 | Score: -205.791 | Avg score: -230.602 |\n",
"| Experiment: 0 Episode: 1879 | Score: -160.357 | Avg score: -229.013 |\n",
"| Experiment: 0 Episode: 1880 | Score: -26.083 | Avg score: -228.075 |\n",
"| Experiment: 0 Episode: 1881 | Score: -120.243 | Avg score: -226.951 |\n",
"| Experiment: 0 Episode: 1882 | Score: -235.239 | Avg score: -226.683 |\n",
"| Experiment: 0 Episode: 1883 | Score: -206.915 | Avg score: -226.009 |\n",
"| Experiment: 0 Episode: 1884 | Score: -402.639 | Avg score: -230.851 |\n",
"| Experiment: 0 Episode: 1885 | Score: -105.198 | Avg score: -230.711 |\n",
"| Experiment: 0 Episode: 1886 | Score: -84.689 | Avg score: -227.581 |\n",
"| Experiment: 0 Episode: 1887 | Score: -276.963 | Avg score: -227.061 |\n",
"| Experiment: 0 Episode: 1888 | Score: -76.148 | Avg score: -226.788 |\n",
"| Experiment: 0 Episode: 1889 | Score: -100.674 | Avg score: -225.563 |\n",
"| Experiment: 0 Episode: 1890 | Score: -353.763 | Avg score: -228.476 |\n",
"| Experiment: 0 Episode: 1891 | Score: -73.793 | Avg score: -228.546 |\n",
"| Experiment: 0 Episode: 1892 | Score: -280.358 | Avg score: -228.584 |\n",
"| Experiment: 0 Episode: 1893 | Score: -260.723 | Avg score: -230.804 |\n",
"| Experiment: 0 Episode: 1894 | Score: -274.238 | Avg score: -232.689 |\n",
"| Experiment: 0 Episode: 1895 | Score: -402.505 | Avg score: -235.126 |\n",
"| Experiment: 0 Episode: 1896 | Score: -384.322 | Avg score: -236.702 |\n",
"| Experiment: 0 Episode: 1897 | Score: -118.791 | Avg score: -233.503 |\n",
"| Experiment: 0 Episode: 1898 | Score: -75.494 | Avg score: -231.441 |\n",
"| Experiment: 0 Episode: 1899 | Score: -154.534 | Avg score: -229.895 |\n",
"| Experiment: 0 Episode: 1900 | Score: -255.823 | Avg score: -228.016 |\n",
"| Experiment: 0 Episode: 1901 | Score: -143.338 | Avg score: -226.803 |\n",
"| Experiment: 0 Episode: 1902 | Score: -51.717 | Avg score: -225.660 |\n",
"| Experiment: 0 Episode: 1903 | Score: -293.525 | Avg score: -225.926 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 Episode: 1904 | Score: -110.399 | Avg score: -222.969 |\n",
"| Experiment: 0 Episode: 1905 | Score: -310.754 | Avg score: -224.684 |\n",
"| Experiment: 0 Episode: 1906 | Score: -74.580 | Avg score: -223.167 |\n",
"| Experiment: 0 Episode: 1907 | Score: -176.898 | Avg score: -221.162 |\n",
"| Experiment: 0 Episode: 1908 | Score: -353.468 | Avg score: -224.154 |\n",
"| Experiment: 0 Episode: 1909 | Score: -458.708 | Avg score: -226.582 |\n",
"| Experiment: 0 Episode: 1910 | Score: -114.554 | Avg score: -225.038 |\n",
"| Experiment: 0 Episode: 1911 | Score: -249.678 | Avg score: -227.028 |\n",
"| Experiment: 0 Episode: 1912 | Score: -175.718 | Avg score: -227.855 |\n",
"| Experiment: 0 Episode: 1913 | Score: -102.950 | Avg score: -224.753 |\n",
"| Experiment: 0 Episode: 1914 | Score: -384.589 | Avg score: -227.616 |\n",
"| Experiment: 0 Episode: 1915 | Score: -66.301 | Avg score: -227.618 |\n",
"| Experiment: 0 Episode: 1916 | Score: -103.961 | Avg score: -226.495 |\n",
"| Experiment: 0 Episode: 1917 | Score: -77.414 | Avg score: -223.188 |\n",
"| Experiment: 0 Episode: 1918 | Score: -189.826 | Avg score: -220.743 |\n",
"| Experiment: 0 Episode: 1919 | Score: -85.675 | Avg score: -218.911 |\n",
"| Experiment: 0 Episode: 1920 | Score: -244.664 | Avg score: -218.044 |\n",
"| Experiment: 0 Episode: 1921 | Score: -177.095 | Avg score: -217.408 |\n",
"| Experiment: 0 Episode: 1922 | Score: -261.000 | Avg score: -214.949 |\n",
"| Experiment: 0 Episode: 1923 | Score: -225.673 | Avg score: -216.401 |\n",
"| Experiment: 0 Episode: 1924 | Score: -303.994 | Avg score: -216.562 |\n",
"| Experiment: 0 Episode: 1925 | Score: -72.461 | Avg score: -213.229 |\n",
"| Experiment: 0 Episode: 1926 | Score: -126.122 | Avg score: -212.879 |\n",
"| Experiment: 0 Episode: 1927 | Score: -409.090 | Avg score: -215.599 |\n",
"| Experiment: 0 Episode: 1928 | Score: -50.597 | Avg score: -211.345 |\n",
"| Experiment: 0 Episode: 1929 | Score: -323.916 | Avg score: -213.652 |\n",
"| Experiment: 0 Episode: 1930 | Score: -435.260 | Avg score: -217.257 |\n",
"| Experiment: 0 Episode: 1931 | Score: -128.085 | Avg score: -215.322 |\n",
"| Experiment: 0 Episode: 1932 | Score: -104.255 | Avg score: -215.432 |\n",
"| Experiment: 0 Episode: 1933 | Score: -359.543 | Avg score: -217.609 |\n",
"| Experiment: 0 Episode: 1934 | Score: -356.143 | Avg score: -219.411 |\n",
"| Experiment: 0 Episode: 1935 | Score: -176.764 | Avg score: -220.017 |\n",
"| Experiment: 0 Episode: 1936 | Score: -535.920 | Avg score: -222.205 |\n",
"| Experiment: 0 Episode: 1937 | Score: -353.934 | Avg score: -222.626 |\n",
"| Experiment: 0 Episode: 1938 | Score: -198.971 | Avg score: -223.900 |\n",
"| Experiment: 0 Episode: 1939 | Score: -232.391 | Avg score: -222.539 |\n",
"| Experiment: 0 Episode: 1940 | Score: -314.977 | Avg score: -221.803 |\n",
"| Experiment: 0 Episode: 1941 | Score: -432.827 | Avg score: -223.180 |\n",
"| Experiment: 0 Episode: 1942 | Score: -271.075 | Avg score: -222.038 |\n",
"| Experiment: 0 Episode: 1943 | Score: -136.402 | Avg score: -221.535 |\n",
"| Experiment: 0 Episode: 1944 | Score: -106.354 | Avg score: -217.743 |\n",
"| Experiment: 0 Episode: 1945 | Score: -138.888 | Avg score: -216.507 |\n",
"| Experiment: 0 Episode: 1946 | Score: -242.143 | Avg score: -217.764 |\n",
"| Experiment: 0 Episode: 1947 | Score: -469.661 | Avg score: -218.772 |\n",
"| Experiment: 0 Episode: 1948 | Score: -141.570 | Avg score: -218.494 |\n",
"| Experiment: 0 Episode: 1949 | Score: -116.273 | Avg score: -216.128 |\n",
"| Experiment: 0 Episode: 1950 | Score: -206.627 | Avg score: -217.127 |\n",
"| Experiment: 0 Episode: 1951 | Score: -426.845 | Avg score: -218.899 |\n",
"| Experiment: 0 Episode: 1952 | Score: -260.522 | Avg score: -215.139 |\n",
"| Experiment: 0 Episode: 1953 | Score: -346.090 | Avg score: -217.017 |\n",
"| Experiment: 0 Episode: 1954 | Score: -64.678 | Avg score: -216.883 |\n",
"| Experiment: 0 Episode: 1955 | Score: -115.687 | Avg score: -216.444 |\n",
"| Experiment: 0 Episode: 1956 | Score: -55.454 | Avg score: -216.258 |\n",
"| Experiment: 0 Episode: 1957 | Score: -178.768 | Avg score: -217.118 |\n",
"| Experiment: 0 Episode: 1958 | Score: -135.381 | Avg score: -218.322 |\n",
"| Experiment: 0 Episode: 1959 | Score: -251.203 | Avg score: -217.021 |\n",
"| Experiment: 0 Episode: 1960 | Score: -290.298 | Avg score: -219.725 |\n",
"| Experiment: 0 Episode: 1961 | Score: -194.692 | Avg score: -218.547 |\n",
"| Experiment: 0 Episode: 1962 | Score: -353.318 | Avg score: -218.134 |\n",
"| Experiment: 0 Episode: 1963 | Score: -403.661 | Avg score: -220.091 |\n",
"| Experiment: 0 Episode: 1964 | Score: -218.283 | Avg score: -221.714 |\n",
"| Experiment: 0 Episode: 1965 | Score: -92.026 | Avg score: -218.321 |\n",
"| Experiment: 0 Episode: 1966 | Score: -141.807 | Avg score: -216.386 |\n",
"| Experiment: 0 Episode: 1967 | Score: -357.640 | Avg score: -217.764 |\n",
"| Experiment: 0 Episode: 1968 | Score: -95.199 | Avg score: -216.568 |\n",
"| Experiment: 0 Episode: 1969 | Score: -212.224 | Avg score: -214.714 |\n",
"| Experiment: 0 Episode: 1970 | Score: -121.169 | Avg score: -215.265 |\n",
"| Experiment: 0 Episode: 1971 | Score: -325.072 | Avg score: -216.261 |\n",
"| Experiment: 0 Episode: 1972 | Score: -309.895 | Avg score: -217.344 |\n",
"| Experiment: 0 Episode: 1973 | Score: -311.810 | Avg score: -217.617 |\n",
"| Experiment: 0 Episode: 1974 | Score: -177.035 | Avg score: -216.503 |\n",
"| Experiment: 0 Episode: 1975 | Score: -246.476 | Avg score: -217.842 |\n",
"| Experiment: 0 Episode: 1976 | Score: -195.574 | Avg score: -218.129 |\n",
"| Experiment: 0 Episode: 1977 | Score: -313.213 | Avg score: -220.060 |\n",
"| Experiment: 0 Episode: 1978 | Score: -117.407 | Avg score: -219.176 |\n",
"| Experiment: 0 Episode: 1979 | Score: -310.926 | Avg score: -220.682 |\n",
"| Experiment: 0 Episode: 1980 | Score: -255.697 | Avg score: -222.978 |\n",
"| Experiment: 0 Episode: 1981 | Score: -422.021 | Avg score: -225.996 |\n",
"| Experiment: 0 Episode: 1982 | Score: -344.077 | Avg score: -227.084 |\n",
"| Experiment: 0 Episode: 1983 | Score: -511.537 | Avg score: -230.131 |\n",
"| Experiment: 0 Episode: 1984 | Score: -319.576 | Avg score: -229.300 |\n",
"| Experiment: 0 Episode: 1985 | Score: -113.339 | Avg score: -229.381 |\n",
"| Experiment: 0 Episode: 1986 | Score: -380.759 | Avg score: -232.342 |\n",
"| Experiment: 0 Episode: 1987 | Score: -44.148 | Avg score: -230.014 |\n",
"| Experiment: 0 Episode: 1988 | Score: -323.511 | Avg score: -232.488 |\n",
"| Experiment: 0 Episode: 1989 | Score: -68.496 | Avg score: -232.166 |\n",
"| Experiment: 0 Episode: 1990 | Score: -233.692 | Avg score: -230.965 |\n",
"| Experiment: 0 Episode: 1991 | Score: -34.061 | Avg score: -230.568 |\n",
"| Experiment: 0 Episode: 1992 | Score: -264.242 | Avg score: -230.407 |\n",
"| Experiment: 0 Episode: 1993 | Score: -151.907 | Avg score: -229.319 |\n",
"| Experiment: 0 Episode: 1994 | Score: -100.471 | Avg score: -227.581 |\n",
"| Experiment: 0 Episode: 1995 | Score: -202.102 | Avg score: -225.577 |\n",
"| Experiment: 0 Episode: 1996 | Score: -59.082 | Avg score: -222.324 |\n",
"| Experiment: 0 Episode: 1997 | Score: -326.797 | Avg score: -224.404 |\n",
"| Experiment: 0 Episode: 1998 | Score: -416.919 | Avg score: -227.819 |\n",
"| Experiment: 0 Episode: 1999 | Score: -200.842 | Avg score: -228.282 |\n",
"| Experiment: 1 Episode: 0 | Score: -346.079 | Avg score: -346.079 |\n",
"| Experiment: 1 Episode: 1 | Score: -267.976 | Avg score: -307.027 |\n",
"| Experiment: 1 Episode: 2 | Score: -279.222 | Avg score: -297.759 |\n",
"| Experiment: 1 Episode: 3 | Score: -248.830 | Avg score: -285.527 |\n",
"| Experiment: 1 Episode: 4 | Score: -261.174 | Avg score: -280.656 |\n",
"| Experiment: 1 Episode: 5 | Score: -31.964 | Avg score: -239.207 |\n",
"| Experiment: 1 Episode: 6 | Score: -84.633 | Avg score: -217.125 |\n",
"| Experiment: 1 Episode: 7 | Score: -274.225 | Avg score: -224.263 |\n",
"| Experiment: 1 Episode: 8 | Score: -130.239 | Avg score: -213.816 |\n",
"| Experiment: 1 Episode: 9 | Score: -482.082 | Avg score: -240.642 |\n",
"| Experiment: 1 Episode: 10 | Score: -303.409 | Avg score: -246.348 |\n",
"| Experiment: 1 Episode: 11 | Score: -213.311 | Avg score: -243.595 |\n",
"| Experiment: 1 Episode: 12 | Score: -281.415 | Avg score: -246.505 |\n",
"| Experiment: 1 Episode: 13 | Score: -32.355 | Avg score: -231.208 |\n",
"| Experiment: 1 Episode: 14 | Score: -203.220 | Avg score: -229.342 |\n",
"| Experiment: 1 Episode: 15 | Score: -382.104 | Avg score: -238.890 |\n",
"| Experiment: 1 Episode: 16 | Score: -57.581 | Avg score: -228.225 |\n",
"| Experiment: 1 Episode: 17 | Score: -455.754 | Avg score: -240.865 |\n",
"| Experiment: 1 Episode: 18 | Score: -110.425 | Avg score: -234.000 |\n",
"| Experiment: 1 Episode: 19 | Score: -125.578 | Avg score: -228.579 |\n",
"| Experiment: 1 Episode: 20 | Score: -399.040 | Avg score: -236.696 |\n",
"| Experiment: 1 Episode: 21 | Score: -279.623 | Avg score: -238.647 |\n",
"| Experiment: 1 Episode: 22 | Score: -230.742 | Avg score: -238.304 |\n",
"| Experiment: 1 Episode: 23 | Score: -128.888 | Avg score: -233.745 |\n",
"| Experiment: 1 Episode: 24 | Score: -79.400 | Avg score: -227.571 |\n",
"| Experiment: 1 Episode: 25 | Score: -71.509 | Avg score: -221.568 |\n",
"| Experiment: 1 Episode: 26 | Score: -78.751 | Avg score: -216.279 |\n",
"| Experiment: 1 Episode: 27 | Score: -126.135 | Avg score: -213.059 |\n",
"| Experiment: 1 Episode: 28 | Score: -277.584 | Avg score: -215.284 |\n",
"| Experiment: 1 Episode: 29 | Score: -71.181 | Avg score: -210.481 |\n",
"| Experiment: 1 Episode: 30 | Score: -278.072 | Avg score: -212.661 |\n",
"| Experiment: 1 Episode: 31 | Score: -343.642 | Avg score: -216.754 |\n",
"| Experiment: 1 Episode: 32 | Score: -320.985 | Avg score: -219.913 |\n",
"| Experiment: 1 Episode: 33 | Score: -400.746 | Avg score: -225.232 |\n",
"| Experiment: 1 Episode: 34 | Score: 104.057 | Avg score: -215.823 |\n",
"| Experiment: 1 Episode: 35 | Score: -91.267 | Avg score: -212.363 |\n",
"| Experiment: 1 Episode: 36 | Score: -156.895 | Avg score: -210.864 |\n",
"| Experiment: 1 Episode: 37 | Score: -285.101 | Avg score: -212.818 |\n",
"| Experiment: 1 Episode: 38 | Score: -438.242 | Avg score: -218.598 |\n",
"| Experiment: 1 Episode: 39 | Score: -252.644 | Avg score: -219.449 |\n",
"| Experiment: 1 Episode: 40 | Score: -110.771 | Avg score: -216.798 |\n",
"| Experiment: 1 Episode: 41 | Score: -94.530 | Avg score: -213.887 |\n",
"| Experiment: 1 Episode: 42 | Score: -148.586 | Avg score: -212.369 |\n",
"| Experiment: 1 Episode: 43 | Score: -193.790 | Avg score: -211.946 |\n",
"| Experiment: 1 Episode: 44 | Score: -329.361 | Avg score: -214.556 |\n",
"| Experiment: 1 Episode: 45 | Score: -219.345 | Avg score: -214.660 |\n",
"| Experiment: 1 Episode: 46 | Score: -317.461 | Avg score: -216.847 |\n",
"| Experiment: 1 Episode: 47 | Score: -66.206 | Avg score: -213.709 |\n",
"| Experiment: 1 Episode: 48 | Score: -135.560 | Avg score: -212.114 |\n",
"| Experiment: 1 Episode: 49 | Score: -102.785 | Avg score: -209.927 |\n",
"| Experiment: 1 Episode: 50 | Score: -332.576 | Avg score: -212.332 |\n",
"| Experiment: 1 Episode: 51 | Score: 53.746 | Avg score: -207.215 |\n",
"| Experiment: 1 Episode: 52 | Score: -76.861 | Avg score: -204.756 |\n",
"| Experiment: 1 Episode: 53 | Score: -294.724 | Avg score: -206.422 |\n",
"| Experiment: 1 Episode: 54 | Score: -83.400 | Avg score: -204.185 |\n",
"| Experiment: 1 Episode: 55 | Score: -47.162 | Avg score: -201.381 |\n",
"| Experiment: 1 Episode: 56 | Score: -420.509 | Avg score: -205.225 |\n",
"| Experiment: 1 Episode: 57 | Score: -1.282 | Avg score: -201.709 |\n",
"| Experiment: 1 Episode: 58 | Score: -41.521 | Avg score: -198.994 |\n",
"| Experiment: 1 Episode: 59 | Score: -195.046 | Avg score: -198.928 |\n",
"| Experiment: 1 Episode: 60 | Score: -121.981 | Avg score: -197.667 |\n",
"| Experiment: 1 Episode: 61 | Score: -273.725 | Avg score: -198.894 |\n",
"| Experiment: 1 Episode: 62 | Score: -442.673 | Avg score: -202.763 |\n",
"| Experiment: 1 Episode: 63 | Score: -507.955 | Avg score: -207.532 |\n",
"| Experiment: 1 Episode: 64 | Score: -54.784 | Avg score: -205.182 |\n",
"| Experiment: 1 Episode: 65 | Score: -94.837 | Avg score: -203.510 |\n",
"| Experiment: 1 Episode: 66 | Score: -128.535 | Avg score: -202.391 |\n",
"| Experiment: 1 Episode: 67 | Score: -322.126 | Avg score: -204.152 |\n",
"| Experiment: 1 Episode: 68 | Score: -117.404 | Avg score: -202.894 |\n",
"| Experiment: 1 Episode: 69 | Score: -381.346 | Avg score: -205.444 |\n",
"| Experiment: 1 Episode: 70 | Score: -197.459 | Avg score: -205.331 |\n",
"| Experiment: 1 Episode: 71 | Score: -88.603 | Avg score: -203.710 |\n",
"| Experiment: 1 Episode: 72 | Score: -315.945 | Avg score: -205.248 |\n",
"| Experiment: 1 Episode: 73 | Score: -307.098 | Avg score: -206.624 |\n",
"| Experiment: 1 Episode: 74 | Score: -85.947 | Avg score: -205.015 |\n",
"| Experiment: 1 Episode: 75 | Score: -147.820 | Avg score: -204.262 |\n",
"| Experiment: 1 Episode: 76 | Score: -235.203 | Avg score: -204.664 |\n",
"| Experiment: 1 Episode: 77 | Score: -114.383 | Avg score: -203.507 |\n",
"| Experiment: 1 Episode: 78 | Score: -78.176 | Avg score: -201.920 |\n",
"| Experiment: 1 Episode: 79 | Score: -152.374 | Avg score: -201.301 |\n",
"| Experiment: 1 Episode: 80 | Score: -212.233 | Avg score: -201.436 |\n",
"| Experiment: 1 Episode: 81 | Score: -246.198 | Avg score: -201.982 |\n",
"| Experiment: 1 Episode: 82 | Score: -344.255 | Avg score: -203.696 |\n",
"| Experiment: 1 Episode: 83 | Score: -137.929 | Avg score: -202.913 |\n",
"| Experiment: 1 Episode: 84 | Score: -346.617 | Avg score: -204.604 |\n",
"| Experiment: 1 Episode: 85 | Score: -85.773 | Avg score: -203.222 |\n",
"| Experiment: 1 Episode: 86 | Score: -99.604 | Avg score: -202.031 |\n",
"| Experiment: 1 Episode: 87 | Score: -371.447 | Avg score: -203.956 |\n",
"| Experiment: 1 Episode: 88 | Score: -158.590 | Avg score: -203.446 |\n",
"| Experiment: 1 Episode: 89 | Score: -163.439 | Avg score: -203.002 |\n",
"| Experiment: 1 Episode: 90 | Score: -114.326 | Avg score: -202.027 |\n",
"| Experiment: 1 Episode: 91 | Score: -307.194 | Avg score: -203.170 |\n",
"| Experiment: 1 Episode: 92 | Score: -406.914 | Avg score: -205.361 |\n",
"| Experiment: 1 Episode: 93 | Score: -92.142 | Avg score: -204.157 |\n",
"| Experiment: 1 Episode: 94 | Score: -91.834 | Avg score: -202.974 |\n",
"| Experiment: 1 Episode: 95 | Score: -100.494 | Avg score: -201.907 |\n",
"| Experiment: 1 Episode: 96 | Score: -360.316 | Avg score: -203.540 |\n",
"| Experiment: 1 Episode: 97 | Score: -82.993 | Avg score: -202.310 |\n",
"| Experiment: 1 Episode: 98 | Score: -78.849 | Avg score: -201.063 |\n",
"| Experiment: 1 Episode: 99 | Score: -403.183 | Avg score: -203.084 |\n",
"| Experiment: 1 Episode: 100 | Score: -96.261 | Avg score: -200.586 |\n",
"| Experiment: 1 Episode: 101 | Score: -122.448 | Avg score: -199.131 |\n",
"| Experiment: 1 Episode: 102 | Score: -53.135 | Avg score: -196.870 |\n",
"| Experiment: 1 Episode: 103 | Score: -225.701 | Avg score: -196.638 |\n",
"| Experiment: 1 Episode: 104 | Score: -303.876 | Avg score: -197.065 |\n",
"| Experiment: 1 Episode: 105 | Score: -455.745 | Avg score: -201.303 |\n",
"| Experiment: 1 Episode: 106 | Score: -117.512 | Avg score: -201.632 |\n",
"| Experiment: 1 Episode: 107 | Score: -425.510 | Avg score: -203.145 |\n",
"| Experiment: 1 Episode: 108 | Score: -118.765 | Avg score: -203.030 |\n",
"| Experiment: 1 Episode: 109 | Score: -157.800 | Avg score: -199.787 |\n",
"| Experiment: 1 Episode: 110 | Score: -176.887 | Avg score: -198.522 |\n",
"| Experiment: 1 Episode: 111 | Score: -160.029 | Avg score: -197.989 |\n",
"| Experiment: 1 Episode: 112 | Score: -545.880 | Avg score: -200.634 |\n",
"| Experiment: 1 Episode: 113 | Score: -46.587 | Avg score: -200.776 |\n",
"| Experiment: 1 Episode: 114 | Score: -310.221 | Avg score: -201.846 |\n",
"| Experiment: 1 Episode: 115 | Score: -344.629 | Avg score: -201.471 |\n",
"| Experiment: 1 Episode: 116 | Score: -135.007 | Avg score: -202.246 |\n",
"| Experiment: 1 Episode: 117 | Score: -22.808 | Avg score: -197.916 |\n",
"| Experiment: 1 Episode: 118 | Score: -242.377 | Avg score: -199.236 |\n",
"| Experiment: 1 Episode: 119 | Score: -96.211 | Avg score: -198.942 |\n",
"| Experiment: 1 Episode: 120 | Score: -215.164 | Avg score: -197.103 |\n",
"| Experiment: 1 Episode: 121 | Score: -88.807 | Avg score: -195.195 |\n",
"| Experiment: 1 Episode: 122 | Score: -438.520 | Avg score: -197.273 |\n",
"| Experiment: 1 Episode: 123 | Score: -589.494 | Avg score: -201.879 |\n",
"| Experiment: 1 Episode: 124 | Score: -142.546 | Avg score: -202.511 |\n",
"| Experiment: 1 Episode: 125 | Score: -365.174 | Avg score: -205.447 |\n",
"| Experiment: 1 Episode: 126 | Score: -80.838 | Avg score: -205.468 |\n",
"| Experiment: 1 Episode: 127 | Score: -195.089 | Avg score: -206.158 |\n",
"| Experiment: 1 Episode: 128 | Score: -93.763 | Avg score: -204.319 |\n",
"| Experiment: 1 Episode: 129 | Score: -85.489 | Avg score: -204.462 |\n",
"| Experiment: 1 Episode: 130 | Score: -73.024 | Avg score: -202.412 |\n",
"| Experiment: 1 Episode: 131 | Score: -302.082 | Avg score: -201.996 |\n",
"| Experiment: 1 Episode: 132 | Score: -217.902 | Avg score: -200.966 |\n",
"| Experiment: 1 Episode: 133 | Score: -294.851 | Avg score: -199.907 |\n",
"| Experiment: 1 Episode: 134 | Score: -72.893 | Avg score: -201.676 |\n",
"| Experiment: 1 Episode: 135 | Score: -269.898 | Avg score: -203.462 |\n",
"| Experiment: 1 Episode: 136 | Score: -162.421 | Avg score: -203.518 |\n",
"| Experiment: 1 Episode: 137 | Score: -82.441 | Avg score: -201.491 |\n",
"| Experiment: 1 Episode: 138 | Score: -135.143 | Avg score: -198.460 |\n",
"| Experiment: 1 Episode: 139 | Score: -112.386 | Avg score: -197.057 |\n",
"| Experiment: 1 Episode: 140 | Score: -101.949 | Avg score: -196.969 |\n",
"| Experiment: 1 Episode: 141 | Score: -170.888 | Avg score: -197.733 |\n",
"| Experiment: 1 Episode: 142 | Score: -108.551 | Avg score: -197.332 |\n",
"| Experiment: 1 Episode: 143 | Score: -140.777 | Avg score: -196.802 |\n",
"| Experiment: 1 Episode: 144 | Score: -58.227 | Avg score: -194.091 |\n",
"| Experiment: 1 Episode: 145 | Score: -292.927 | Avg score: -194.827 |\n",
"| Experiment: 1 Episode: 146 | Score: 62.413 | Avg score: -191.028 |\n",
"| Experiment: 1 Episode: 147 | Score: -100.018 | Avg score: -191.366 |\n",
"| Experiment: 1 Episode: 148 | Score: -408.614 | Avg score: -194.097 |\n",
"| Experiment: 1 Episode: 149 | Score: -135.946 | Avg score: -194.428 |\n",
"| Experiment: 1 Episode: 150 | Score: -141.470 | Avg score: -192.517 |\n",
"| Experiment: 1 Episode: 151 | Score: -389.181 | Avg score: -196.947 |\n",
"| Experiment: 1 Episode: 152 | Score: -282.454 | Avg score: -199.003 |\n",
"| Experiment: 1 Episode: 153 | Score: -385.237 | Avg score: -199.908 |\n",
"| Experiment: 1 Episode: 154 | Score: -302.530 | Avg score: -202.099 |\n",
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"| Experiment: 1 Episode: 156 | Score: -301.618 | Avg score: -201.978 |\n",
"| Experiment: 1 Episode: 157 | Score: -402.692 | Avg score: -205.993 |\n",
"| Experiment: 1 Episode: 158 | Score: -47.309 | Avg score: -206.050 |\n",
"| Experiment: 1 Episode: 159 | Score: -404.147 | Avg score: -208.141 |\n",
"| Experiment: 1 Episode: 160 | Score: -133.982 | Avg score: -208.261 |\n",
"| Experiment: 1 Episode: 161 | Score: -94.703 | Avg score: -206.471 |\n",
"| Experiment: 1 Episode: 162 | Score: -199.180 | Avg score: -204.036 |\n",
"| Experiment: 1 Episode: 163 | Score: -333.229 | Avg score: -202.289 |\n",
"| Experiment: 1 Episode: 164 | Score: -311.259 | Avg score: -204.854 |\n",
"| Experiment: 1 Episode: 165 | Score: -138.112 | Avg score: -205.287 |\n",
"| Experiment: 1 Episode: 166 | Score: -421.027 | Avg score: -208.211 |\n",
"| Experiment: 1 Episode: 167 | Score: -325.430 | Avg score: -208.245 |\n",
"| Experiment: 1 Episode: 168 | Score: -48.620 | Avg score: -207.557 |\n",
"| Experiment: 1 Episode: 169 | Score: -110.219 | Avg score: -204.845 |\n",
"| Experiment: 1 Episode: 170 | Score: -85.911 | Avg score: -203.730 |\n",
"| Experiment: 1 Episode: 171 | Score: -488.222 | Avg score: -207.726 |\n",
"| Experiment: 1 Episode: 172 | Score: -295.091 | Avg score: -207.518 |\n",
"| Experiment: 1 Episode: 173 | Score: -434.661 | Avg score: -208.793 |\n",
"| Experiment: 1 Episode: 174 | Score: -346.267 | Avg score: -211.396 |\n",
"| Experiment: 1 Episode: 175 | Score: -348.383 | Avg score: -213.402 |\n",
"| Experiment: 1 Episode: 176 | Score: -86.608 | Avg score: -211.916 |\n",
"| Experiment: 1 Episode: 177 | Score: -176.833 | Avg score: -212.541 |\n",
"| Experiment: 1 Episode: 178 | Score: -270.349 | Avg score: -214.462 |\n",
"| Experiment: 1 Episode: 179 | Score: -25.355 | Avg score: -213.192 |\n",
"| Experiment: 1 Episode: 180 | Score: -101.048 | Avg score: -212.080 |\n",
"| Experiment: 1 Episode: 181 | Score: -96.315 | Avg score: -210.581 |\n",
"| Experiment: 1 Episode: 182 | Score: -338.114 | Avg score: -210.520 |\n",
"| Experiment: 1 Episode: 183 | Score: -327.493 | Avg score: -212.416 |\n",
"| Experiment: 1 Episode: 184 | Score: -125.468 | Avg score: -210.204 |\n",
"| Experiment: 1 Episode: 185 | Score: -171.183 | Avg score: -211.058 |\n",
"| Experiment: 1 Episode: 186 | Score: -489.760 | Avg score: -214.960 |\n",
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"| Experiment: 1 Episode: 188 | Score: -435.029 | Avg score: -218.737 |\n",
"| Experiment: 1 Episode: 189 | Score: -316.570 | Avg score: -220.268 |\n",
"| Experiment: 1 Episode: 190 | Score: -492.428 | Avg score: -224.049 |\n",
"| Experiment: 1 Episode: 191 | Score: -266.567 | Avg score: -223.643 |\n",
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"| Experiment: 1 Episode: 193 | Score: -255.106 | Avg score: -223.327 |\n",
"| Experiment: 1 Episode: 194 | Score: -85.349 | Avg score: -223.263 |\n",
"| Experiment: 1 Episode: 195 | Score: -326.063 | Avg score: -225.518 |\n",
"| Experiment: 1 Episode: 196 | Score: -198.022 | Avg score: -223.895 |\n",
"| Experiment: 1 Episode: 197 | Score: -57.843 | Avg score: -223.644 |\n",
"| Experiment: 1 Episode: 198 | Score: -358.089 | Avg score: -226.436 |\n",
"| Experiment: 1 Episode: 199 | Score: -164.922 | Avg score: -224.054 |\n",
"| Experiment: 1 Episode: 200 | Score: -111.672 | Avg score: -224.208 |\n",
"| Experiment: 1 Episode: 201 | Score: -214.837 | Avg score: -225.132 |\n",
"| Experiment: 1 Episode: 202 | Score: -91.902 | Avg score: -225.519 |\n",
"| Experiment: 1 Episode: 203 | Score: -320.562 | Avg score: -226.468 |\n",
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"| Experiment: 1 Episode: 206 | Score: -89.413 | Avg score: -222.906 |\n",
"| Experiment: 1 Episode: 207 | Score: -127.671 | Avg score: -219.928 |\n",
"| Experiment: 1 Episode: 208 | Score: -34.119 | Avg score: -219.082 |\n",
"| Experiment: 1 Episode: 209 | Score: -240.488 | Avg score: -219.908 |\n",
"| Experiment: 1 Episode: 210 | Score: -316.415 | Avg score: -221.304 |\n",
"| Experiment: 1 Episode: 211 | Score: -52.798 | Avg score: -220.231 |\n",
"| Experiment: 1 Episode: 212 | Score: -214.914 | Avg score: -216.922 |\n",
"| Experiment: 1 Episode: 213 | Score: -388.411 | Avg score: -220.340 |\n",
"| Experiment: 1 Episode: 214 | Score: -290.313 | Avg score: -220.141 |\n",
"| Experiment: 1 Episode: 215 | Score: -103.886 | Avg score: -217.733 |\n",
"| Experiment: 1 Episode: 216 | Score: -47.897 | Avg score: -216.862 |\n",
"| Experiment: 1 Episode: 217 | Score: -382.680 | Avg score: -220.461 |\n",
"| Experiment: 1 Episode: 218 | Score: -77.562 | Avg score: -218.813 |\n",
"| Experiment: 1 Episode: 219 | Score: -195.310 | Avg score: -219.804 |\n",
"| Experiment: 1 Episode: 220 | Score: -325.291 | Avg score: -220.905 |\n",
"| Experiment: 1 Episode: 221 | Score: -412.153 | Avg score: -224.139 |\n",
"| Experiment: 1 Episode: 222 | Score: -474.271 | Avg score: -224.496 |\n",
"| Experiment: 1 Episode: 223 | Score: -301.920 | Avg score: -221.620 |\n",
"| Experiment: 1 Episode: 224 | Score: -370.030 | Avg score: -223.895 |\n",
"| Experiment: 1 Episode: 225 | Score: -70.201 | Avg score: -220.946 |\n",
"| Experiment: 1 Episode: 226 | Score: -142.336 | Avg score: -221.561 |\n",
"| Experiment: 1 Episode: 227 | Score: -366.193 | Avg score: -223.272 |\n",
"| Experiment: 1 Episode: 228 | Score: -249.951 | Avg score: -224.833 |\n",
"| Experiment: 1 Episode: 229 | Score: -468.196 | Avg score: -228.661 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 Episode: 230 | Score: -353.597 | Avg score: -231.466 |\n",
"| Experiment: 1 Episode: 231 | Score: -319.400 | Avg score: -231.639 |\n",
"| Experiment: 1 Episode: 232 | Score: -112.539 | Avg score: -230.586 |\n",
"| Experiment: 1 Episode: 233 | Score: -78.485 | Avg score: -228.422 |\n",
"| Experiment: 1 Episode: 234 | Score: -70.361 | Avg score: -228.397 |\n",
"| Experiment: 1 Episode: 235 | Score: -285.757 | Avg score: -228.555 |\n",
"| Experiment: 1 Episode: 236 | Score: -82.807 | Avg score: -227.759 |\n",
"| Experiment: 1 Episode: 237 | Score: -110.217 | Avg score: -228.037 |\n",
"| Experiment: 1 Episode: 238 | Score: -421.259 | Avg score: -230.898 |\n",
"| Experiment: 1 Episode: 239 | Score: -117.526 | Avg score: -230.950 |\n",
"| Experiment: 1 Episode: 240 | Score: -111.957 | Avg score: -231.050 |\n",
"| Experiment: 1 Episode: 241 | Score: -53.319 | Avg score: -229.874 |\n",
"| Experiment: 1 Episode: 242 | Score: -368.384 | Avg score: -232.472 |\n",
"| Experiment: 1 Episode: 243 | Score: -113.417 | Avg score: -232.199 |\n",
"| Experiment: 1 Episode: 244 | Score: -558.701 | Avg score: -237.203 |\n",
"| Experiment: 1 Episode: 245 | Score: -57.194 | Avg score: -234.846 |\n",
"| Experiment: 1 Episode: 246 | Score: -234.999 | Avg score: -237.820 |\n",
"| Experiment: 1 Episode: 247 | Score: -66.815 | Avg score: -237.488 |\n",
"| Experiment: 1 Episode: 248 | Score: -338.859 | Avg score: -236.791 |\n",
"| Experiment: 1 Episode: 249 | Score: -88.126 | Avg score: -236.312 |\n",
"| Experiment: 1 Episode: 250 | Score: -197.940 | Avg score: -236.877 |\n",
"| Experiment: 1 Episode: 251 | Score: -68.408 | Avg score: -233.669 |\n",
"| Experiment: 1 Episode: 252 | Score: -86.176 | Avg score: -231.707 |\n",
"| Experiment: 1 Episode: 253 | Score: -296.994 | Avg score: -230.824 |\n",
"| Experiment: 1 Episode: 254 | Score: -134.479 | Avg score: -229.144 |\n",
"| Experiment: 1 Episode: 255 | Score: -76.656 | Avg score: -228.370 |\n",
"| Experiment: 1 Episode: 256 | Score: -158.480 | Avg score: -226.939 |\n",
"| Experiment: 1 Episode: 257 | Score: -65.464 | Avg score: -223.567 |\n",
"| Experiment: 1 Episode: 258 | Score: -95.011 | Avg score: -224.044 |\n",
"| Experiment: 1 Episode: 259 | Score: -325.132 | Avg score: -223.253 |\n",
"| Experiment: 1 Episode: 260 | Score: -214.883 | Avg score: -224.062 |\n",
"| Experiment: 1 Episode: 261 | Score: -22.204 | Avg score: -223.337 |\n",
"| Experiment: 1 Episode: 262 | Score: -106.690 | Avg score: -222.413 |\n",
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"| Experiment: 1 Episode: 264 | Score: -84.950 | Avg score: -217.910 |\n",
"| Experiment: 1 Episode: 265 | Score: -312.041 | Avg score: -219.650 |\n",
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"| Experiment: 1 Episode: 267 | Score: -96.265 | Avg score: -216.210 |\n",
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"| Experiment: 1 Episode: 298 | Score: -77.388 | Avg score: -200.851 |\n",
"| Experiment: 1 Episode: 299 | Score: -337.042 | Avg score: -202.572 |\n",
"| Experiment: 1 Episode: 300 | Score: -233.940 | Avg score: -203.795 |\n",
"| Experiment: 1 Episode: 301 | Score: -203.221 | Avg score: -203.679 |\n",
"| Experiment: 1 Episode: 302 | Score: -144.359 | Avg score: -204.203 |\n",
"| Experiment: 1 Episode: 303 | Score: -324.123 | Avg score: -204.239 |\n",
"| Experiment: 1 Episode: 304 | Score: -55.098 | Avg score: -203.444 |\n",
"| Experiment: 1 Episode: 305 | Score: -110.122 | Avg score: -201.575 |\n",
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"| Experiment: 1 Episode: 317 | Score: -358.112 | Avg score: -203.263 |\n",
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"| Experiment: 1 Episode: 320 | Score: -356.179 | Avg score: -205.596 |\n",
"| Experiment: 1 Episode: 321 | Score: -46.263 | Avg score: -201.937 |\n",
"| Experiment: 1 Episode: 322 | Score: -121.843 | Avg score: -198.413 |\n",
"| Experiment: 1 Episode: 323 | Score: -221.527 | Avg score: -197.609 |\n",
"| Experiment: 1 Episode: 324 | Score: -263.462 | Avg score: -196.543 |\n",
"| Experiment: 1 Episode: 325 | Score: -145.879 | Avg score: -197.300 |\n",
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"| Experiment: 1 Episode: 328 | Score: -162.084 | Avg score: -195.940 |\n",
"| Experiment: 1 Episode: 329 | Score: -152.336 | Avg score: -192.782 |\n",
"| Experiment: 1 Episode: 330 | Score: -295.243 | Avg score: -192.198 |\n",
"| Experiment: 1 Episode: 331 | Score: -339.345 | Avg score: -192.398 |\n",
"| Experiment: 1 Episode: 332 | Score: -189.623 | Avg score: -193.168 |\n",
"| Experiment: 1 Episode: 333 | Score: -85.596 | Avg score: -193.239 |\n",
"| Experiment: 1 Episode: 334 | Score: -11.197 | Avg score: -192.648 |\n",
"| Experiment: 1 Episode: 335 | Score: -39.579 | Avg score: -190.186 |\n",
"| Experiment: 1 Episode: 336 | Score: -337.245 | Avg score: -192.730 |\n",
"| Experiment: 1 Episode: 337 | Score: -383.373 | Avg score: -195.462 |\n",
"| Experiment: 1 Episode: 338 | Score: -208.943 | Avg score: -193.339 |\n",
"| Experiment: 1 Episode: 339 | Score: -90.522 | Avg score: -193.069 |\n",
"| Experiment: 1 Episode: 340 | Score: -181.172 | Avg score: -193.761 |\n",
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"| Experiment: 1 Episode: 342 | Score: -191.615 | Avg score: -194.047 |\n",
"| Experiment: 1 Episode: 343 | Score: -69.677 | Avg score: -193.610 |\n",
"| Experiment: 1 Episode: 344 | Score: -122.363 | Avg score: -189.246 |\n",
"| Experiment: 1 Episode: 345 | Score: -79.142 | Avg score: -189.466 |\n",
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"| Experiment: 1 Episode: 347 | Score: -491.901 | Avg score: -193.261 |\n",
"| Experiment: 1 Episode: 348 | Score: -114.581 | Avg score: -191.018 |\n",
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"| Experiment: 1 Episode: 350 | Score: -101.851 | Avg score: -190.324 |\n",
"| Experiment: 1 Episode: 351 | Score: -453.679 | Avg score: -194.177 |\n",
"| Experiment: 1 Episode: 352 | Score: -349.665 | Avg score: -196.812 |\n",
"| Experiment: 1 Episode: 353 | Score: -420.059 | Avg score: -198.043 |\n",
"| Experiment: 1 Episode: 354 | Score: -324.146 | Avg score: -199.939 |\n",
"| Experiment: 1 Episode: 355 | Score: -177.367 | Avg score: -200.946 |\n",
"| Experiment: 1 Episode: 356 | Score: -103.057 | Avg score: -200.392 |\n",
"| Experiment: 1 Episode: 357 | Score: -7.624 | Avg score: -199.814 |\n",
"| Experiment: 1 Episode: 358 | Score: -432.719 | Avg score: -203.191 |\n",
"| Experiment: 1 Episode: 359 | Score: -151.721 | Avg score: -201.457 |\n",
"| Experiment: 1 Episode: 360 | Score: -67.208 | Avg score: -199.980 |\n",
"| Experiment: 1 Episode: 361 | Score: -216.748 | Avg score: -201.926 |\n",
"| Experiment: 1 Episode: 362 | Score: -169.687 | Avg score: -202.555 |\n",
"| Experiment: 1 Episode: 363 | Score: -350.528 | Avg score: -204.967 |\n",
"| Experiment: 1 Episode: 364 | Score: -199.753 | Avg score: -206.116 |\n",
"| Experiment: 1 Episode: 365 | Score: -344.209 | Avg score: -206.437 |\n",
"| Experiment: 1 Episode: 366 | Score: -393.228 | Avg score: -207.307 |\n",
"| Experiment: 1 Episode: 367 | Score: -340.424 | Avg score: -209.749 |\n",
"| Experiment: 1 Episode: 368 | Score: -336.086 | Avg score: -212.052 |\n",
"| Experiment: 1 Episode: 369 | Score: -307.391 | Avg score: -211.425 |\n",
"| Experiment: 1 Episode: 370 | Score: -12.597 | Avg score: -208.555 |\n",
"| Experiment: 1 Episode: 371 | Score: -314.573 | Avg score: -210.724 |\n",
"| Experiment: 1 Episode: 372 | Score: -374.632 | Avg score: -211.963 |\n",
"| Experiment: 1 Episode: 373 | Score: -73.177 | Avg score: -212.103 |\n",
"| Experiment: 1 Episode: 374 | Score: -304.471 | Avg score: -214.689 |\n",
"| Experiment: 1 Episode: 375 | Score: -44.295 | Avg score: -213.217 |\n",
"| Experiment: 1 Episode: 376 | Score: -62.923 | Avg score: -212.200 |\n",
"| Experiment: 1 Episode: 377 | Score: -62.176 | Avg score: -211.580 |\n",
"| Experiment: 1 Episode: 378 | Score: -211.872 | Avg score: -208.739 |\n",
"| Experiment: 1 Episode: 379 | Score: -364.469 | Avg score: -210.932 |\n",
"| Experiment: 1 Episode: 380 | Score: -272.673 | Avg score: -212.328 |\n",
"| Experiment: 1 Episode: 381 | Score: -462.880 | Avg score: -214.435 |\n",
"| Experiment: 1 Episode: 382 | Score: -158.475 | Avg score: -213.890 |\n",
"| Experiment: 1 Episode: 383 | Score: -353.004 | Avg score: -213.968 |\n",
"| Experiment: 1 Episode: 384 | Score: -164.249 | Avg score: -212.586 |\n",
"| Experiment: 1 Episode: 385 | Score: -57.553 | Avg score: -209.669 |\n",
"| Experiment: 1 Episode: 386 | Score: -359.841 | Avg score: -210.210 |\n",
"| Experiment: 1 Episode: 387 | Score: -47.685 | Avg score: -209.172 |\n",
"| Experiment: 1 Episode: 388 | Score: -114.053 | Avg score: -207.192 |\n",
"| Experiment: 1 Episode: 389 | Score: -96.798 | Avg score: -207.161 |\n",
"| Experiment: 1 Episode: 390 | Score: -204.601 | Avg score: -207.786 |\n",
"| Experiment: 1 Episode: 391 | Score: -245.106 | Avg score: -208.747 |\n",
"| Experiment: 1 Episode: 392 | Score: 89.741 | Avg score: -205.685 |\n",
"| Experiment: 1 Episode: 393 | Score: -85.904 | Avg score: -204.978 |\n",
"| Experiment: 1 Episode: 394 | Score: -156.764 | Avg score: -204.731 |\n",
"| Experiment: 1 Episode: 395 | Score: -408.230 | Avg score: -207.146 |\n",
"| Experiment: 1 Episode: 396 | Score: -111.488 | Avg score: -206.101 |\n",
"| Experiment: 1 Episode: 397 | Score: -142.265 | Avg score: -205.666 |\n",
"| Experiment: 1 Episode: 398 | Score: -379.550 | Avg score: -208.688 |\n",
"| Experiment: 1 Episode: 399 | Score: -182.204 | Avg score: -207.139 |\n",
"| Experiment: 1 Episode: 400 | Score: -366.011 | Avg score: -208.460 |\n",
"| Experiment: 1 Episode: 401 | Score: -437.388 | Avg score: -210.802 |\n",
"| Experiment: 1 Episode: 402 | Score: -117.562 | Avg score: -210.534 |\n",
"| Experiment: 1 Episode: 403 | Score: 1.477 | Avg score: -207.278 |\n",
"| Experiment: 1 Episode: 404 | Score: -317.041 | Avg score: -209.897 |\n",
"| Experiment: 1 Episode: 405 | Score: -210.593 | Avg score: -210.902 |\n",
"| Experiment: 1 Episode: 406 | Score: -178.258 | Avg score: -212.156 |\n",
"| Experiment: 1 Episode: 407 | Score: -350.654 | Avg score: -215.132 |\n",
"| Experiment: 1 Episode: 408 | Score: -397.595 | Avg score: -215.138 |\n",
"| Experiment: 1 Episode: 409 | Score: -126.122 | Avg score: -213.401 |\n",
"| Experiment: 1 Episode: 410 | Score: -167.709 | Avg score: -214.018 |\n",
"| Experiment: 1 Episode: 411 | Score: -536.862 | Avg score: -218.524 |\n",
"| Experiment: 1 Episode: 412 | Score: -273.047 | Avg score: -217.087 |\n",
"| Experiment: 1 Episode: 413 | Score: -331.990 | Avg score: -217.978 |\n",
"| Experiment: 1 Episode: 414 | Score: -261.596 | Avg score: -217.714 |\n",
"| Experiment: 1 Episode: 415 | Score: -132.586 | Avg score: -218.964 |\n",
"| Experiment: 1 Episode: 416 | Score: -202.681 | Avg score: -219.497 |\n",
"| Experiment: 1 Episode: 417 | Score: -69.146 | Avg score: -216.607 |\n",
"| Experiment: 1 Episode: 418 | Score: -343.116 | Avg score: -216.465 |\n",
"| Experiment: 1 Episode: 419 | Score: -143.677 | Avg score: -216.722 |\n",
"| Experiment: 1 Episode: 420 | Score: -159.723 | Avg score: -214.757 |\n",
"| Experiment: 1 Episode: 421 | Score: -321.516 | Avg score: -217.510 |\n",
"| Experiment: 1 Episode: 422 | Score: -65.463 | Avg score: -216.946 |\n",
"| Experiment: 1 Episode: 423 | Score: -223.097 | Avg score: -216.962 |\n",
"| Experiment: 1 Episode: 424 | Score: -247.233 | Avg score: -216.799 |\n",
"| Experiment: 1 Episode: 425 | Score: -216.482 | Avg score: -217.505 |\n",
"| Experiment: 1 Episode: 426 | Score: -397.235 | Avg score: -220.449 |\n",
"| Experiment: 1 Episode: 427 | Score: -433.038 | Avg score: -221.204 |\n",
"| Experiment: 1 Episode: 428 | Score: -184.221 | Avg score: -221.425 |\n",
"| Experiment: 1 Episode: 429 | Score: -148.745 | Avg score: -221.389 |\n",
"| Experiment: 1 Episode: 430 | Score: -188.906 | Avg score: -220.326 |\n",
"| Experiment: 1 Episode: 431 | Score: -180.302 | Avg score: -218.735 |\n",
"| Experiment: 1 Episode: 432 | Score: -405.189 | Avg score: -220.891 |\n",
"| Experiment: 1 Episode: 433 | Score: -428.798 | Avg score: -224.323 |\n",
"| Experiment: 1 Episode: 434 | Score: -216.373 | Avg score: -226.375 |\n",
"| Experiment: 1 Episode: 435 | Score: -318.668 | Avg score: -229.166 |\n",
"| Experiment: 1 Episode: 436 | Score: -196.184 | Avg score: -227.755 |\n",
"| Experiment: 1 Episode: 437 | Score: -56.705 | Avg score: -224.488 |\n",
"| Experiment: 1 Episode: 438 | Score: -124.838 | Avg score: -223.647 |\n",
"| Experiment: 1 Episode: 439 | Score: -318.695 | Avg score: -225.929 |\n",
"| Experiment: 1 Episode: 440 | Score: -264.206 | Avg score: -226.759 |\n",
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"| Experiment: 1 Episode: 442 | Score: -426.233 | Avg score: -227.596 |\n",
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"| Experiment: 1 Episode: 444 | Score: -135.850 | Avg score: -227.946 |\n",
"| Experiment: 1 Episode: 445 | Score: -397.726 | Avg score: -231.132 |\n",
"| Experiment: 1 Episode: 446 | Score: -369.184 | Avg score: -232.929 |\n",
"| Experiment: 1 Episode: 447 | Score: -233.934 | Avg score: -230.350 |\n",
"| Experiment: 1 Episode: 448 | Score: -316.976 | Avg score: -232.373 |\n",
"| Experiment: 1 Episode: 449 | Score: -246.708 | Avg score: -233.692 |\n",
"| Experiment: 1 Episode: 450 | Score: -184.265 | Avg score: -234.516 |\n",
"| Experiment: 1 Episode: 451 | Score: -188.101 | Avg score: -231.860 |\n",
"| Experiment: 1 Episode: 452 | Score: -77.993 | Avg score: -229.144 |\n",
"| Experiment: 1 Episode: 453 | Score: -265.159 | Avg score: -227.595 |\n",
"| Experiment: 1 Episode: 454 | Score: -207.937 | Avg score: -226.433 |\n",
"| Experiment: 1 Episode: 455 | Score: -82.441 | Avg score: -225.483 |\n",
"| Experiment: 1 Episode: 456 | Score: -63.593 | Avg score: -225.089 |\n",
"| Experiment: 1 Episode: 457 | Score: -103.464 | Avg score: -226.047 |\n",
"| Experiment: 1 Episode: 458 | Score: -336.993 | Avg score: -225.090 |\n",
"| Experiment: 1 Episode: 459 | Score: -328.906 | Avg score: -226.862 |\n",
"| Experiment: 1 Episode: 460 | Score: -91.997 | Avg score: -227.110 |\n",
"| Experiment: 1 Episode: 461 | Score: -89.809 | Avg score: -225.840 |\n",
"| Experiment: 1 Episode: 462 | Score: -309.039 | Avg score: -227.234 |\n",
"| Experiment: 1 Episode: 463 | Score: -141.042 | Avg score: -225.139 |\n",
"| Experiment: 1 Episode: 464 | Score: -153.011 | Avg score: -224.671 |\n",
"| Experiment: 1 Episode: 465 | Score: -111.278 | Avg score: -222.342 |\n",
"| Experiment: 1 Episode: 466 | Score: -225.671 | Avg score: -220.666 |\n",
"| Experiment: 1 Episode: 467 | Score: -398.659 | Avg score: -221.249 |\n",
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"| Experiment: 1 Episode: 469 | Score: -66.620 | Avg score: -217.457 |\n",
"| Experiment: 1 Episode: 470 | Score: -67.826 | Avg score: -218.009 |\n",
"| Experiment: 1 Episode: 471 | Score: -309.666 | Avg score: -217.960 |\n",
"| Experiment: 1 Episode: 472 | Score: -42.986 | Avg score: -214.643 |\n",
"| Experiment: 1 Episode: 473 | Score: -343.238 | Avg score: -217.344 |\n",
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"| Experiment: 1 Episode: 475 | Score: -397.489 | Avg score: -220.258 |\n",
"| Experiment: 1 Episode: 476 | Score: -395.909 | Avg score: -223.588 |\n",
"| Experiment: 1 Episode: 477 | Score: -63.024 | Avg score: -223.596 |\n",
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"| Experiment: 1 Episode: 479 | Score: -316.135 | Avg score: -223.903 |\n",
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"| Experiment: 1 Episode: 482 | Score: -269.539 | Avg score: -222.859 |\n",
"| Experiment: 1 Episode: 483 | Score: -100.093 | Avg score: -220.330 |\n",
"| Experiment: 1 Episode: 484 | Score: -107.766 | Avg score: -219.765 |\n",
"| Experiment: 1 Episode: 485 | Score: -95.070 | Avg score: -220.140 |\n",
"| Experiment: 1 Episode: 486 | Score: -64.479 | Avg score: -217.187 |\n",
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"| Experiment: 1 Episode: 488 | Score: -131.175 | Avg score: -220.669 |\n",
"| Experiment: 1 Episode: 489 | Score: -305.955 | Avg score: -222.761 |\n",
"| Experiment: 1 Episode: 490 | Score: -148.969 | Avg score: -222.204 |\n",
"| Experiment: 1 Episode: 491 | Score: -294.544 | Avg score: -222.699 |\n",
"| Experiment: 1 Episode: 492 | Score: -175.569 | Avg score: -225.352 |\n",
"| Experiment: 1 Episode: 493 | Score: -293.753 | Avg score: -227.430 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 Episode: 494 | Score: -100.338 | Avg score: -226.866 |\n",
"| Experiment: 1 Episode: 495 | Score: -154.030 | Avg score: -224.324 |\n",
"| Experiment: 1 Episode: 496 | Score: -147.973 | Avg score: -224.689 |\n",
"| Experiment: 1 Episode: 497 | Score: -424.627 | Avg score: -227.513 |\n",
"| Experiment: 1 Episode: 498 | Score: -270.205 | Avg score: -226.419 |\n",
"| Experiment: 1 Episode: 499 | Score: -316.766 | Avg score: -227.765 |\n",
"| Experiment: 1 Episode: 500 | Score: -372.623 | Avg score: -227.831 |\n",
"| Experiment: 1 Episode: 501 | Score: -286.625 | Avg score: -226.323 |\n",
"| Experiment: 1 Episode: 502 | Score: -385.493 | Avg score: -229.003 |\n",
"| Experiment: 1 Episode: 503 | Score: -52.535 | Avg score: -229.543 |\n",
"| Experiment: 1 Episode: 504 | Score: -347.996 | Avg score: -229.852 |\n",
"| Experiment: 1 Episode: 505 | Score: -259.972 | Avg score: -230.346 |\n",
"| Experiment: 1 Episode: 506 | Score: -419.112 | Avg score: -232.755 |\n",
"| Experiment: 1 Episode: 507 | Score: -154.281 | Avg score: -230.791 |\n",
"| Experiment: 1 Episode: 508 | Score: -172.614 | Avg score: -228.541 |\n",
"| Experiment: 1 Episode: 509 | Score: -144.987 | Avg score: -228.730 |\n",
"| Experiment: 1 Episode: 510 | Score: -352.624 | Avg score: -230.579 |\n",
"| Experiment: 1 Episode: 511 | Score: -338.271 | Avg score: -228.593 |\n",
"| Experiment: 1 Episode: 512 | Score: -76.476 | Avg score: -226.627 |\n",
"| Experiment: 1 Episode: 513 | Score: -129.801 | Avg score: -224.605 |\n",
"| Experiment: 1 Episode: 514 | Score: -234.011 | Avg score: -224.330 |\n",
"| Experiment: 1 Episode: 515 | Score: -163.375 | Avg score: -224.637 |\n",
"| Experiment: 1 Episode: 516 | Score: -67.881 | Avg score: -223.289 |\n",
"| Experiment: 1 Episode: 517 | Score: -87.096 | Avg score: -223.469 |\n",
"| Experiment: 1 Episode: 518 | Score: -375.839 | Avg score: -223.796 |\n",
"| Experiment: 1 Episode: 519 | Score: -411.203 | Avg score: -226.471 |\n",
"| Experiment: 1 Episode: 520 | Score: -103.628 | Avg score: -225.910 |\n",
"| Experiment: 1 Episode: 521 | Score: -562.148 | Avg score: -228.317 |\n",
"| Experiment: 1 Episode: 522 | Score: -82.195 | Avg score: -228.484 |\n",
"| Experiment: 1 Episode: 523 | Score: -84.390 | Avg score: -227.097 |\n",
"| Experiment: 1 Episode: 524 | Score: -306.304 | Avg score: -227.688 |\n",
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"| Experiment: 1 Episode: 526 | Score: -182.083 | Avg score: -225.769 |\n",
"| Experiment: 1 Episode: 527 | Score: -172.499 | Avg score: -223.164 |\n",
"| Experiment: 1 Episode: 528 | Score: -98.693 | Avg score: -222.309 |\n",
"| Experiment: 1 Episode: 529 | Score: -72.025 | Avg score: -221.541 |\n",
"| Experiment: 1 Episode: 530 | Score: -218.815 | Avg score: -221.840 |\n",
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"| Experiment: 1 Episode: 532 | Score: -62.107 | Avg score: -217.354 |\n",
"| Experiment: 1 Episode: 533 | Score: -367.382 | Avg score: -216.740 |\n",
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"| Experiment: 1 Episode: 535 | Score: -388.317 | Avg score: -216.819 |\n",
"| Experiment: 1 Episode: 536 | Score: -309.226 | Avg score: -217.950 |\n",
"| Experiment: 1 Episode: 537 | Score: -308.788 | Avg score: -220.470 |\n",
"| Experiment: 1 Episode: 538 | Score: -193.354 | Avg score: -221.156 |\n",
"| Experiment: 1 Episode: 539 | Score: -289.264 | Avg score: -220.861 |\n",
"| Experiment: 1 Episode: 540 | Score: -237.637 | Avg score: -220.596 |\n",
"| Experiment: 1 Episode: 541 | Score: -63.515 | Avg score: -220.154 |\n",
"| Experiment: 1 Episode: 542 | Score: -142.723 | Avg score: -217.319 |\n",
"| Experiment: 1 Episode: 543 | Score: -95.756 | Avg score: -217.364 |\n",
"| Experiment: 1 Episode: 544 | Score: -229.950 | Avg score: -218.305 |\n",
"| Experiment: 1 Episode: 545 | Score: -301.391 | Avg score: -217.342 |\n",
"| Experiment: 1 Episode: 546 | Score: -104.939 | Avg score: -214.699 |\n",
"| Experiment: 1 Episode: 547 | Score: -269.154 | Avg score: -215.052 |\n",
"| Experiment: 1 Episode: 548 | Score: -271.857 | Avg score: -214.600 |\n",
"| Experiment: 1 Episode: 549 | Score: -103.181 | Avg score: -213.165 |\n",
"| Experiment: 1 Episode: 550 | Score: -233.128 | Avg score: -213.654 |\n",
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"| Experiment: 1 Episode: 554 | Score: -551.234 | Avg score: -216.479 |\n",
"| Experiment: 1 Episode: 555 | Score: -46.410 | Avg score: -216.118 |\n",
"| Experiment: 1 Episode: 556 | Score: -269.727 | Avg score: -218.180 |\n",
"| Experiment: 1 Episode: 557 | Score: -201.416 | Avg score: -219.159 |\n",
"| Experiment: 1 Episode: 558 | Score: -443.009 | Avg score: -220.219 |\n",
"| Experiment: 1 Episode: 559 | Score: -67.144 | Avg score: -217.602 |\n",
"| Experiment: 1 Episode: 560 | Score: -135.273 | Avg score: -218.034 |\n",
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"| Experiment: 1 Episode: 562 | Score: -89.618 | Avg score: -215.974 |\n",
"| Experiment: 1 Episode: 563 | Score: -137.405 | Avg score: -215.938 |\n",
"| Experiment: 1 Episode: 564 | Score: -212.764 | Avg score: -216.536 |\n",
"| Experiment: 1 Episode: 565 | Score: -46.914 | Avg score: -215.892 |\n",
"| Experiment: 1 Episode: 566 | Score: -96.538 | Avg score: -214.601 |\n",
"| Experiment: 1 Episode: 567 | Score: -137.155 | Avg score: -211.986 |\n",
"| Experiment: 1 Episode: 568 | Score: -355.177 | Avg score: -213.561 |\n",
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"| Experiment: 1 Episode: 571 | Score: -64.348 | Avg score: -215.126 |\n",
"| Experiment: 1 Episode: 572 | Score: -72.416 | Avg score: -215.420 |\n",
"| Experiment: 1 Episode: 573 | Score: -116.945 | Avg score: -213.158 |\n",
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"| Experiment: 1 Episode: 575 | Score: -67.375 | Avg score: -208.545 |\n",
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"| Experiment: 1 Episode: 578 | Score: -30.096 | Avg score: -204.552 |\n",
"| Experiment: 1 Episode: 579 | Score: -220.759 | Avg score: -203.598 |\n",
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"| Experiment: 1 Episode: 581 | Score: -90.223 | Avg score: -199.806 |\n",
"| Experiment: 1 Episode: 582 | Score: -41.684 | Avg score: -197.527 |\n",
"| Experiment: 1 Episode: 583 | Score: -41.591 | Avg score: -196.942 |\n",
"| Experiment: 1 Episode: 584 | Score: -87.401 | Avg score: -196.738 |\n",
"| Experiment: 1 Episode: 585 | Score: -121.711 | Avg score: -197.005 |\n",
"| Experiment: 1 Episode: 586 | Score: -269.853 | Avg score: -199.059 |\n",
"| Experiment: 1 Episode: 587 | Score: -343.186 | Avg score: -198.702 |\n",
"| Experiment: 1 Episode: 588 | Score: -90.410 | Avg score: -198.295 |\n",
"| Experiment: 1 Episode: 589 | Score: -115.817 | Avg score: -196.393 |\n",
"| Experiment: 1 Episode: 590 | Score: -160.738 | Avg score: -196.511 |\n",
"| Experiment: 1 Episode: 591 | Score: -193.873 | Avg score: -195.504 |\n",
"| Experiment: 1 Episode: 592 | Score: -543.278 | Avg score: -199.181 |\n",
"| Experiment: 1 Episode: 593 | Score: -239.073 | Avg score: -198.634 |\n",
"| Experiment: 1 Episode: 594 | Score: -392.755 | Avg score: -201.559 |\n",
"| Experiment: 1 Episode: 595 | Score: -83.383 | Avg score: -200.852 |\n",
"| Experiment: 1 Episode: 596 | Score: -31.586 | Avg score: -199.688 |\n",
"| Experiment: 1 Episode: 597 | Score: -393.191 | Avg score: -199.374 |\n",
"| Experiment: 1 Episode: 598 | Score: -50.804 | Avg score: -197.180 |\n",
"| Experiment: 1 Episode: 599 | Score: -86.284 | Avg score: -194.875 |\n",
"| Experiment: 1 Episode: 600 | Score: -288.385 | Avg score: -194.033 |\n",
"| Experiment: 1 Episode: 601 | Score: -67.136 | Avg score: -191.838 |\n",
"| Experiment: 1 Episode: 602 | Score: -342.961 | Avg score: -191.412 |\n",
"| Experiment: 1 Episode: 603 | Score: -287.459 | Avg score: -193.762 |\n",
"| Experiment: 1 Episode: 604 | Score: -185.584 | Avg score: -192.138 |\n",
"| Experiment: 1 Episode: 605 | Score: -208.962 | Avg score: -191.627 |\n",
"| Experiment: 1 Episode: 606 | Score: -483.145 | Avg score: -192.268 |\n",
"| Experiment: 1 Episode: 607 | Score: -184.010 | Avg score: -192.565 |\n",
"| Experiment: 1 Episode: 608 | Score: -130.709 | Avg score: -192.146 |\n",
"| Experiment: 1 Episode: 609 | Score: -254.911 | Avg score: -193.245 |\n",
"| Experiment: 1 Episode: 610 | Score: -339.790 | Avg score: -193.117 |\n",
"| Experiment: 1 Episode: 611 | Score: -323.635 | Avg score: -192.971 |\n",
"| Experiment: 1 Episode: 612 | Score: -46.094 | Avg score: -192.667 |\n",
"| Experiment: 1 Episode: 613 | Score: -473.217 | Avg score: -196.101 |\n",
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"| Experiment: 1 Episode: 617 | Score: -254.597 | Avg score: -198.061 |\n",
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"| Experiment: 1 Episode: 619 | Score: -347.463 | Avg score: -198.512 |\n",
"| Experiment: 1 Episode: 620 | Score: 20.914 | Avg score: -197.266 |\n",
"| Experiment: 1 Episode: 621 | Score: -463.242 | Avg score: -196.277 |\n",
"| Experiment: 1 Episode: 622 | Score: -141.726 | Avg score: -196.873 |\n",
"| Experiment: 1 Episode: 623 | Score: -443.494 | Avg score: -200.464 |\n",
"| Experiment: 1 Episode: 624 | Score: -408.147 | Avg score: -201.482 |\n",
"| Experiment: 1 Episode: 625 | Score: -212.021 | Avg score: -201.204 |\n",
"| Experiment: 1 Episode: 626 | Score: -145.906 | Avg score: -200.843 |\n",
"| Experiment: 1 Episode: 627 | Score: -328.861 | Avg score: -202.406 |\n",
"| Experiment: 1 Episode: 628 | Score: -449.997 | Avg score: -205.919 |\n",
"| Experiment: 1 Episode: 629 | Score: -444.043 | Avg score: -209.639 |\n",
"| Experiment: 1 Episode: 630 | Score: -109.374 | Avg score: -208.545 |\n",
"| Experiment: 1 Episode: 631 | Score: -199.730 | Avg score: -209.795 |\n",
"| Experiment: 1 Episode: 632 | Score: -78.988 | Avg score: -209.964 |\n",
"| Experiment: 1 Episode: 633 | Score: -213.785 | Avg score: -208.428 |\n",
"| Experiment: 1 Episode: 634 | Score: -360.769 | Avg score: -210.489 |\n",
"| Experiment: 1 Episode: 635 | Score: -67.023 | Avg score: -207.276 |\n",
"| Experiment: 1 Episode: 636 | Score: -95.641 | Avg score: -205.140 |\n",
"| Experiment: 1 Episode: 637 | Score: -349.309 | Avg score: -205.545 |\n",
"| Experiment: 1 Episode: 638 | Score: -132.403 | Avg score: -204.936 |\n",
"| Experiment: 1 Episode: 639 | Score: -85.542 | Avg score: -202.899 |\n",
"| Experiment: 1 Episode: 640 | Score: -103.805 | Avg score: -201.560 |\n",
"| Experiment: 1 Episode: 641 | Score: -156.942 | Avg score: -202.495 |\n",
"| Experiment: 1 Episode: 642 | Score: -54.330 | Avg score: -201.611 |\n",
"| Experiment: 1 Episode: 643 | Score: -379.637 | Avg score: -204.449 |\n",
"| Experiment: 1 Episode: 644 | Score: -237.061 | Avg score: -204.521 |\n",
"| Experiment: 1 Episode: 645 | Score: -105.995 | Avg score: -202.567 |\n",
"| Experiment: 1 Episode: 646 | Score: -242.682 | Avg score: -203.944 |\n",
"| Experiment: 1 Episode: 647 | Score: -64.569 | Avg score: -201.898 |\n",
"| Experiment: 1 Episode: 648 | Score: -374.810 | Avg score: -202.928 |\n",
"| Experiment: 1 Episode: 649 | Score: -187.837 | Avg score: -203.774 |\n",
"| Experiment: 1 Episode: 650 | Score: -74.628 | Avg score: -202.189 |\n",
"| Experiment: 1 Episode: 651 | Score: -419.994 | Avg score: -206.094 |\n",
"| Experiment: 1 Episode: 652 | Score: -183.064 | Avg score: -203.890 |\n",
"| Experiment: 1 Episode: 653 | Score: -68.036 | Avg score: -204.196 |\n",
"| Experiment: 1 Episode: 654 | Score: -93.597 | Avg score: -199.620 |\n",
"| Experiment: 1 Episode: 655 | Score: -426.957 | Avg score: -203.425 |\n",
"| Experiment: 1 Episode: 656 | Score: -61.362 | Avg score: -201.341 |\n",
"| Experiment: 1 Episode: 657 | Score: -133.947 | Avg score: -200.667 |\n",
"| Experiment: 1 Episode: 658 | Score: -184.787 | Avg score: -198.084 |\n",
"| Experiment: 1 Episode: 659 | Score: -276.755 | Avg score: -200.181 |\n",
"| Experiment: 1 Episode: 660 | Score: -127.206 | Avg score: -200.100 |\n",
"| Experiment: 1 Episode: 661 | Score: -217.820 | Avg score: -201.246 |\n",
"| Experiment: 1 Episode: 662 | Score: -59.681 | Avg score: -200.946 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 Episode: 663 | Score: -201.943 | Avg score: -201.592 |\n",
"| Experiment: 1 Episode: 664 | Score: -163.176 | Avg score: -201.096 |\n",
"| Experiment: 1 Episode: 665 | Score: -208.023 | Avg score: -202.707 |\n",
"| Experiment: 1 Episode: 666 | Score: -171.024 | Avg score: -203.452 |\n",
"| Experiment: 1 Episode: 667 | Score: -41.003 | Avg score: -202.490 |\n",
"| Experiment: 1 Episode: 668 | Score: -87.337 | Avg score: -199.812 |\n",
"| Experiment: 1 Episode: 669 | Score: -402.887 | Avg score: -202.218 |\n",
"| Experiment: 1 Episode: 670 | Score: -103.201 | Avg score: -199.510 |\n",
"| Experiment: 1 Episode: 671 | Score: -26.444 | Avg score: -199.131 |\n",
"| Experiment: 1 Episode: 672 | Score: -107.652 | Avg score: -199.483 |\n",
"| Experiment: 1 Episode: 673 | Score: -172.733 | Avg score: -200.041 |\n",
"| Experiment: 1 Episode: 674 | Score: -150.697 | Avg score: -200.433 |\n",
"| Experiment: 1 Episode: 675 | Score: -239.960 | Avg score: -202.159 |\n",
"| Experiment: 1 Episode: 676 | Score: -341.061 | Avg score: -204.805 |\n",
"| Experiment: 1 Episode: 677 | Score: -127.957 | Avg score: -203.645 |\n",
"| Experiment: 1 Episode: 678 | Score: -144.174 | Avg score: -204.786 |\n",
"| Experiment: 1 Episode: 679 | Score: -377.897 | Avg score: -206.357 |\n",
"| Experiment: 1 Episode: 680 | Score: -299.813 | Avg score: -208.849 |\n",
"| Experiment: 1 Episode: 681 | Score: -164.835 | Avg score: -209.595 |\n",
"| Experiment: 1 Episode: 682 | Score: -238.363 | Avg score: -211.562 |\n",
"| Experiment: 1 Episode: 683 | Score: -368.493 | Avg score: -214.831 |\n",
"| Experiment: 1 Episode: 684 | Score: -118.078 | Avg score: -215.138 |\n",
"| Experiment: 1 Episode: 685 | Score: -449.591 | Avg score: -218.416 |\n",
"| Experiment: 1 Episode: 686 | Score: -131.644 | Avg score: -217.034 |\n",
"| Experiment: 1 Episode: 687 | Score: -206.532 | Avg score: -215.668 |\n",
"| Experiment: 1 Episode: 688 | Score: -76.208 | Avg score: -215.526 |\n",
"| Experiment: 1 Episode: 689 | Score: -150.886 | Avg score: -215.876 |\n",
"| Experiment: 1 Episode: 690 | Score: -455.941 | Avg score: -218.829 |\n",
"| Experiment: 1 Episode: 691 | Score: -58.038 | Avg score: -217.470 |\n",
"| Experiment: 1 Episode: 692 | Score: -19.948 | Avg score: -212.237 |\n",
"| Experiment: 1 Episode: 693 | Score: -112.837 | Avg score: -210.975 |\n",
"| Experiment: 1 Episode: 694 | Score: -328.916 | Avg score: -210.336 |\n",
"| Experiment: 1 Episode: 695 | Score: -138.309 | Avg score: -210.885 |\n",
"| Experiment: 1 Episode: 696 | Score: -370.097 | Avg score: -214.270 |\n",
"| Experiment: 1 Episode: 697 | Score: -115.832 | Avg score: -211.497 |\n",
"| Experiment: 1 Episode: 698 | Score: -282.698 | Avg score: -213.816 |\n",
"| Experiment: 1 Episode: 699 | Score: -93.010 | Avg score: -213.883 |\n",
"| Experiment: 1 Episode: 700 | Score: -60.065 | Avg score: -211.600 |\n",
"| Experiment: 1 Episode: 701 | Score: -86.409 | Avg score: -211.793 |\n",
"| Experiment: 1 Episode: 702 | Score: -446.851 | Avg score: -212.832 |\n",
"| Experiment: 1 Episode: 703 | Score: -64.296 | Avg score: -210.600 |\n",
"| Experiment: 1 Episode: 704 | Score: -95.134 | Avg score: -209.695 |\n",
"| Experiment: 1 Episode: 705 | Score: -70.614 | Avg score: -208.312 |\n",
"| Experiment: 1 Episode: 706 | Score: -282.577 | Avg score: -206.306 |\n",
"| Experiment: 1 Episode: 707 | Score: -435.100 | Avg score: -208.817 |\n",
"| Experiment: 1 Episode: 708 | Score: -91.096 | Avg score: -208.421 |\n",
"| Experiment: 1 Episode: 709 | Score: -144.560 | Avg score: -207.318 |\n",
"| Experiment: 1 Episode: 710 | Score: -423.217 | Avg score: -208.152 |\n",
"| Experiment: 1 Episode: 711 | Score: -89.869 | Avg score: -205.814 |\n",
"| Experiment: 1 Episode: 712 | Score: -318.853 | Avg score: -208.542 |\n",
"| Experiment: 1 Episode: 713 | Score: -101.070 | Avg score: -204.820 |\n",
"| Experiment: 1 Episode: 714 | Score: -106.092 | Avg score: -203.381 |\n",
"| Experiment: 1 Episode: 715 | Score: -324.026 | Avg score: -205.319 |\n",
"| Experiment: 1 Episode: 716 | Score: -108.314 | Avg score: -205.266 |\n",
"| Experiment: 1 Episode: 717 | Score: -96.444 | Avg score: -203.685 |\n",
"| Experiment: 1 Episode: 718 | Score: -76.085 | Avg score: -199.600 |\n",
"| Experiment: 1 Episode: 719 | Score: -108.552 | Avg score: -197.211 |\n",
"| Experiment: 1 Episode: 720 | Score: -204.683 | Avg score: -199.466 |\n",
"| Experiment: 1 Episode: 721 | Score: -352.052 | Avg score: -198.355 |\n",
"| Experiment: 1 Episode: 722 | Score: -225.297 | Avg score: -199.190 |\n",
"| Experiment: 1 Episode: 723 | Score: -69.162 | Avg score: -195.447 |\n",
"| Experiment: 1 Episode: 724 | Score: -199.120 | Avg score: -193.357 |\n",
"| Experiment: 1 Episode: 725 | Score: -102.716 | Avg score: -192.264 |\n",
"| Experiment: 1 Episode: 726 | Score: -338.098 | Avg score: -194.186 |\n",
"| Experiment: 1 Episode: 727 | Score: -394.071 | Avg score: -194.838 |\n",
"| Experiment: 1 Episode: 728 | Score: -128.556 | Avg score: -191.623 |\n",
"| Experiment: 1 Episode: 729 | Score: -246.205 | Avg score: -189.645 |\n",
"| Experiment: 1 Episode: 730 | Score: -282.952 | Avg score: -191.381 |\n",
"| Experiment: 1 Episode: 731 | Score: -280.591 | Avg score: -192.189 |\n",
"| Experiment: 1 Episode: 732 | Score: -359.926 | Avg score: -194.999 |\n",
"| Experiment: 1 Episode: 733 | Score: -151.827 | Avg score: -194.379 |\n",
"| Experiment: 1 Episode: 734 | Score: -392.490 | Avg score: -194.696 |\n",
"| Experiment: 1 Episode: 735 | Score: -290.611 | Avg score: -196.932 |\n",
"| Experiment: 1 Episode: 736 | Score: -79.013 | Avg score: -196.766 |\n",
"| Experiment: 1 Episode: 737 | Score: -352.188 | Avg score: -196.795 |\n",
"| Experiment: 1 Episode: 738 | Score: -303.846 | Avg score: -198.509 |\n",
"| Experiment: 1 Episode: 739 | Score: -83.870 | Avg score: -198.492 |\n",
"| Experiment: 1 Episode: 740 | Score: -163.669 | Avg score: -199.091 |\n",
"| Experiment: 1 Episode: 741 | Score: -358.140 | Avg score: -201.103 |\n",
"| Experiment: 1 Episode: 742 | Score: -39.404 | Avg score: -200.954 |\n",
"| Experiment: 1 Episode: 743 | Score: -59.673 | Avg score: -197.754 |\n",
"| Experiment: 1 Episode: 744 | Score: -338.751 | Avg score: -198.771 |\n",
"| Experiment: 1 Episode: 745 | Score: -251.942 | Avg score: -200.230 |\n",
"| Experiment: 1 Episode: 746 | Score: -195.095 | Avg score: -199.755 |\n",
"| Experiment: 1 Episode: 747 | Score: -271.763 | Avg score: -201.827 |\n",
"| Experiment: 1 Episode: 748 | Score: -458.952 | Avg score: -202.668 |\n",
"| Experiment: 1 Episode: 749 | Score: -355.068 | Avg score: -204.340 |\n",
"| Experiment: 1 Episode: 750 | Score: -151.007 | Avg score: -205.104 |\n",
"| Experiment: 1 Episode: 751 | Score: -219.316 | Avg score: -203.097 |\n",
"| Experiment: 1 Episode: 752 | Score: -327.620 | Avg score: -204.543 |\n",
"| Experiment: 1 Episode: 753 | Score: -334.207 | Avg score: -207.205 |\n",
"| Experiment: 1 Episode: 754 | Score: -176.805 | Avg score: -208.037 |\n",
"| Experiment: 1 Episode: 755 | Score: -263.349 | Avg score: -206.401 |\n",
"| Experiment: 1 Episode: 756 | Score: -280.731 | Avg score: -208.594 |\n",
"| Experiment: 1 Episode: 757 | Score: -37.356 | Avg score: -207.628 |\n",
"| Experiment: 1 Episode: 758 | Score: -217.897 | Avg score: -207.959 |\n",
"| Experiment: 1 Episode: 759 | Score: -265.161 | Avg score: -207.843 |\n",
"| Experiment: 1 Episode: 760 | Score: -113.526 | Avg score: -207.707 |\n",
"| Experiment: 1 Episode: 761 | Score: -144.866 | Avg score: -206.977 |\n",
"| Experiment: 1 Episode: 762 | Score: -90.939 | Avg score: -207.290 |\n",
"| Experiment: 1 Episode: 763 | Score: -87.824 | Avg score: -206.149 |\n",
"| Experiment: 1 Episode: 764 | Score: -413.152 | Avg score: -208.648 |\n",
"| Experiment: 1 Episode: 765 | Score: -195.949 | Avg score: -208.528 |\n",
"| Experiment: 1 Episode: 766 | Score: -136.867 | Avg score: -208.186 |\n",
"| Experiment: 1 Episode: 767 | Score: -494.875 | Avg score: -212.725 |\n",
"| Experiment: 1 Episode: 768 | Score: -310.815 | Avg score: -214.960 |\n",
"| Experiment: 1 Episode: 769 | Score: -176.020 | Avg score: -212.691 |\n",
"| Experiment: 1 Episode: 770 | Score: -92.807 | Avg score: -212.587 |\n",
"| Experiment: 1 Episode: 771 | Score: -137.722 | Avg score: -213.700 |\n",
"| Experiment: 1 Episode: 772 | Score: -32.151 | Avg score: -212.945 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 Episode: 773 | Score: -125.619 | Avg score: -212.474 |\n",
"| Experiment: 1 Episode: 774 | Score: -120.092 | Avg score: -212.167 |\n",
"| Experiment: 1 Episode: 775 | Score: -296.630 | Avg score: -212.734 |\n",
"| Experiment: 1 Episode: 776 | Score: -34.122 | Avg score: -209.665 |\n",
"| Experiment: 1 Episode: 777 | Score: -300.533 | Avg score: -211.391 |\n",
"| Experiment: 1 Episode: 778 | Score: -175.842 | Avg score: -211.707 |\n",
"| Experiment: 1 Episode: 779 | Score: -283.150 | Avg score: -210.760 |\n",
"| Experiment: 1 Episode: 780 | Score: -324.337 | Avg score: -211.005 |\n",
"| Experiment: 1 Episode: 781 | Score: -210.188 | Avg score: -211.459 |\n",
"| Experiment: 1 Episode: 782 | Score: -308.913 | Avg score: -212.164 |\n",
"| Experiment: 1 Episode: 783 | Score: -179.963 | Avg score: -210.279 |\n",
"| Experiment: 1 Episode: 784 | Score: -76.235 | Avg score: -209.860 |\n",
"| Experiment: 1 Episode: 785 | Score: -77.887 | Avg score: -206.143 |\n",
"| Experiment: 1 Episode: 786 | Score: -433.860 | Avg score: -209.165 |\n",
"| Experiment: 1 Episode: 787 | Score: -524.907 | Avg score: -212.349 |\n",
"| Experiment: 1 Episode: 788 | Score: -277.366 | Avg score: -214.361 |\n",
"| Experiment: 1 Episode: 789 | Score: -216.940 | Avg score: -215.021 |\n",
"| Experiment: 1 Episode: 790 | Score: -348.844 | Avg score: -213.950 |\n",
"| Experiment: 1 Episode: 791 | Score: -304.962 | Avg score: -216.420 |\n",
"| Experiment: 1 Episode: 792 | Score: -366.890 | Avg score: -219.889 |\n",
"| Experiment: 1 Episode: 793 | Score: -241.873 | Avg score: -221.179 |\n",
"| Experiment: 1 Episode: 794 | Score: -208.442 | Avg score: -219.975 |\n",
"| Experiment: 1 Episode: 795 | Score: -119.605 | Avg score: -219.788 |\n",
"| Experiment: 1 Episode: 796 | Score: -121.768 | Avg score: -217.304 |\n",
"| Experiment: 1 Episode: 797 | Score: -438.470 | Avg score: -220.531 |\n",
"| Experiment: 1 Episode: 798 | Score: -261.728 | Avg score: -220.321 |\n",
"| Experiment: 1 Episode: 799 | Score: -63.502 | Avg score: -220.026 |\n",
"| Experiment: 1 Episode: 800 | Score: -8.079 | Avg score: -219.506 |\n",
"| Experiment: 1 Episode: 801 | Score: -40.792 | Avg score: -219.050 |\n",
"| Experiment: 1 Episode: 802 | Score: -418.713 | Avg score: -218.768 |\n",
"| Experiment: 1 Episode: 803 | Score: -269.577 | Avg score: -220.821 |\n",
"| Experiment: 1 Episode: 804 | Score: -310.675 | Avg score: -222.977 |\n",
"| Experiment: 1 Episode: 805 | Score: -421.324 | Avg score: -226.484 |\n",
"| Experiment: 1 Episode: 806 | Score: -125.728 | Avg score: -224.915 |\n",
"| Experiment: 1 Episode: 807 | Score: -170.237 | Avg score: -222.267 |\n",
"| Experiment: 1 Episode: 808 | Score: -98.337 | Avg score: -222.339 |\n",
"| Experiment: 1 Episode: 809 | Score: -276.617 | Avg score: -223.660 |\n",
"| Experiment: 1 Episode: 810 | Score: -70.461 | Avg score: -220.132 |\n",
"| Experiment: 1 Episode: 811 | Score: -56.815 | Avg score: -219.801 |\n",
"| Experiment: 1 Episode: 812 | Score: -55.823 | Avg score: -217.171 |\n",
"| Experiment: 1 Episode: 813 | Score: -19.543 | Avg score: -216.356 |\n",
"| Experiment: 1 Episode: 814 | Score: -405.322 | Avg score: -219.348 |\n",
"| Experiment: 1 Episode: 815 | Score: -54.265 | Avg score: -216.651 |\n",
"| Experiment: 1 Episode: 816 | Score: -54.037 | Avg score: -216.108 |\n",
"| Experiment: 1 Episode: 817 | Score: -70.417 | Avg score: -215.848 |\n",
"| Experiment: 1 Episode: 818 | Score: -295.779 | Avg score: -218.045 |\n",
"| Experiment: 1 Episode: 819 | Score: 14.935 | Avg score: -216.810 |\n",
"| Experiment: 1 Episode: 820 | Score: -303.264 | Avg score: -217.795 |\n",
"| Experiment: 1 Episode: 821 | Score: -362.636 | Avg score: -217.901 |\n",
"| Experiment: 1 Episode: 822 | Score: -124.018 | Avg score: -216.889 |\n",
"| Experiment: 1 Episode: 823 | Score: -205.185 | Avg score: -218.249 |\n",
"| Experiment: 1 Episode: 824 | Score: -148.337 | Avg score: -217.741 |\n",
"| Experiment: 1 Episode: 825 | Score: -355.038 | Avg score: -220.264 |\n",
"| Experiment: 1 Episode: 826 | Score: -296.468 | Avg score: -219.848 |\n",
"| Experiment: 1 Episode: 827 | Score: -214.967 | Avg score: -218.057 |\n",
"| Experiment: 1 Episode: 828 | Score: -238.539 | Avg score: -219.157 |\n",
"| Experiment: 1 Episode: 829 | Score: -156.060 | Avg score: -218.255 |\n",
"| Experiment: 1 Episode: 830 | Score: -533.642 | Avg score: -220.762 |\n",
"| Experiment: 1 Episode: 831 | Score: -61.704 | Avg score: -218.573 |\n",
"| Experiment: 1 Episode: 832 | Score: -67.333 | Avg score: -215.647 |\n",
"| Experiment: 1 Episode: 833 | Score: -81.921 | Avg score: -214.948 |\n",
"| Experiment: 1 Episode: 834 | Score: -300.628 | Avg score: -214.030 |\n",
"| Experiment: 1 Episode: 835 | Score: -355.347 | Avg score: -214.677 |\n",
"| Experiment: 1 Episode: 836 | Score: -65.866 | Avg score: -214.545 |\n",
"| Experiment: 1 Episode: 837 | Score: -372.380 | Avg score: -214.747 |\n",
"| Experiment: 1 Episode: 838 | Score: -415.852 | Avg score: -215.867 |\n",
"| Experiment: 1 Episode: 839 | Score: -43.648 | Avg score: -215.465 |\n",
"| Experiment: 1 Episode: 840 | Score: -319.550 | Avg score: -217.024 |\n",
"| Experiment: 1 Episode: 841 | Score: -221.320 | Avg score: -215.656 |\n",
"| Experiment: 1 Episode: 842 | Score: -127.792 | Avg score: -216.540 |\n",
"| Experiment: 1 Episode: 843 | Score: -57.962 | Avg score: -216.523 |\n",
"| Experiment: 1 Episode: 844 | Score: -87.847 | Avg score: -214.014 |\n",
"| Experiment: 1 Episode: 845 | Score: -63.012 | Avg score: -212.124 |\n",
"| Experiment: 1 Episode: 846 | Score: -106.584 | Avg score: -211.239 |\n",
"| Experiment: 1 Episode: 847 | Score: -225.199 | Avg score: -210.774 |\n",
"| Experiment: 1 Episode: 848 | Score: -378.216 | Avg score: -209.966 |\n",
"| Experiment: 1 Episode: 849 | Score: -133.736 | Avg score: -207.753 |\n",
"| Experiment: 1 Episode: 850 | Score: -403.078 | Avg score: -210.274 |\n",
"| Experiment: 1 Episode: 851 | Score: -247.229 | Avg score: -210.553 |\n",
"| Experiment: 1 Episode: 852 | Score: -51.828 | Avg score: -207.795 |\n",
"| Experiment: 1 Episode: 853 | Score: -91.037 | Avg score: -205.363 |\n",
"| Experiment: 1 Episode: 854 | Score: -220.527 | Avg score: -205.800 |\n",
"| Experiment: 1 Episode: 855 | Score: -134.857 | Avg score: -204.515 |\n",
"| Experiment: 1 Episode: 856 | Score: -97.401 | Avg score: -202.682 |\n",
"| Experiment: 1 Episode: 857 | Score: -232.652 | Avg score: -204.635 |\n",
"| Experiment: 1 Episode: 858 | Score: -154.831 | Avg score: -204.004 |\n",
"| Experiment: 1 Episode: 859 | Score: -174.666 | Avg score: -203.099 |\n",
"| Experiment: 1 Episode: 860 | Score: -245.321 | Avg score: -204.417 |\n",
"| Experiment: 1 Episode: 861 | Score: -61.584 | Avg score: -203.585 |\n",
"| Experiment: 1 Episode: 862 | Score: -57.586 | Avg score: -203.251 |\n",
"| Experiment: 1 Episode: 863 | Score: -147.718 | Avg score: -203.850 |\n",
"| Experiment: 1 Episode: 864 | Score: -296.198 | Avg score: -202.680 |\n",
"| Experiment: 1 Episode: 865 | Score: -113.172 | Avg score: -201.853 |\n",
"| Experiment: 1 Episode: 866 | Score: -118.495 | Avg score: -201.669 |\n",
"| Experiment: 1 Episode: 867 | Score: -90.824 | Avg score: -197.628 |\n",
"| Experiment: 1 Episode: 868 | Score: -339.320 | Avg score: -197.913 |\n",
"| Experiment: 1 Episode: 869 | Score: -376.680 | Avg score: -199.920 |\n",
"| Experiment: 1 Episode: 870 | Score: -231.988 | Avg score: -201.312 |\n",
"| Experiment: 1 Episode: 871 | Score: -60.151 | Avg score: -200.536 |\n",
"| Experiment: 1 Episode: 872 | Score: -350.341 | Avg score: -203.718 |\n",
"| Experiment: 1 Episode: 873 | Score: -309.136 | Avg score: -205.553 |\n",
"| Experiment: 1 Episode: 874 | Score: -153.172 | Avg score: -205.884 |\n",
"| Experiment: 1 Episode: 875 | Score: -334.896 | Avg score: -206.267 |\n",
"| Experiment: 1 Episode: 876 | Score: -215.247 | Avg score: -208.078 |\n",
"| Experiment: 1 Episode: 877 | Score: -79.349 | Avg score: -205.866 |\n",
"| Experiment: 1 Episode: 878 | Score: -88.533 | Avg score: -204.993 |\n",
"| Experiment: 1 Episode: 879 | Score: -168.179 | Avg score: -203.843 |\n",
"| Experiment: 1 Episode: 880 | Score: -81.287 | Avg score: -201.413 |\n",
"| Experiment: 1 Episode: 881 | Score: -32.763 | Avg score: -199.639 |\n",
"| Experiment: 1 Episode: 882 | Score: -87.158 | Avg score: -197.421 |\n",
"| Experiment: 1 Episode: 883 | Score: -169.125 | Avg score: -197.313 |\n",
"| Experiment: 1 Episode: 884 | Score: -124.148 | Avg score: -197.792 |\n",
"| Experiment: 1 Episode: 885 | Score: -308.442 | Avg score: -200.097 |\n",
"| Experiment: 1 Episode: 886 | Score: -374.846 | Avg score: -199.507 |\n",
"| Experiment: 1 Episode: 887 | Score: -51.280 | Avg score: -194.771 |\n",
"| Experiment: 1 Episode: 888 | Score: -47.180 | Avg score: -192.469 |\n",
"| Experiment: 1 Episode: 889 | Score: -393.955 | Avg score: -194.239 |\n",
"| Experiment: 1 Episode: 890 | Score: -92.298 | Avg score: -191.674 |\n",
"| Experiment: 1 Episode: 891 | Score: -124.210 | Avg score: -189.866 |\n",
"| Experiment: 1 Episode: 892 | Score: -75.221 | Avg score: -186.949 |\n",
"| Experiment: 1 Episode: 893 | Score: -66.866 | Avg score: -185.199 |\n",
"| Experiment: 1 Episode: 894 | Score: -112.873 | Avg score: -184.244 |\n",
"| Experiment: 1 Episode: 895 | Score: -336.004 | Avg score: -186.408 |\n",
"| Experiment: 1 Episode: 896 | Score: -122.613 | Avg score: -186.416 |\n",
"| Experiment: 1 Episode: 897 | Score: -221.912 | Avg score: -184.251 |\n",
"| Experiment: 1 Episode: 898 | Score: -74.373 | Avg score: -182.377 |\n",
"| Experiment: 1 Episode: 899 | Score: -131.620 | Avg score: -183.058 |\n",
"| Experiment: 1 Episode: 900 | Score: -181.365 | Avg score: -184.791 |\n",
"| Experiment: 1 Episode: 901 | Score: -98.524 | Avg score: -185.368 |\n",
"| Experiment: 1 Episode: 902 | Score: -303.602 | Avg score: -184.217 |\n",
"| Experiment: 1 Episode: 903 | Score: -364.990 | Avg score: -185.171 |\n",
"| Experiment: 1 Episode: 904 | Score: -337.137 | Avg score: -185.436 |\n",
"| Experiment: 1 Episode: 905 | Score: -41.370 | Avg score: -181.636 |\n",
"| Experiment: 1 Episode: 906 | Score: -295.551 | Avg score: -183.335 |\n",
"| Experiment: 1 Episode: 907 | Score: -90.419 | Avg score: -182.537 |\n",
"| Experiment: 1 Episode: 908 | Score: -342.589 | Avg score: -184.979 |\n",
"| Experiment: 1 Episode: 909 | Score: -335.857 | Avg score: -185.571 |\n",
"| Experiment: 1 Episode: 910 | Score: -395.010 | Avg score: -188.817 |\n",
"| Experiment: 1 Episode: 911 | Score: -91.278 | Avg score: -189.162 |\n",
"| Experiment: 1 Episode: 912 | Score: -95.346 | Avg score: -189.557 |\n",
"| Experiment: 1 Episode: 913 | Score: -368.433 | Avg score: -193.046 |\n",
"| Experiment: 1 Episode: 914 | Score: -296.148 | Avg score: -191.954 |\n",
"| Experiment: 1 Episode: 915 | Score: -387.085 | Avg score: -195.282 |\n",
"| Experiment: 1 Episode: 916 | Score: -120.568 | Avg score: -195.947 |\n",
"| Experiment: 1 Episode: 917 | Score: -136.292 | Avg score: -196.606 |\n",
"| Experiment: 1 Episode: 918 | Score: -236.067 | Avg score: -196.009 |\n",
"| Experiment: 1 Episode: 919 | Score: -412.716 | Avg score: -200.286 |\n",
"| Experiment: 1 Episode: 920 | Score: -83.000 | Avg score: -198.083 |\n",
"| Experiment: 1 Episode: 921 | Score: -283.337 | Avg score: -197.290 |\n",
"| Experiment: 1 Episode: 922 | Score: -276.780 | Avg score: -198.818 |\n",
"| Experiment: 1 Episode: 923 | Score: -288.917 | Avg score: -199.655 |\n",
"| Experiment: 1 Episode: 924 | Score: -120.594 | Avg score: -199.377 |\n",
"| Experiment: 1 Episode: 925 | Score: -60.711 | Avg score: -196.434 |\n",
"| Experiment: 1 Episode: 926 | Score: -250.052 | Avg score: -195.970 |\n",
"| Experiment: 1 Episode: 927 | Score: -159.714 | Avg score: -195.418 |\n",
"| Experiment: 1 Episode: 928 | Score: -180.653 | Avg score: -194.839 |\n",
"| Experiment: 1 Episode: 929 | Score: -301.047 | Avg score: -196.289 |\n",
"| Experiment: 1 Episode: 930 | Score: -344.203 | Avg score: -194.394 |\n",
"| Experiment: 1 Episode: 931 | Score: -499.521 | Avg score: -198.772 |\n",
"| Experiment: 1 Episode: 932 | Score: -167.773 | Avg score: -199.777 |\n",
"| Experiment: 1 Episode: 933 | Score: -304.611 | Avg score: -202.004 |\n",
"| Experiment: 1 Episode: 934 | Score: -341.387 | Avg score: -202.411 |\n",
"| Experiment: 1 Episode: 935 | Score: -215.598 | Avg score: -201.014 |\n",
"| Experiment: 1 Episode: 936 | Score: -46.883 | Avg score: -200.824 |\n",
"| Experiment: 1 Episode: 937 | Score: -64.050 | Avg score: -197.741 |\n",
"| Experiment: 1 Episode: 938 | Score: -298.740 | Avg score: -196.569 |\n",
"| Experiment: 1 Episode: 939 | Score: -300.756 | Avg score: -199.141 |\n",
"| Experiment: 1 Episode: 940 | Score: -220.667 | Avg score: -198.152 |\n",
"| Experiment: 1 Episode: 941 | Score: -107.776 | Avg score: -197.016 |\n",
"| Experiment: 1 Episode: 942 | Score: -202.256 | Avg score: -197.761 |\n",
"| Experiment: 1 Episode: 943 | Score: -301.976 | Avg score: -200.201 |\n",
"| Experiment: 1 Episode: 944 | Score: -329.236 | Avg score: -202.615 |\n",
"| Experiment: 1 Episode: 945 | Score: -226.544 | Avg score: -204.250 |\n",
"| Experiment: 1 Episode: 946 | Score: -144.395 | Avg score: -204.628 |\n",
"| Experiment: 1 Episode: 947 | Score: -351.729 | Avg score: -205.894 |\n",
"| Experiment: 1 Episode: 948 | Score: -252.459 | Avg score: -204.636 |\n",
"| Experiment: 1 Episode: 949 | Score: -99.797 | Avg score: -204.297 |\n",
"| Experiment: 1 Episode: 950 | Score: -399.774 | Avg score: -204.264 |\n",
"| Experiment: 1 Episode: 951 | Score: -77.493 | Avg score: -202.566 |\n",
"| Experiment: 1 Episode: 952 | Score: -245.121 | Avg score: -204.499 |\n",
"| Experiment: 1 Episode: 953 | Score: -313.979 | Avg score: -206.729 |\n",
"| Experiment: 1 Episode: 954 | Score: -197.354 | Avg score: -206.497 |\n",
"| Experiment: 1 Episode: 955 | Score: -144.521 | Avg score: -206.594 |\n",
"| Experiment: 1 Episode: 956 | Score: -318.668 | Avg score: -208.806 |\n",
"| Experiment: 1 Episode: 957 | Score: -292.117 | Avg score: -209.401 |\n",
"| Experiment: 1 Episode: 958 | Score: -119.338 | Avg score: -209.046 |\n",
"| Experiment: 1 Episode: 959 | Score: -442.150 | Avg score: -211.721 |\n",
"| Experiment: 1 Episode: 960 | Score: -157.895 | Avg score: -210.847 |\n",
"| Experiment: 1 Episode: 961 | Score: -313.515 | Avg score: -213.366 |\n",
"| Experiment: 1 Episode: 962 | Score: -406.156 | Avg score: -216.852 |\n",
"| Experiment: 1 Episode: 963 | Score: -320.288 | Avg score: -218.577 |\n",
"| Experiment: 1 Episode: 964 | Score: -212.409 | Avg score: -217.739 |\n",
"| Experiment: 1 Episode: 965 | Score: -385.189 | Avg score: -220.460 |\n",
"| Experiment: 1 Episode: 966 | Score: -101.996 | Avg score: -220.295 |\n",
"| Experiment: 1 Episode: 967 | Score: -130.373 | Avg score: -220.690 |\n",
"| Experiment: 1 Episode: 968 | Score: -156.518 | Avg score: -218.862 |\n",
"| Experiment: 1 Episode: 969 | Score: -92.032 | Avg score: -216.016 |\n",
"| Experiment: 1 Episode: 970 | Score: -117.836 | Avg score: -214.874 |\n",
"| Experiment: 1 Episode: 971 | Score: -81.501 | Avg score: -215.088 |\n",
"| Experiment: 1 Episode: 972 | Score: -238.911 | Avg score: -213.973 |\n",
"| Experiment: 1 Episode: 973 | Score: -123.886 | Avg score: -212.121 |\n",
"| Experiment: 1 Episode: 974 | Score: -84.341 | Avg score: -211.432 |\n",
"| Experiment: 1 Episode: 975 | Score: -351.221 | Avg score: -211.596 |\n",
"| Experiment: 1 Episode: 976 | Score: -317.272 | Avg score: -212.616 |\n",
"| Experiment: 1 Episode: 977 | Score: -56.096 | Avg score: -212.383 |\n",
"| Experiment: 1 Episode: 978 | Score: -285.386 | Avg score: -214.352 |\n",
"| Experiment: 1 Episode: 979 | Score: -295.818 | Avg score: -215.628 |\n",
"| Experiment: 1 Episode: 980 | Score: -188.335 | Avg score: -216.699 |\n",
"| Experiment: 1 Episode: 981 | Score: -81.046 | Avg score: -217.182 |\n",
"| Experiment: 1 Episode: 982 | Score: -90.668 | Avg score: -217.217 |\n",
"| Experiment: 1 Episode: 983 | Score: -97.966 | Avg score: -216.505 |\n",
"| Experiment: 1 Episode: 984 | Score: -361.206 | Avg score: -218.876 |\n",
"| Experiment: 1 Episode: 985 | Score: -191.498 | Avg score: -217.706 |\n",
"| Experiment: 1 Episode: 986 | Score: -259.618 | Avg score: -216.554 |\n",
"| Experiment: 1 Episode: 987 | Score: -285.936 | Avg score: -218.901 |\n",
"| Experiment: 1 Episode: 988 | Score: -181.505 | Avg score: -220.244 |\n",
"| Experiment: 1 Episode: 989 | Score: -342.839 | Avg score: -219.733 |\n",
"| Experiment: 1 Episode: 990 | Score: -369.125 | Avg score: -222.501 |\n",
"| Experiment: 1 Episode: 991 | Score: -186.178 | Avg score: -223.121 |\n",
"| Experiment: 1 Episode: 992 | Score: -127.150 | Avg score: -223.640 |\n",
"| Experiment: 1 Episode: 993 | Score: -338.544 | Avg score: -226.357 |\n",
"| Experiment: 1 Episode: 994 | Score: -66.033 | Avg score: -225.888 |\n",
"| Experiment: 1 Episode: 995 | Score: -36.445 | Avg score: -222.893 |\n",
"| Experiment: 1 Episode: 996 | Score: -174.095 | Avg score: -223.408 |\n",
"| Experiment: 1 Episode: 997 | Score: -216.890 | Avg score: -223.357 |\n",
"| Experiment: 1 Episode: 998 | Score: -127.628 | Avg score: -223.890 |\n",
"| Experiment: 1 Episode: 999 | Score: -165.855 | Avg score: -224.232 |\n",
"| Experiment: 1 Episode: 1000 | Score: -19.679 | Avg score: -222.615 |\n",
"| Experiment: 1 Episode: 1001 | Score: -235.114 | Avg score: -223.981 |\n",
"| Experiment: 1 Episode: 1002 | Score: -98.722 | Avg score: -221.932 |\n",
"| Experiment: 1 Episode: 1003 | Score: -86.604 | Avg score: -219.149 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 Episode: 1004 | Score: -440.761 | Avg score: -220.185 |\n",
"| Experiment: 1 Episode: 1005 | Score: -52.650 | Avg score: -220.298 |\n",
"| Experiment: 1 Episode: 1006 | Score: -58.394 | Avg score: -217.926 |\n",
"| Experiment: 1 Episode: 1007 | Score: -310.077 | Avg score: -220.123 |\n",
"| Experiment: 1 Episode: 1008 | Score: -89.976 | Avg score: -217.596 |\n",
"| Experiment: 1 Episode: 1009 | Score: -265.730 | Avg score: -216.895 |\n",
"| Experiment: 1 Episode: 1010 | Score: -428.697 | Avg score: -217.232 |\n",
"| Experiment: 1 Episode: 1011 | Score: -7.903 | Avg score: -216.398 |\n",
"| Experiment: 1 Episode: 1012 | Score: -105.688 | Avg score: -216.502 |\n",
"| Experiment: 1 Episode: 1013 | Score: -76.949 | Avg score: -213.587 |\n",
"| Experiment: 1 Episode: 1014 | Score: -69.541 | Avg score: -211.321 |\n",
"| Experiment: 1 Episode: 1015 | Score: -392.679 | Avg score: -211.377 |\n",
"| Experiment: 1 Episode: 1016 | Score: -334.278 | Avg score: -213.514 |\n",
"| Experiment: 1 Episode: 1017 | Score: -150.746 | Avg score: -213.658 |\n",
"| Experiment: 1 Episode: 1018 | Score: -203.597 | Avg score: -213.334 |\n",
"| Experiment: 1 Episode: 1019 | Score: -93.630 | Avg score: -210.143 |\n",
"| Experiment: 1 Episode: 1020 | Score: -71.400 | Avg score: -210.027 |\n",
"| Experiment: 1 Episode: 1021 | Score: -88.668 | Avg score: -208.080 |\n",
"| Experiment: 1 Episode: 1022 | Score: -373.375 | Avg score: -209.046 |\n",
"| Experiment: 1 Episode: 1023 | Score: -128.920 | Avg score: -207.446 |\n",
"| Experiment: 1 Episode: 1024 | Score: -240.380 | Avg score: -208.644 |\n",
"| Experiment: 1 Episode: 1025 | Score: -292.727 | Avg score: -210.964 |\n",
"| Experiment: 1 Episode: 1026 | Score: -327.510 | Avg score: -211.739 |\n",
"| Experiment: 1 Episode: 1027 | Score: -167.830 | Avg score: -211.820 |\n",
"| Experiment: 1 Episode: 1028 | Score: -133.744 | Avg score: -211.351 |\n",
"| Experiment: 1 Episode: 1029 | Score: -304.238 | Avg score: -211.383 |\n",
"| Experiment: 1 Episode: 1030 | Score: -229.270 | Avg score: -210.233 |\n",
"| Experiment: 1 Episode: 1031 | Score: -337.960 | Avg score: -208.618 |\n",
"| Experiment: 1 Episode: 1032 | Score: -128.481 | Avg score: -208.225 |\n",
"| Experiment: 1 Episode: 1033 | Score: -325.476 | Avg score: -208.434 |\n",
"| Experiment: 1 Episode: 1034 | Score: -276.967 | Avg score: -207.789 |\n",
"| Experiment: 1 Episode: 1035 | Score: -391.741 | Avg score: -209.551 |\n",
"| Experiment: 1 Episode: 1036 | Score: -302.421 | Avg score: -212.106 |\n",
"| Experiment: 1 Episode: 1037 | Score: -576.342 | Avg score: -217.229 |\n",
"| Experiment: 1 Episode: 1038 | Score: -393.884 | Avg score: -218.181 |\n",
"| Experiment: 1 Episode: 1039 | Score: -379.710 | Avg score: -218.970 |\n",
"| Experiment: 1 Episode: 1040 | Score: -316.822 | Avg score: -219.932 |\n",
"| Experiment: 1 Episode: 1041 | Score: -260.970 | Avg score: -221.464 |\n",
"| Experiment: 1 Episode: 1042 | Score: -80.828 | Avg score: -220.249 |\n",
"| Experiment: 1 Episode: 1043 | Score: -290.181 | Avg score: -220.131 |\n",
"| Experiment: 1 Episode: 1044 | Score: -286.651 | Avg score: -219.705 |\n",
"| Experiment: 1 Episode: 1045 | Score: -33.633 | Avg score: -217.776 |\n",
"| Experiment: 1 Episode: 1046 | Score: -263.205 | Avg score: -218.964 |\n",
"| Experiment: 1 Episode: 1047 | Score: -298.525 | Avg score: -218.432 |\n",
"| Experiment: 1 Episode: 1048 | Score: -84.462 | Avg score: -216.752 |\n",
"| Experiment: 1 Episode: 1049 | Score: -100.010 | Avg score: -216.755 |\n",
"| Experiment: 1 Episode: 1050 | Score: -169.462 | Avg score: -214.451 |\n",
"| Experiment: 1 Episode: 1051 | Score: -88.496 | Avg score: -214.561 |\n",
"| Experiment: 1 Episode: 1052 | Score: -104.657 | Avg score: -213.157 |\n",
"| Experiment: 1 Episode: 1053 | Score: -168.208 | Avg score: -211.699 |\n",
"| Experiment: 1 Episode: 1054 | Score: -58.696 | Avg score: -210.313 |\n",
"| Experiment: 1 Episode: 1055 | Score: -112.173 | Avg score: -209.989 |\n",
"| Experiment: 1 Episode: 1056 | Score: -63.480 | Avg score: -207.437 |\n",
"| Experiment: 1 Episode: 1057 | Score: -86.670 | Avg score: -205.383 |\n",
"| Experiment: 1 Episode: 1058 | Score: -261.140 | Avg score: -206.801 |\n",
"| Experiment: 1 Episode: 1059 | Score: -358.034 | Avg score: -205.960 |\n",
"| Experiment: 1 Episode: 1060 | Score: -504.803 | Avg score: -209.429 |\n",
"| Experiment: 1 Episode: 1061 | Score: -56.795 | Avg score: -206.861 |\n",
"| Experiment: 1 Episode: 1062 | Score: -353.578 | Avg score: -206.336 |\n",
"| Experiment: 1 Episode: 1063 | Score: -412.658 | Avg score: -207.259 |\n",
"| Experiment: 1 Episode: 1064 | Score: -120.136 | Avg score: -206.337 |\n",
"| Experiment: 1 Episode: 1065 | Score: -131.735 | Avg score: -203.802 |\n",
"| Experiment: 1 Episode: 1066 | Score: -54.148 | Avg score: -203.324 |\n",
"| Experiment: 1 Episode: 1067 | Score: -91.370 | Avg score: -202.934 |\n",
"| Experiment: 1 Episode: 1068 | Score: -378.941 | Avg score: -205.158 |\n",
"| Experiment: 1 Episode: 1069 | Score: -137.526 | Avg score: -205.613 |\n",
"| Experiment: 1 Episode: 1070 | Score: -111.700 | Avg score: -205.551 |\n",
"| Experiment: 1 Episode: 1071 | Score: -308.544 | Avg score: -207.822 |\n",
"| Experiment: 1 Episode: 1072 | Score: -252.866 | Avg score: -207.961 |\n",
"| Experiment: 1 Episode: 1073 | Score: -51.911 | Avg score: -207.242 |\n",
"| Experiment: 1 Episode: 1074 | Score: -69.835 | Avg score: -207.097 |\n",
"| Experiment: 1 Episode: 1075 | Score: -291.239 | Avg score: -206.497 |\n",
"| Experiment: 1 Episode: 1076 | Score: -339.595 | Avg score: -206.720 |\n",
"| Experiment: 1 Episode: 1077 | Score: -111.755 | Avg score: -207.277 |\n",
"| Experiment: 1 Episode: 1078 | Score: -203.836 | Avg score: -206.461 |\n",
"| Experiment: 1 Episode: 1079 | Score: -233.954 | Avg score: -205.842 |\n",
"| Experiment: 1 Episode: 1080 | Score: -144.460 | Avg score: -205.404 |\n",
"| Experiment: 1 Episode: 1081 | Score: -124.628 | Avg score: -205.840 |\n",
"| Experiment: 1 Episode: 1082 | Score: -37.127 | Avg score: -205.304 |\n",
"| Experiment: 1 Episode: 1083 | Score: -216.351 | Avg score: -206.488 |\n",
"| Experiment: 1 Episode: 1084 | Score: -45.265 | Avg score: -203.329 |\n",
"| Experiment: 1 Episode: 1085 | Score: -79.854 | Avg score: -202.212 |\n",
"| Experiment: 1 Episode: 1086 | Score: -88.488 | Avg score: -200.501 |\n",
"| Experiment: 1 Episode: 1087 | Score: -288.282 | Avg score: -200.524 |\n",
"| Experiment: 1 Episode: 1088 | Score: -124.479 | Avg score: -199.954 |\n",
"| Experiment: 1 Episode: 1089 | Score: -117.223 | Avg score: -197.698 |\n",
"| Experiment: 1 Episode: 1090 | Score: -375.174 | Avg score: -197.758 |\n",
"| Experiment: 1 Episode: 1091 | Score: -91.453 | Avg score: -196.811 |\n",
"| Experiment: 1 Episode: 1092 | Score: -366.490 | Avg score: -199.205 |\n",
"| Experiment: 1 Episode: 1093 | Score: -350.148 | Avg score: -199.321 |\n",
"| Experiment: 1 Episode: 1094 | Score: -298.060 | Avg score: -201.641 |\n",
"| Experiment: 1 Episode: 1095 | Score: -170.839 | Avg score: -202.985 |\n",
"| Experiment: 1 Episode: 1096 | Score: -140.660 | Avg score: -202.650 |\n",
"| Experiment: 1 Episode: 1097 | Score: -263.699 | Avg score: -203.118 |\n",
"| Experiment: 1 Episode: 1098 | Score: -124.292 | Avg score: -203.085 |\n",
"| Experiment: 1 Episode: 1099 | Score: -31.479 | Avg score: -201.741 |\n",
"| Experiment: 1 Episode: 1100 | Score: -305.461 | Avg score: -204.599 |\n",
"| Experiment: 1 Episode: 1101 | Score: -89.151 | Avg score: -203.140 |\n",
"| Experiment: 1 Episode: 1102 | Score: -206.901 | Avg score: -204.221 |\n",
"| Experiment: 1 Episode: 1103 | Score: -232.551 | Avg score: -205.681 |\n",
"| Experiment: 1 Episode: 1104 | Score: -293.435 | Avg score: -204.208 |\n",
"| Experiment: 1 Episode: 1105 | Score: -104.083 | Avg score: -204.722 |\n",
"| Experiment: 1 Episode: 1106 | Score: -311.003 | Avg score: -207.248 |\n",
"| Experiment: 1 Episode: 1107 | Score: -169.362 | Avg score: -205.841 |\n",
"| Experiment: 1 Episode: 1108 | Score: -312.728 | Avg score: -208.068 |\n",
"| Experiment: 1 Episode: 1109 | Score: -98.038 | Avg score: -206.391 |\n",
"| Experiment: 1 Episode: 1110 | Score: -109.976 | Avg score: -203.204 |\n",
"| Experiment: 1 Episode: 1111 | Score: -90.514 | Avg score: -204.030 |\n",
"| Experiment: 1 Episode: 1112 | Score: -58.988 | Avg score: -203.563 |\n",
"| Experiment: 1 Episode: 1113 | Score: -367.995 | Avg score: -206.474 |\n",
"| Experiment: 1 Episode: 1114 | Score: -253.889 | Avg score: -208.317 |\n",
"| Experiment: 1 Episode: 1115 | Score: -461.342 | Avg score: -209.004 |\n",
"| Experiment: 1 Episode: 1116 | Score: -257.700 | Avg score: -208.238 |\n",
"| Experiment: 1 Episode: 1117 | Score: -137.702 | Avg score: -208.108 |\n",
"| Experiment: 1 Episode: 1118 | Score: -472.902 | Avg score: -210.801 |\n",
"| Experiment: 1 Episode: 1119 | Score: -12.914 | Avg score: -209.994 |\n",
"| Experiment: 1 Episode: 1120 | Score: -87.896 | Avg score: -210.158 |\n",
"| Experiment: 1 Episode: 1121 | Score: -457.090 | Avg score: -213.843 |\n",
"| Experiment: 1 Episode: 1122 | Score: -67.059 | Avg score: -210.780 |\n",
"| Experiment: 1 Episode: 1123 | Score: -317.880 | Avg score: -212.669 |\n",
"| Experiment: 1 Episode: 1124 | Score: -298.737 | Avg score: -213.253 |\n",
"| Experiment: 1 Episode: 1125 | Score: -336.079 | Avg score: -213.686 |\n",
"| Experiment: 1 Episode: 1126 | Score: -214.315 | Avg score: -212.554 |\n",
"| Experiment: 1 Episode: 1127 | Score: -97.309 | Avg score: -211.849 |\n",
"| Experiment: 1 Episode: 1128 | Score: -359.938 | Avg score: -214.111 |\n",
"| Experiment: 1 Episode: 1129 | Score: -64.645 | Avg score: -211.715 |\n",
"| Experiment: 1 Episode: 1130 | Score: -387.067 | Avg score: -213.293 |\n",
"| Experiment: 1 Episode: 1131 | Score: -233.899 | Avg score: -212.252 |\n",
"| Experiment: 1 Episode: 1132 | Score: -84.097 | Avg score: -211.809 |\n",
"| Experiment: 1 Episode: 1133 | Score: -193.387 | Avg score: -210.488 |\n",
"| Experiment: 1 Episode: 1134 | Score: -270.219 | Avg score: -210.420 |\n",
"| Experiment: 1 Episode: 1135 | Score: -78.733 | Avg score: -207.290 |\n",
"| Experiment: 1 Episode: 1136 | Score: -291.336 | Avg score: -207.179 |\n",
"| Experiment: 1 Episode: 1137 | Score: -337.768 | Avg score: -204.794 |\n",
"| Experiment: 1 Episode: 1138 | Score: -418.064 | Avg score: -205.035 |\n",
"| Experiment: 1 Episode: 1139 | Score: -158.017 | Avg score: -202.818 |\n",
"| Experiment: 1 Episode: 1140 | Score: -104.406 | Avg score: -200.694 |\n",
"| Experiment: 1 Episode: 1141 | Score: -193.438 | Avg score: -200.019 |\n",
"| Experiment: 1 Episode: 1142 | Score: -57.961 | Avg score: -199.790 |\n",
"| Experiment: 1 Episode: 1143 | Score: -48.382 | Avg score: -197.372 |\n",
"| Experiment: 1 Episode: 1144 | Score: -249.764 | Avg score: -197.003 |\n",
"| Experiment: 1 Episode: 1145 | Score: -415.637 | Avg score: -200.823 |\n",
"| Experiment: 1 Episode: 1146 | Score: -355.579 | Avg score: -201.747 |\n",
"| Experiment: 1 Episode: 1147 | Score: -309.173 | Avg score: -201.854 |\n",
"| Experiment: 1 Episode: 1148 | Score: -234.080 | Avg score: -203.350 |\n",
"| Experiment: 1 Episode: 1149 | Score: -440.301 | Avg score: -206.753 |\n",
"| Experiment: 1 Episode: 1150 | Score: -219.386 | Avg score: -207.252 |\n",
"| Experiment: 1 Episode: 1151 | Score: -369.678 | Avg score: -210.064 |\n",
"| Experiment: 1 Episode: 1152 | Score: -136.780 | Avg score: -210.385 |\n",
"| Experiment: 1 Episode: 1153 | Score: -387.860 | Avg score: -212.582 |\n",
"| Experiment: 1 Episode: 1154 | Score: -335.957 | Avg score: -215.354 |\n",
"| Experiment: 1 Episode: 1155 | Score: -158.893 | Avg score: -215.821 |\n",
"| Experiment: 1 Episode: 1156 | Score: -218.225 | Avg score: -217.369 |\n",
"| Experiment: 1 Episode: 1157 | Score: -156.063 | Avg score: -218.063 |\n",
"| Experiment: 1 Episode: 1158 | Score: -234.001 | Avg score: -217.791 |\n",
"| Experiment: 1 Episode: 1159 | Score: -74.661 | Avg score: -214.958 |\n",
"| Experiment: 1 Episode: 1160 | Score: -414.996 | Avg score: -214.060 |\n",
"| Experiment: 1 Episode: 1161 | Score: -368.986 | Avg score: -217.182 |\n",
"| Experiment: 1 Episode: 1162 | Score: -231.029 | Avg score: -215.956 |\n",
"| Experiment: 1 Episode: 1163 | Score: 147.119 | Avg score: -210.358 |\n",
"| Experiment: 1 Episode: 1164 | Score: -421.599 | Avg score: -213.373 |\n",
"| Experiment: 1 Episode: 1165 | Score: -115.300 | Avg score: -213.209 |\n",
"| Experiment: 1 Episode: 1166 | Score: 6.000 | Avg score: -212.607 |\n",
"| Experiment: 1 Episode: 1167 | Score: -235.752 | Avg score: -214.051 |\n",
"| Experiment: 1 Episode: 1168 | Score: -138.639 | Avg score: -211.648 |\n",
"| Experiment: 1 Episode: 1169 | Score: -204.473 | Avg score: -212.317 |\n",
"| Experiment: 1 Episode: 1170 | Score: -178.681 | Avg score: -212.987 |\n",
"| Experiment: 1 Episode: 1171 | Score: -188.545 | Avg score: -211.787 |\n",
"| Experiment: 1 Episode: 1172 | Score: -376.778 | Avg score: -213.026 |\n",
"| Experiment: 1 Episode: 1173 | Score: -201.803 | Avg score: -214.525 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 Episode: 1174 | Score: -248.137 | Avg score: -216.308 |\n",
"| Experiment: 1 Episode: 1175 | Score: -58.992 | Avg score: -213.986 |\n",
"| Experiment: 1 Episode: 1176 | Score: -121.033 | Avg score: -211.800 |\n",
"| Experiment: 1 Episode: 1177 | Score: -276.555 | Avg score: -213.448 |\n",
"| Experiment: 1 Episode: 1178 | Score: -309.788 | Avg score: -214.508 |\n",
"| Experiment: 1 Episode: 1179 | Score: -396.477 | Avg score: -216.133 |\n",
"| Experiment: 1 Episode: 1180 | Score: -85.423 | Avg score: -215.543 |\n",
"| Experiment: 1 Episode: 1181 | Score: -2.937 | Avg score: -214.326 |\n",
"| Experiment: 1 Episode: 1182 | Score: -259.060 | Avg score: -216.545 |\n",
"| Experiment: 1 Episode: 1183 | Score: -219.693 | Avg score: -216.578 |\n",
"| Experiment: 1 Episode: 1184 | Score: -266.586 | Avg score: -218.792 |\n",
"| Experiment: 1 Episode: 1185 | Score: -69.078 | Avg score: -218.684 |\n",
"| Experiment: 1 Episode: 1186 | Score: -168.047 | Avg score: -219.479 |\n",
"| Experiment: 1 Episode: 1187 | Score: -355.590 | Avg score: -220.153 |\n",
"| Experiment: 1 Episode: 1188 | Score: -71.552 | Avg score: -219.623 |\n",
"| Experiment: 1 Episode: 1189 | Score: -284.280 | Avg score: -221.294 |\n",
"| Experiment: 1 Episode: 1190 | Score: -355.866 | Avg score: -221.101 |\n",
"| Experiment: 1 Episode: 1191 | Score: -206.771 | Avg score: -222.254 |\n",
"| Experiment: 1 Episode: 1192 | Score: -145.704 | Avg score: -220.046 |\n",
"| Experiment: 1 Episode: 1193 | Score: -416.314 | Avg score: -220.708 |\n",
"| Experiment: 1 Episode: 1194 | Score: -366.315 | Avg score: -221.390 |\n",
"| Experiment: 1 Episode: 1195 | Score: -251.366 | Avg score: -222.196 |\n",
"| Experiment: 1 Episode: 1196 | Score: -199.921 | Avg score: -222.788 |\n",
"| Experiment: 1 Episode: 1197 | Score: -63.355 | Avg score: -220.785 |\n",
"| Experiment: 1 Episode: 1198 | Score: -80.033 | Avg score: -220.342 |\n",
"| Experiment: 1 Episode: 1199 | Score: -61.498 | Avg score: -220.642 |\n",
"| Experiment: 1 Episode: 1200 | Score: -80.443 | Avg score: -218.392 |\n",
"| Experiment: 1 Episode: 1201 | Score: -163.131 | Avg score: -219.132 |\n",
"| Experiment: 1 Episode: 1202 | Score: -310.952 | Avg score: -220.172 |\n",
"| Experiment: 1 Episode: 1203 | Score: -115.937 | Avg score: -219.006 |\n",
"| Experiment: 1 Episode: 1204 | Score: -87.714 | Avg score: -216.949 |\n",
"| Experiment: 1 Episode: 1205 | Score: -243.929 | Avg score: -218.348 |\n",
"| Experiment: 1 Episode: 1206 | Score: -297.056 | Avg score: -218.208 |\n",
"| Experiment: 1 Episode: 1207 | Score: -159.158 | Avg score: -218.106 |\n",
"| Experiment: 1 Episode: 1208 | Score: -39.676 | Avg score: -215.376 |\n",
"| Experiment: 1 Episode: 1209 | Score: -386.042 | Avg score: -218.256 |\n",
"| Experiment: 1 Episode: 1210 | Score: -162.764 | Avg score: -218.783 |\n",
"| Experiment: 1 Episode: 1211 | Score: -292.741 | Avg score: -220.806 |\n",
"| Experiment: 1 Episode: 1212 | Score: -118.310 | Avg score: -221.399 |\n",
"| Experiment: 1 Episode: 1213 | Score: -98.975 | Avg score: -218.709 |\n",
"| Experiment: 1 Episode: 1214 | Score: -0.655 | Avg score: -216.176 |\n",
"| Experiment: 1 Episode: 1215 | Score: -41.088 | Avg score: -211.974 |\n",
"| Experiment: 1 Episode: 1216 | Score: -186.919 | Avg score: -211.266 |\n",
"| Experiment: 1 Episode: 1217 | Score: -62.546 | Avg score: -210.514 |\n",
"| Experiment: 1 Episode: 1218 | Score: -280.819 | Avg score: -208.594 |\n",
"| Experiment: 1 Episode: 1219 | Score: -223.849 | Avg score: -210.703 |\n",
"| Experiment: 1 Episode: 1220 | Score: -254.649 | Avg score: -212.371 |\n",
"| Experiment: 1 Episode: 1221 | Score: -70.305 | Avg score: -208.503 |\n",
"| Experiment: 1 Episode: 1222 | Score: -18.952 | Avg score: -208.022 |\n",
"| Experiment: 1 Episode: 1223 | Score: -200.445 | Avg score: -206.847 |\n",
"| Experiment: 1 Episode: 1224 | Score: -76.831 | Avg score: -204.628 |\n",
"| Experiment: 1 Episode: 1225 | Score: -84.628 | Avg score: -202.114 |\n",
"| Experiment: 1 Episode: 1226 | Score: -207.466 | Avg score: -202.045 |\n",
"| Experiment: 1 Episode: 1227 | Score: -151.020 | Avg score: -202.582 |\n",
"| Experiment: 1 Episode: 1228 | Score: -99.242 | Avg score: -199.975 |\n",
"| Experiment: 1 Episode: 1229 | Score: -464.487 | Avg score: -203.974 |\n",
"| Experiment: 1 Episode: 1230 | Score: -189.964 | Avg score: -202.003 |\n",
"| Experiment: 1 Episode: 1231 | Score: -385.540 | Avg score: -203.519 |\n",
"| Experiment: 1 Episode: 1232 | Score: -250.731 | Avg score: -205.185 |\n",
"| Experiment: 1 Episode: 1233 | Score: -90.686 | Avg score: -204.158 |\n",
"| Experiment: 1 Episode: 1234 | Score: -276.618 | Avg score: -204.222 |\n",
"| Experiment: 1 Episode: 1235 | Score: 131.813 | Avg score: -202.117 |\n",
"| Experiment: 1 Episode: 1236 | Score: -238.344 | Avg score: -201.587 |\n",
"| Experiment: 1 Episode: 1237 | Score: -311.921 | Avg score: -201.329 |\n",
"| Experiment: 1 Episode: 1238 | Score: -147.396 | Avg score: -198.622 |\n",
"| Experiment: 1 Episode: 1239 | Score: -354.266 | Avg score: -200.584 |\n",
"| Experiment: 1 Episode: 1240 | Score: -159.421 | Avg score: -201.135 |\n",
"| Experiment: 1 Episode: 1241 | Score: -359.635 | Avg score: -202.797 |\n",
"| Experiment: 1 Episode: 1242 | Score: -212.083 | Avg score: -204.338 |\n",
"| Experiment: 1 Episode: 1243 | Score: -56.632 | Avg score: -204.420 |\n",
"| Experiment: 1 Episode: 1244 | Score: -84.442 | Avg score: -202.767 |\n",
"| Experiment: 1 Episode: 1245 | Score: -354.935 | Avg score: -202.160 |\n",
"| Experiment: 1 Episode: 1246 | Score: -61.947 | Avg score: -199.224 |\n",
"| Experiment: 1 Episode: 1247 | Score: -350.372 | Avg score: -199.636 |\n",
"| Experiment: 1 Episode: 1248 | Score: -397.489 | Avg score: -201.270 |\n",
"| Experiment: 1 Episode: 1249 | Score: -47.294 | Avg score: -197.340 |\n",
"| Experiment: 1 Episode: 1250 | Score: -415.724 | Avg score: -199.303 |\n",
"| Experiment: 1 Episode: 1251 | Score: -214.752 | Avg score: -197.754 |\n",
"| Experiment: 1 Episode: 1252 | Score: -59.785 | Avg score: -196.984 |\n",
"| Experiment: 1 Episode: 1253 | Score: -127.308 | Avg score: -194.378 |\n",
"| Experiment: 1 Episode: 1254 | Score: -124.124 | Avg score: -192.260 |\n",
"| Experiment: 1 Episode: 1255 | Score: -46.805 | Avg score: -191.139 |\n",
"| Experiment: 1 Episode: 1256 | Score: -74.435 | Avg score: -189.701 |\n",
"| Experiment: 1 Episode: 1257 | Score: -309.472 | Avg score: -191.235 |\n",
"| Experiment: 1 Episode: 1258 | Score: -161.078 | Avg score: -190.506 |\n",
"| Experiment: 1 Episode: 1259 | Score: -163.101 | Avg score: -191.390 |\n",
"| Experiment: 1 Episode: 1260 | Score: -215.071 | Avg score: -189.391 |\n",
"| Experiment: 1 Episode: 1261 | Score: -99.433 | Avg score: -186.696 |\n",
"| Experiment: 1 Episode: 1262 | Score: -150.174 | Avg score: -185.887 |\n",
"| Experiment: 1 Episode: 1263 | Score: -311.437 | Avg score: -190.473 |\n",
"| Experiment: 1 Episode: 1264 | Score: -138.745 | Avg score: -187.644 |\n",
"| Experiment: 1 Episode: 1265 | Score: -77.100 | Avg score: -187.262 |\n",
"| Experiment: 1 Episode: 1266 | Score: -125.929 | Avg score: -188.581 |\n",
"| Experiment: 1 Episode: 1267 | Score: -85.801 | Avg score: -187.082 |\n",
"| Experiment: 1 Episode: 1268 | Score: -104.865 | Avg score: -186.744 |\n",
"| Experiment: 1 Episode: 1269 | Score: -44.845 | Avg score: -185.148 |\n",
"| Experiment: 1 Episode: 1270 | Score: -433.027 | Avg score: -187.691 |\n",
"| Experiment: 1 Episode: 1271 | Score: -135.188 | Avg score: -187.158 |\n",
"| Experiment: 1 Episode: 1272 | Score: -242.263 | Avg score: -185.813 |\n",
"| Experiment: 1 Episode: 1273 | Score: -314.441 | Avg score: -186.939 |\n",
"| Experiment: 1 Episode: 1274 | Score: -255.214 | Avg score: -187.010 |\n",
"| Experiment: 1 Episode: 1275 | Score: -108.462 | Avg score: -187.505 |\n",
"| Experiment: 1 Episode: 1276 | Score: -155.747 | Avg score: -187.852 |\n",
"| Experiment: 1 Episode: 1277 | Score: -226.410 | Avg score: -187.350 |\n",
"| Experiment: 1 Episode: 1278 | Score: -207.626 | Avg score: -186.329 |\n",
"| Experiment: 1 Episode: 1279 | Score: -94.790 | Avg score: -183.312 |\n",
"| Experiment: 1 Episode: 1280 | Score: -250.004 | Avg score: -184.958 |\n",
"| Experiment: 1 Episode: 1281 | Score: -84.942 | Avg score: -185.778 |\n",
"| Experiment: 1 Episode: 1282 | Score: -23.110 | Avg score: -183.418 |\n",
"| Experiment: 1 Episode: 1283 | Score: -256.482 | Avg score: -183.786 |\n",
"| Experiment: 1 Episode: 1284 | Score: -108.101 | Avg score: -182.201 |\n",
"| Experiment: 1 Episode: 1285 | Score: -314.419 | Avg score: -184.655 |\n",
"| Experiment: 1 Episode: 1286 | Score: -538.485 | Avg score: -188.359 |\n",
"| Experiment: 1 Episode: 1287 | Score: -366.244 | Avg score: -188.465 |\n",
"| Experiment: 1 Episode: 1288 | Score: -402.522 | Avg score: -191.775 |\n",
"| Experiment: 1 Episode: 1289 | Score: -273.899 | Avg score: -191.671 |\n",
"| Experiment: 1 Episode: 1290 | Score: -63.896 | Avg score: -188.752 |\n",
"| Experiment: 1 Episode: 1291 | Score: -39.006 | Avg score: -187.074 |\n",
"| Experiment: 1 Episode: 1292 | Score: -122.674 | Avg score: -186.844 |\n",
"| Experiment: 1 Episode: 1293 | Score: -62.630 | Avg score: -183.307 |\n",
"| Experiment: 1 Episode: 1294 | Score: -93.191 | Avg score: -180.576 |\n",
"| Experiment: 1 Episode: 1295 | Score: -596.581 | Avg score: -184.028 |\n",
"| Experiment: 1 Episode: 1296 | Score: -614.176 | Avg score: -188.170 |\n",
"| Experiment: 1 Episode: 1297 | Score: -394.641 | Avg score: -191.483 |\n",
"| Experiment: 1 Episode: 1298 | Score: -299.666 | Avg score: -193.679 |\n",
"| Experiment: 1 Episode: 1299 | Score: -354.656 | Avg score: -196.611 |\n",
"| Experiment: 1 Episode: 1300 | Score: -62.400 | Avg score: -196.431 |\n",
"| Experiment: 1 Episode: 1301 | Score: -273.823 | Avg score: -197.538 |\n",
"| Experiment: 1 Episode: 1302 | Score: -65.919 | Avg score: -195.087 |\n",
"| Experiment: 1 Episode: 1303 | Score: -265.318 | Avg score: -196.581 |\n",
"| Experiment: 1 Episode: 1304 | Score: -230.521 | Avg score: -198.009 |\n",
"| Experiment: 1 Episode: 1305 | Score: -89.612 | Avg score: -196.466 |\n",
"| Experiment: 1 Episode: 1306 | Score: 47.170 | Avg score: -193.024 |\n",
"| Experiment: 1 Episode: 1307 | Score: -283.645 | Avg score: -194.269 |\n",
"| Experiment: 1 Episode: 1308 | Score: -121.213 | Avg score: -195.084 |\n",
"| Experiment: 1 Episode: 1309 | Score: -416.703 | Avg score: -195.391 |\n",
"| Experiment: 1 Episode: 1310 | Score: -104.088 | Avg score: -194.804 |\n",
"| Experiment: 1 Episode: 1311 | Score: -346.142 | Avg score: -195.338 |\n",
"| Experiment: 1 Episode: 1312 | Score: -304.718 | Avg score: -197.202 |\n",
"| Experiment: 1 Episode: 1313 | Score: -128.514 | Avg score: -197.497 |\n",
"| Experiment: 1 Episode: 1314 | Score: -441.449 | Avg score: -201.905 |\n",
"| Experiment: 1 Episode: 1315 | Score: -254.854 | Avg score: -204.043 |\n",
"| Experiment: 1 Episode: 1316 | Score: -98.289 | Avg score: -203.157 |\n",
"| Experiment: 1 Episode: 1317 | Score: -288.936 | Avg score: -205.420 |\n",
"| Experiment: 1 Episode: 1318 | Score: -72.002 | Avg score: -203.332 |\n",
"| Experiment: 1 Episode: 1319 | Score: -94.276 | Avg score: -202.037 |\n",
"| Experiment: 1 Episode: 1320 | Score: -452.440 | Avg score: -204.014 |\n",
"| Experiment: 1 Episode: 1321 | Score: -224.946 | Avg score: -205.561 |\n",
"| Experiment: 1 Episode: 1322 | Score: -344.097 | Avg score: -208.812 |\n",
"| Experiment: 1 Episode: 1323 | Score: -354.762 | Avg score: -210.356 |\n",
"| Experiment: 1 Episode: 1324 | Score: -242.030 | Avg score: -212.007 |\n",
"| Experiment: 1 Episode: 1325 | Score: -324.034 | Avg score: -214.402 |\n",
"| Experiment: 1 Episode: 1326 | Score: -383.638 | Avg score: -216.163 |\n",
"| Experiment: 1 Episode: 1327 | Score: -42.439 | Avg score: -215.077 |\n",
"| Experiment: 1 Episode: 1328 | Score: -132.898 | Avg score: -215.414 |\n",
"| Experiment: 1 Episode: 1329 | Score: -90.956 | Avg score: -211.679 |\n",
"| Experiment: 1 Episode: 1330 | Score: -394.079 | Avg score: -213.720 |\n",
"| Experiment: 1 Episode: 1331 | Score: -430.549 | Avg score: -214.170 |\n",
"| Experiment: 1 Episode: 1332 | Score: -316.576 | Avg score: -214.828 |\n",
"| Experiment: 1 Episode: 1333 | Score: -173.182 | Avg score: -215.653 |\n",
"| Experiment: 1 Episode: 1334 | Score: -172.785 | Avg score: -214.615 |\n",
"| Experiment: 1 Episode: 1335 | Score: -109.738 | Avg score: -217.031 |\n",
"| Experiment: 1 Episode: 1336 | Score: -126.773 | Avg score: -215.915 |\n",
"| Experiment: 1 Episode: 1337 | Score: -80.305 | Avg score: -213.599 |\n",
"| Experiment: 1 Episode: 1338 | Score: -64.558 | Avg score: -212.770 |\n",
"| Experiment: 1 Episode: 1339 | Score: -215.356 | Avg score: -211.381 |\n",
"| Experiment: 1 Episode: 1340 | Score: -230.305 | Avg score: -212.090 |\n",
"| Experiment: 1 Episode: 1341 | Score: -209.504 | Avg score: -210.589 |\n",
"| Experiment: 1 Episode: 1342 | Score: -82.007 | Avg score: -209.288 |\n",
"| Experiment: 1 Episode: 1343 | Score: -105.631 | Avg score: -209.778 |\n",
"| Experiment: 1 Episode: 1344 | Score: -167.019 | Avg score: -210.604 |\n",
"| Experiment: 1 Episode: 1345 | Score: -318.525 | Avg score: -210.240 |\n",
"| Experiment: 1 Episode: 1346 | Score: -114.661 | Avg score: -210.767 |\n",
"| Experiment: 1 Episode: 1347 | Score: -103.478 | Avg score: -208.298 |\n",
"| Experiment: 1 Episode: 1348 | Score: -68.002 | Avg score: -205.003 |\n",
"| Experiment: 1 Episode: 1349 | Score: -158.511 | Avg score: -206.115 |\n",
"| Experiment: 1 Episode: 1350 | Score: -431.157 | Avg score: -206.269 |\n",
"| Experiment: 1 Episode: 1351 | Score: -40.541 | Avg score: -204.527 |\n",
"| Experiment: 1 Episode: 1352 | Score: -326.379 | Avg score: -207.193 |\n",
"| Experiment: 1 Episode: 1353 | Score: -325.308 | Avg score: -209.173 |\n",
"| Experiment: 1 Episode: 1354 | Score: -117.019 | Avg score: -209.102 |\n",
"| Experiment: 1 Episode: 1355 | Score: -25.898 | Avg score: -208.893 |\n",
"| Experiment: 1 Episode: 1356 | Score: -423.346 | Avg score: -212.382 |\n",
"| Experiment: 1 Episode: 1357 | Score: -364.465 | Avg score: -212.932 |\n",
"| Experiment: 1 Episode: 1358 | Score: -107.578 | Avg score: -212.397 |\n",
"| Experiment: 1 Episode: 1359 | Score: -336.261 | Avg score: -214.129 |\n",
"| Experiment: 1 Episode: 1360 | Score: -106.916 | Avg score: -213.047 |\n",
"| Experiment: 1 Episode: 1361 | Score: -30.031 | Avg score: -212.353 |\n",
"| Experiment: 1 Episode: 1362 | Score: -341.805 | Avg score: -214.270 |\n",
"| Experiment: 1 Episode: 1363 | Score: -90.830 | Avg score: -212.064 |\n",
"| Experiment: 1 Episode: 1364 | Score: -54.453 | Avg score: -211.221 |\n",
"| Experiment: 1 Episode: 1365 | Score: -178.295 | Avg score: -212.233 |\n",
"| Experiment: 1 Episode: 1366 | Score: -263.843 | Avg score: -213.612 |\n",
"| Experiment: 1 Episode: 1367 | Score: -114.352 | Avg score: -213.897 |\n",
"| Experiment: 1 Episode: 1368 | Score: -65.425 | Avg score: -213.503 |\n",
"| Experiment: 1 Episode: 1369 | Score: -82.842 | Avg score: -213.883 |\n",
"| Experiment: 1 Episode: 1370 | Score: -282.085 | Avg score: -212.373 |\n",
"| Experiment: 1 Episode: 1371 | Score: -168.746 | Avg score: -212.709 |\n",
"| Experiment: 1 Episode: 1372 | Score: -420.509 | Avg score: -214.491 |\n",
"| Experiment: 1 Episode: 1373 | Score: -283.058 | Avg score: -214.178 |\n",
"| Experiment: 1 Episode: 1374 | Score: -364.401 | Avg score: -215.269 |\n",
"| Experiment: 1 Episode: 1375 | Score: -84.116 | Avg score: -215.026 |\n",
"| Experiment: 1 Episode: 1376 | Score: -172.287 | Avg score: -215.191 |\n",
"| Experiment: 1 Episode: 1377 | Score: -127.651 | Avg score: -214.204 |\n",
"| Experiment: 1 Episode: 1378 | Score: -293.449 | Avg score: -215.062 |\n",
"| Experiment: 1 Episode: 1379 | Score: -269.222 | Avg score: -216.806 |\n",
"| Experiment: 1 Episode: 1380 | Score: -199.160 | Avg score: -216.298 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 Episode: 1381 | Score: -287.736 | Avg score: -218.326 |\n",
"| Experiment: 1 Episode: 1382 | Score: -131.592 | Avg score: -219.411 |\n",
"| Experiment: 1 Episode: 1383 | Score: -289.777 | Avg score: -219.744 |\n",
"| Experiment: 1 Episode: 1384 | Score: -234.236 | Avg score: -221.005 |\n",
"| Experiment: 1 Episode: 1385 | Score: -274.606 | Avg score: -220.607 |\n",
"| Experiment: 1 Episode: 1386 | Score: -41.708 | Avg score: -215.639 |\n",
"| Experiment: 1 Episode: 1387 | Score: -188.283 | Avg score: -213.859 |\n",
"| Experiment: 1 Episode: 1388 | Score: -295.473 | Avg score: -212.789 |\n",
"| Experiment: 1 Episode: 1389 | Score: -110.805 | Avg score: -211.158 |\n",
"| Experiment: 1 Episode: 1390 | Score: -192.629 | Avg score: -212.445 |\n",
"| Experiment: 1 Episode: 1391 | Score: -106.585 | Avg score: -213.121 |\n",
"| Experiment: 1 Episode: 1392 | Score: -281.138 | Avg score: -214.706 |\n",
"| Experiment: 1 Episode: 1393 | Score: -290.419 | Avg score: -216.984 |\n",
"| Experiment: 1 Episode: 1394 | Score: -305.115 | Avg score: -219.103 |\n",
"| Experiment: 1 Episode: 1395 | Score: -328.330 | Avg score: -216.420 |\n",
"| Experiment: 1 Episode: 1396 | Score: -409.745 | Avg score: -214.376 |\n",
"| Experiment: 1 Episode: 1397 | Score: -102.108 | Avg score: -211.451 |\n",
"| Experiment: 1 Episode: 1398 | Score: -112.983 | Avg score: -209.584 |\n",
"| Experiment: 1 Episode: 1399 | Score: -191.799 | Avg score: -207.955 |\n",
"| Experiment: 1 Episode: 1400 | Score: -146.323 | Avg score: -208.795 |\n",
"| Experiment: 1 Episode: 1401 | Score: -275.091 | Avg score: -208.807 |\n",
"| Experiment: 1 Episode: 1402 | Score: -52.261 | Avg score: -208.671 |\n",
"| Experiment: 1 Episode: 1403 | Score: -323.209 | Avg score: -209.250 |\n",
"| Experiment: 1 Episode: 1404 | Score: -290.967 | Avg score: -209.854 |\n",
"| Experiment: 1 Episode: 1405 | Score: -102.562 | Avg score: -209.984 |\n",
"| Experiment: 1 Episode: 1406 | Score: -468.470 | Avg score: -215.140 |\n",
"| Experiment: 1 Episode: 1407 | Score: -10.095 | Avg score: -212.404 |\n",
"| Experiment: 1 Episode: 1408 | Score: -95.439 | Avg score: -212.147 |\n",
"| Experiment: 1 Episode: 1409 | Score: -268.146 | Avg score: -210.661 |\n",
"| Experiment: 1 Episode: 1410 | Score: -116.081 | Avg score: -210.781 |\n",
"| Experiment: 1 Episode: 1411 | Score: -154.472 | Avg score: -208.864 |\n",
"| Experiment: 1 Episode: 1412 | Score: -270.360 | Avg score: -208.521 |\n",
"| Experiment: 1 Episode: 1413 | Score: -493.638 | Avg score: -212.172 |\n",
"| Experiment: 1 Episode: 1414 | Score: -200.782 | Avg score: -209.765 |\n",
"| Experiment: 1 Episode: 1415 | Score: -371.514 | Avg score: -210.932 |\n",
"| Experiment: 1 Episode: 1416 | Score: -183.307 | Avg score: -211.782 |\n",
"| Experiment: 1 Episode: 1417 | Score: -278.672 | Avg score: -211.680 |\n",
"| Experiment: 1 Episode: 1418 | Score: -377.891 | Avg score: -214.738 |\n",
"| Experiment: 1 Episode: 1419 | Score: -72.895 | Avg score: -214.525 |\n",
"| Experiment: 1 Episode: 1420 | Score: -261.847 | Avg score: -212.619 |\n",
"| Experiment: 1 Episode: 1421 | Score: -135.216 | Avg score: -211.721 |\n",
"| Experiment: 1 Episode: 1422 | Score: -60.315 | Avg score: -208.884 |\n",
"| Experiment: 1 Episode: 1423 | Score: -81.701 | Avg score: -206.153 |\n",
"| Experiment: 1 Episode: 1424 | Score: -83.545 | Avg score: -204.568 |\n",
"| Experiment: 1 Episode: 1425 | Score: -399.124 | Avg score: -205.319 |\n",
"| Experiment: 1 Episode: 1426 | Score: -329.292 | Avg score: -204.776 |\n",
"| Experiment: 1 Episode: 1427 | Score: -342.434 | Avg score: -207.775 |\n",
"| Experiment: 1 Episode: 1428 | Score: -348.018 | Avg score: -209.927 |\n",
"| Experiment: 1 Episode: 1429 | Score: -94.594 | Avg score: -209.963 |\n",
"| Experiment: 1 Episode: 1430 | Score: -411.310 | Avg score: -210.135 |\n",
"| Experiment: 1 Episode: 1431 | Score: -317.312 | Avg score: -209.003 |\n",
"| Experiment: 1 Episode: 1432 | Score: -132.686 | Avg score: -207.164 |\n",
"| Experiment: 1 Episode: 1433 | Score: -114.830 | Avg score: -206.581 |\n",
"| Experiment: 1 Episode: 1434 | Score: -362.543 | Avg score: -208.478 |\n",
"| Experiment: 1 Episode: 1435 | Score: -249.195 | Avg score: -209.873 |\n",
"| Experiment: 1 Episode: 1436 | Score: -252.802 | Avg score: -211.133 |\n",
"| Experiment: 1 Episode: 1437 | Score: -63.434 | Avg score: -210.964 |\n",
"| Experiment: 1 Episode: 1438 | Score: -405.545 | Avg score: -214.374 |\n",
"| Experiment: 1 Episode: 1439 | Score: -54.698 | Avg score: -212.768 |\n",
"| Experiment: 1 Episode: 1440 | Score: -127.243 | Avg score: -211.737 |\n",
"| Experiment: 1 Episode: 1441 | Score: -64.146 | Avg score: -210.283 |\n",
"| Experiment: 1 Episode: 1442 | Score: -77.586 | Avg score: -210.239 |\n",
"| Experiment: 1 Episode: 1443 | Score: -85.031 | Avg score: -210.033 |\n",
"| Experiment: 1 Episode: 1444 | Score: -51.519 | Avg score: -208.878 |\n",
"| Experiment: 1 Episode: 1445 | Score: -197.889 | Avg score: -207.672 |\n",
"| Experiment: 1 Episode: 1446 | Score: -176.957 | Avg score: -208.295 |\n",
"| Experiment: 1 Episode: 1447 | Score: -352.244 | Avg score: -210.782 |\n",
"| Experiment: 1 Episode: 1448 | Score: -187.705 | Avg score: -211.979 |\n",
"| Experiment: 1 Episode: 1449 | Score: -87.749 | Avg score: -211.272 |\n",
"| Experiment: 1 Episode: 1450 | Score: -49.606 | Avg score: -207.456 |\n",
"| Experiment: 1 Episode: 1451 | Score: -203.013 | Avg score: -209.081 |\n",
"| Experiment: 1 Episode: 1452 | Score: -79.055 | Avg score: -206.608 |\n",
"| Experiment: 1 Episode: 1453 | Score: -67.960 | Avg score: -204.034 |\n",
"| Experiment: 1 Episode: 1454 | Score: -383.471 | Avg score: -206.699 |\n",
"| Experiment: 1 Episode: 1455 | Score: -60.449 | Avg score: -207.044 |\n",
"| Experiment: 1 Episode: 1456 | Score: -360.257 | Avg score: -206.413 |\n",
"| Experiment: 1 Episode: 1457 | Score: -464.104 | Avg score: -207.410 |\n",
"| Experiment: 1 Episode: 1458 | Score: -344.586 | Avg score: -209.780 |\n",
"| Experiment: 1 Episode: 1459 | Score: -79.950 | Avg score: -207.217 |\n",
"| Experiment: 1 Episode: 1460 | Score: -98.880 | Avg score: -207.136 |\n",
"| Experiment: 1 Episode: 1461 | Score: -481.323 | Avg score: -211.649 |\n",
"| Experiment: 1 Episode: 1462 | Score: -283.113 | Avg score: -211.062 |\n",
"| Experiment: 1 Episode: 1463 | Score: -261.945 | Avg score: -212.774 |\n",
"| Experiment: 1 Episode: 1464 | Score: -59.896 | Avg score: -212.828 |\n",
"| Experiment: 1 Episode: 1465 | Score: -333.491 | Avg score: -214.380 |\n",
"| Experiment: 1 Episode: 1466 | Score: -259.289 | Avg score: -214.334 |\n",
"| Experiment: 1 Episode: 1467 | Score: -81.407 | Avg score: -214.005 |\n",
"| Experiment: 1 Episode: 1468 | Score: -288.195 | Avg score: -216.233 |\n",
"| Experiment: 1 Episode: 1469 | Score: -127.315 | Avg score: -216.677 |\n",
"| Experiment: 1 Episode: 1470 | Score: -79.956 | Avg score: -214.656 |\n",
"| Experiment: 1 Episode: 1471 | Score: -51.402 | Avg score: -213.483 |\n",
"| Experiment: 1 Episode: 1472 | Score: -106.514 | Avg score: -210.343 |\n",
"| Experiment: 1 Episode: 1473 | Score: -310.501 | Avg score: -210.617 |\n",
"| Experiment: 1 Episode: 1474 | Score: -116.408 | Avg score: -208.137 |\n",
"| Experiment: 1 Episode: 1475 | Score: -89.804 | Avg score: -208.194 |\n",
"| Experiment: 1 Episode: 1476 | Score: -133.272 | Avg score: -207.804 |\n",
"| Experiment: 1 Episode: 1477 | Score: -96.046 | Avg score: -207.488 |\n",
"| Experiment: 1 Episode: 1478 | Score: -186.268 | Avg score: -206.416 |\n",
"| Experiment: 1 Episode: 1479 | Score: -240.997 | Avg score: -206.134 |\n",
"| Experiment: 1 Episode: 1480 | Score: -342.037 | Avg score: -207.563 |\n",
"| Experiment: 1 Episode: 1481 | Score: -330.602 | Avg score: -207.991 |\n",
"| Experiment: 1 Episode: 1482 | Score: -214.971 | Avg score: -208.825 |\n",
"| Experiment: 1 Episode: 1483 | Score: -376.735 | Avg score: -209.695 |\n",
"| Experiment: 1 Episode: 1484 | Score: -432.104 | Avg score: -211.673 |\n",
"| Experiment: 1 Episode: 1485 | Score: -148.596 | Avg score: -210.413 |\n",
"| Experiment: 1 Episode: 1486 | Score: -295.069 | Avg score: -212.947 |\n",
"| Experiment: 1 Episode: 1487 | Score: -137.023 | Avg score: -212.434 |\n",
"| Experiment: 1 Episode: 1488 | Score: -82.785 | Avg score: -210.307 |\n",
"| Experiment: 1 Episode: 1489 | Score: -92.227 | Avg score: -210.122 |\n",
"| Experiment: 1 Episode: 1490 | Score: -232.111 | Avg score: -210.516 |\n",
"| Experiment: 1 Episode: 1491 | Score: -382.005 | Avg score: -213.271 |\n",
"| Experiment: 1 Episode: 1492 | Score: -93.111 | Avg score: -211.390 |\n",
"| Experiment: 1 Episode: 1493 | Score: -134.033 | Avg score: -209.827 |\n",
"| Experiment: 1 Episode: 1494 | Score: -423.049 | Avg score: -211.006 |\n",
"| Experiment: 1 Episode: 1495 | Score: -55.281 | Avg score: -208.275 |\n",
"| Experiment: 1 Episode: 1496 | Score: -273.256 | Avg score: -206.910 |\n",
"| Experiment: 1 Episode: 1497 | Score: -118.181 | Avg score: -207.071 |\n",
"| Experiment: 1 Episode: 1498 | Score: -97.491 | Avg score: -206.916 |\n",
"| Experiment: 1 Episode: 1499 | Score: -21.966 | Avg score: -205.218 |\n",
"| Experiment: 1 Episode: 1500 | Score: -284.917 | Avg score: -206.604 |\n",
"| Experiment: 1 Episode: 1501 | Score: -121.009 | Avg score: -205.063 |\n",
"| Experiment: 1 Episode: 1502 | Score: -480.428 | Avg score: -209.345 |\n",
"| Experiment: 1 Episode: 1503 | Score: -282.653 | Avg score: -208.939 |\n",
"| Experiment: 1 Episode: 1504 | Score: -330.922 | Avg score: -209.339 |\n",
"| Experiment: 1 Episode: 1505 | Score: -177.284 | Avg score: -210.086 |\n",
"| Experiment: 1 Episode: 1506 | Score: -536.124 | Avg score: -210.762 |\n",
"| Experiment: 1 Episode: 1507 | Score: -172.824 | Avg score: -212.390 |\n",
"| Experiment: 1 Episode: 1508 | Score: -49.909 | Avg score: -211.934 |\n",
"| Experiment: 1 Episode: 1509 | Score: -243.597 | Avg score: -211.689 |\n",
"| Experiment: 1 Episode: 1510 | Score: -132.923 | Avg score: -211.857 |\n",
"| Experiment: 1 Episode: 1511 | Score: -96.336 | Avg score: -211.276 |\n",
"| Experiment: 1 Episode: 1512 | Score: -394.256 | Avg score: -212.515 |\n",
"| Experiment: 1 Episode: 1513 | Score: -372.944 | Avg score: -211.308 |\n",
"| Experiment: 1 Episode: 1514 | Score: -143.150 | Avg score: -210.732 |\n",
"| Experiment: 1 Episode: 1515 | Score: -124.460 | Avg score: -208.261 |\n",
"| Experiment: 1 Episode: 1516 | Score: -97.546 | Avg score: -207.404 |\n",
"| Experiment: 1 Episode: 1517 | Score: -173.932 | Avg score: -206.356 |\n",
"| Experiment: 1 Episode: 1518 | Score: -39.646 | Avg score: -202.974 |\n",
"| Experiment: 1 Episode: 1519 | Score: -125.365 | Avg score: -203.498 |\n",
"| Experiment: 1 Episode: 1520 | Score: -108.295 | Avg score: -201.963 |\n",
"| Experiment: 1 Episode: 1521 | Score: -322.676 | Avg score: -203.838 |\n",
"| Experiment: 1 Episode: 1522 | Score: -144.871 | Avg score: -204.683 |\n",
"| Experiment: 1 Episode: 1523 | Score: -248.509 | Avg score: -206.351 |\n",
"| Experiment: 1 Episode: 1524 | Score: -20.817 | Avg score: -205.724 |\n",
"| Experiment: 1 Episode: 1525 | Score: -163.723 | Avg score: -203.370 |\n",
"| Experiment: 1 Episode: 1526 | Score: -124.721 | Avg score: -201.324 |\n",
"| Experiment: 1 Episode: 1527 | Score: -65.838 | Avg score: -198.558 |\n",
"| Experiment: 1 Episode: 1528 | Score: -61.504 | Avg score: -195.693 |\n",
"| Experiment: 1 Episode: 1529 | Score: -230.400 | Avg score: -197.051 |\n",
"| Experiment: 1 Episode: 1530 | Score: -329.249 | Avg score: -196.231 |\n",
"| Experiment: 1 Episode: 1531 | Score: -153.344 | Avg score: -194.591 |\n",
"| Experiment: 1 Episode: 1532 | Score: -464.764 | Avg score: -197.912 |\n",
"| Experiment: 1 Episode: 1533 | Score: -338.588 | Avg score: -200.149 |\n",
"| Experiment: 1 Episode: 1534 | Score: -154.067 | Avg score: -198.064 |\n",
"| Experiment: 1 Episode: 1535 | Score: -261.404 | Avg score: -198.187 |\n",
"| Experiment: 1 Episode: 1536 | Score: -74.112 | Avg score: -196.400 |\n",
"| Experiment: 1 Episode: 1537 | Score: -232.929 | Avg score: -198.095 |\n",
"| Experiment: 1 Episode: 1538 | Score: -209.195 | Avg score: -196.131 |\n",
"| Experiment: 1 Episode: 1539 | Score: -105.965 | Avg score: -196.644 |\n",
"| Experiment: 1 Episode: 1540 | Score: -344.037 | Avg score: -198.812 |\n",
"| Experiment: 1 Episode: 1541 | Score: -236.635 | Avg score: -200.537 |\n",
"| Experiment: 1 Episode: 1542 | Score: -335.557 | Avg score: -203.116 |\n",
"| Experiment: 1 Episode: 1543 | Score: -306.872 | Avg score: -205.335 |\n",
"| Experiment: 1 Episode: 1544 | Score: -443.831 | Avg score: -209.258 |\n",
"| Experiment: 1 Episode: 1545 | Score: -381.940 | Avg score: -211.098 |\n",
"| Experiment: 1 Episode: 1546 | Score: -116.046 | Avg score: -210.489 |\n",
"| Experiment: 1 Episode: 1547 | Score: -289.893 | Avg score: -209.866 |\n",
"| Experiment: 1 Episode: 1548 | Score: -318.150 | Avg score: -211.170 |\n",
"| Experiment: 1 Episode: 1549 | Score: -401.076 | Avg score: -214.303 |\n",
"| Experiment: 1 Episode: 1550 | Score: -296.773 | Avg score: -216.775 |\n",
"| Experiment: 1 Episode: 1551 | Score: -158.274 | Avg score: -216.328 |\n",
"| Experiment: 1 Episode: 1552 | Score: -176.905 | Avg score: -217.306 |\n",
"| Experiment: 1 Episode: 1553 | Score: -84.275 | Avg score: -217.469 |\n",
"| Experiment: 1 Episode: 1554 | Score: -156.920 | Avg score: -215.204 |\n",
"| Experiment: 1 Episode: 1555 | Score: -94.793 | Avg score: -215.547 |\n",
"| Experiment: 1 Episode: 1556 | Score: -292.316 | Avg score: -214.868 |\n",
"| Experiment: 1 Episode: 1557 | Score: -336.404 | Avg score: -213.591 |\n",
"| Experiment: 1 Episode: 1558 | Score: -207.078 | Avg score: -212.216 |\n",
"| Experiment: 1 Episode: 1559 | Score: -200.267 | Avg score: -213.419 |\n",
"| Experiment: 1 Episode: 1560 | Score: -71.295 | Avg score: -213.143 |\n",
"| Experiment: 1 Episode: 1561 | Score: -103.621 | Avg score: -209.366 |\n",
"| Experiment: 1 Episode: 1562 | Score: -355.489 | Avg score: -210.090 |\n",
"| Experiment: 1 Episode: 1563 | Score: -530.974 | Avg score: -212.780 |\n",
"| Experiment: 1 Episode: 1564 | Score: -263.125 | Avg score: -214.812 |\n",
"| Experiment: 1 Episode: 1565 | Score: -291.981 | Avg score: -214.397 |\n",
"| Experiment: 1 Episode: 1566 | Score: 112.788 | Avg score: -210.677 |\n",
"| Experiment: 1 Episode: 1567 | Score: -384.660 | Avg score: -213.709 |\n",
"| Experiment: 1 Episode: 1568 | Score: -497.888 | Avg score: -215.806 |\n",
"| Experiment: 1 Episode: 1569 | Score: -111.259 | Avg score: -215.645 |\n",
"| Experiment: 1 Episode: 1570 | Score: -132.185 | Avg score: -216.168 |\n",
"| Experiment: 1 Episode: 1571 | Score: -264.716 | Avg score: -218.301 |\n",
"| Experiment: 1 Episode: 1572 | Score: -402.856 | Avg score: -221.264 |\n",
"| Experiment: 1 Episode: 1573 | Score: -415.330 | Avg score: -222.313 |\n",
"| Experiment: 1 Episode: 1574 | Score: -332.548 | Avg score: -224.474 |\n",
"| Experiment: 1 Episode: 1575 | Score: -94.217 | Avg score: -224.518 |\n",
"| Experiment: 1 Episode: 1576 | Score: -58.104 | Avg score: -223.766 |\n",
"| Experiment: 1 Episode: 1577 | Score: -378.301 | Avg score: -226.589 |\n",
"| Experiment: 1 Episode: 1578 | Score: -105.226 | Avg score: -225.779 |\n",
"| Experiment: 1 Episode: 1579 | Score: -73.713 | Avg score: -224.106 |\n",
"| Experiment: 1 Episode: 1580 | Score: -338.644 | Avg score: -224.072 |\n",
"| Experiment: 1 Episode: 1581 | Score: -251.848 | Avg score: -223.284 |\n",
"| Experiment: 1 Episode: 1582 | Score: -30.658 | Avg score: -221.441 |\n",
"| Experiment: 1 Episode: 1583 | Score: -207.467 | Avg score: -219.748 |\n",
"| Experiment: 1 Episode: 1584 | Score: -182.369 | Avg score: -217.251 |\n",
"| Experiment: 1 Episode: 1585 | Score: -426.684 | Avg score: -220.032 |\n",
"| Experiment: 1 Episode: 1586 | Score: -217.874 | Avg score: -219.260 |\n",
"| Experiment: 1 Episode: 1587 | Score: -77.727 | Avg score: -218.667 |\n",
"| Experiment: 1 Episode: 1588 | Score: -54.889 | Avg score: -218.388 |\n",
"| Experiment: 1 Episode: 1589 | Score: -114.919 | Avg score: -218.615 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 Episode: 1590 | Score: -382.852 | Avg score: -220.122 |\n",
"| Experiment: 1 Episode: 1591 | Score: -87.289 | Avg score: -217.175 |\n",
"| Experiment: 1 Episode: 1592 | Score: -384.234 | Avg score: -220.087 |\n",
"| Experiment: 1 Episode: 1593 | Score: -43.172 | Avg score: -219.178 |\n",
"| Experiment: 1 Episode: 1594 | Score: -286.587 | Avg score: -217.813 |\n",
"| Experiment: 1 Episode: 1595 | Score: -187.357 | Avg score: -219.134 |\n",
"| Experiment: 1 Episode: 1596 | Score: -159.601 | Avg score: -217.998 |\n",
"| Experiment: 1 Episode: 1597 | Score: -52.407 | Avg score: -217.340 |\n",
"| Experiment: 1 Episode: 1598 | Score: -308.844 | Avg score: -219.453 |\n",
"| Experiment: 1 Episode: 1599 | Score: -213.886 | Avg score: -221.372 |\n",
"| Experiment: 1 Episode: 1600 | Score: -110.498 | Avg score: -219.628 |\n",
"| Experiment: 1 Episode: 1601 | Score: -206.403 | Avg score: -220.482 |\n",
"| Experiment: 1 Episode: 1602 | Score: -106.722 | Avg score: -216.745 |\n",
"| Experiment: 1 Episode: 1603 | Score: -76.508 | Avg score: -214.684 |\n",
"| Experiment: 1 Episode: 1604 | Score: -84.353 | Avg score: -212.218 |\n",
"| Experiment: 1 Episode: 1605 | Score: -64.961 | Avg score: -211.095 |\n",
"| Experiment: 1 Episode: 1606 | Score: -379.101 | Avg score: -209.525 |\n",
"| Experiment: 1 Episode: 1607 | Score: -145.828 | Avg score: -209.255 |\n",
"| Experiment: 1 Episode: 1608 | Score: -302.606 | Avg score: -211.782 |\n",
"| Experiment: 1 Episode: 1609 | Score: -75.198 | Avg score: -210.098 |\n",
"| Experiment: 1 Episode: 1610 | Score: -74.854 | Avg score: -209.517 |\n",
"| Experiment: 1 Episode: 1611 | Score: -468.053 | Avg score: -213.234 |\n",
"| Experiment: 1 Episode: 1612 | Score: -61.288 | Avg score: -209.904 |\n",
"| Experiment: 1 Episode: 1613 | Score: -55.379 | Avg score: -206.729 |\n",
"| Experiment: 1 Episode: 1614 | Score: -66.578 | Avg score: -205.963 |\n",
"| Experiment: 1 Episode: 1615 | Score: -149.576 | Avg score: -206.214 |\n",
"| Experiment: 1 Episode: 1616 | Score: -158.458 | Avg score: -206.823 |\n",
"| Experiment: 1 Episode: 1617 | Score: -281.784 | Avg score: -207.902 |\n",
"| Experiment: 1 Episode: 1618 | Score: -281.550 | Avg score: -210.321 |\n",
"| Experiment: 1 Episode: 1619 | Score: -361.714 | Avg score: -212.684 |\n",
"| Experiment: 1 Episode: 1620 | Score: -272.431 | Avg score: -214.326 |\n",
"| Experiment: 1 Episode: 1621 | Score: -114.153 | Avg score: -212.240 |\n",
"| Experiment: 1 Episode: 1622 | Score: -609.477 | Avg score: -216.887 |\n",
"| Experiment: 1 Episode: 1623 | Score: -101.525 | Avg score: -215.417 |\n",
"| Experiment: 1 Episode: 1624 | Score: -311.500 | Avg score: -218.324 |\n",
"| Experiment: 1 Episode: 1625 | Score: -312.950 | Avg score: -219.816 |\n",
"| Experiment: 1 Episode: 1626 | Score: -98.302 | Avg score: -219.552 |\n",
"| Experiment: 1 Episode: 1627 | Score: -226.472 | Avg score: -221.158 |\n",
"| Experiment: 1 Episode: 1628 | Score: -62.676 | Avg score: -221.170 |\n",
"| Experiment: 1 Episode: 1629 | Score: -194.442 | Avg score: -220.810 |\n",
"| Experiment: 1 Episode: 1630 | Score: -308.219 | Avg score: -220.600 |\n",
"| Experiment: 1 Episode: 1631 | Score: -348.576 | Avg score: -222.552 |\n",
"| Experiment: 1 Episode: 1632 | Score: -63.600 | Avg score: -218.540 |\n",
"| Experiment: 1 Episode: 1633 | Score: -152.933 | Avg score: -216.684 |\n",
"| Experiment: 1 Episode: 1634 | Score: -294.050 | Avg score: -218.084 |\n",
"| Experiment: 1 Episode: 1635 | Score: -116.444 | Avg score: -216.634 |\n",
"| Experiment: 1 Episode: 1636 | Score: -307.426 | Avg score: -218.967 |\n",
"| Experiment: 1 Episode: 1637 | Score: -284.074 | Avg score: -219.479 |\n",
"| Experiment: 1 Episode: 1638 | Score: -64.089 | Avg score: -218.028 |\n",
"| Experiment: 1 Episode: 1639 | Score: -108.697 | Avg score: -218.055 |\n",
"| Experiment: 1 Episode: 1640 | Score: -73.977 | Avg score: -215.354 |\n",
"| Experiment: 1 Episode: 1641 | Score: -377.193 | Avg score: -216.760 |\n",
"| Experiment: 1 Episode: 1642 | Score: -118.760 | Avg score: -214.592 |\n",
"| Experiment: 1 Episode: 1643 | Score: -202.218 | Avg score: -213.545 |\n",
"| Experiment: 1 Episode: 1644 | Score: -136.541 | Avg score: -210.473 |\n",
"| Experiment: 1 Episode: 1645 | Score: -162.480 | Avg score: -208.278 |\n",
"| Experiment: 1 Episode: 1646 | Score: -386.698 | Avg score: -210.984 |\n",
"| Experiment: 1 Episode: 1647 | Score: -232.832 | Avg score: -210.414 |\n",
"| Experiment: 1 Episode: 1648 | Score: -415.605 | Avg score: -211.388 |\n",
"| Experiment: 1 Episode: 1649 | Score: -152.532 | Avg score: -208.903 |\n",
"| Experiment: 1 Episode: 1650 | Score: -249.754 | Avg score: -208.433 |\n",
"| Experiment: 1 Episode: 1651 | Score: -87.997 | Avg score: -207.730 |\n",
"| Experiment: 1 Episode: 1652 | Score: -249.070 | Avg score: -208.452 |\n",
"| Experiment: 1 Episode: 1653 | Score: -85.632 | Avg score: -208.465 |\n",
"| Experiment: 1 Episode: 1654 | Score: -97.685 | Avg score: -207.873 |\n",
"| Experiment: 1 Episode: 1655 | Score: -119.246 | Avg score: -208.117 |\n",
"| Experiment: 1 Episode: 1656 | Score: -259.848 | Avg score: -207.793 |\n",
"| Experiment: 1 Episode: 1657 | Score: -98.770 | Avg score: -205.416 |\n",
"| Experiment: 1 Episode: 1658 | Score: -332.585 | Avg score: -206.671 |\n",
"| Experiment: 1 Episode: 1659 | Score: -134.361 | Avg score: -206.012 |\n",
"| Experiment: 1 Episode: 1660 | Score: 124.192 | Avg score: -204.058 |\n",
"| Experiment: 1 Episode: 1661 | Score: -86.467 | Avg score: -203.886 |\n",
"| Experiment: 1 Episode: 1662 | Score: -104.115 | Avg score: -201.372 |\n",
"| Experiment: 1 Episode: 1663 | Score: -203.571 | Avg score: -198.098 |\n",
"| Experiment: 1 Episode: 1664 | Score: -148.535 | Avg score: -196.952 |\n",
"| Experiment: 1 Episode: 1665 | Score: -188.458 | Avg score: -195.917 |\n",
"| Experiment: 1 Episode: 1666 | Score: -293.429 | Avg score: -199.979 |\n",
"| Experiment: 1 Episode: 1667 | Score: -203.283 | Avg score: -198.165 |\n",
"| Experiment: 1 Episode: 1668 | Score: -226.619 | Avg score: -195.453 |\n",
"| Experiment: 1 Episode: 1669 | Score: -171.931 | Avg score: -196.060 |\n",
"| Experiment: 1 Episode: 1670 | Score: -277.301 | Avg score: -197.511 |\n",
"| Experiment: 1 Episode: 1671 | Score: -270.268 | Avg score: -197.566 |\n",
"| Experiment: 1 Episode: 1672 | Score: -291.916 | Avg score: -196.457 |\n",
"| Experiment: 1 Episode: 1673 | Score: -420.688 | Avg score: -196.510 |\n",
"| Experiment: 1 Episode: 1674 | Score: -369.779 | Avg score: -196.883 |\n",
"| Experiment: 1 Episode: 1675 | Score: -226.630 | Avg score: -198.207 |\n",
"| Experiment: 1 Episode: 1676 | Score: -211.699 | Avg score: -199.743 |\n",
"| Experiment: 1 Episode: 1677 | Score: -350.146 | Avg score: -199.461 |\n",
"| Experiment: 1 Episode: 1678 | Score: -317.188 | Avg score: -201.581 |\n",
"| Experiment: 1 Episode: 1679 | Score: -201.703 | Avg score: -202.861 |\n",
"| Experiment: 1 Episode: 1680 | Score: -263.136 | Avg score: -202.106 |\n",
"| Experiment: 1 Episode: 1681 | Score: -118.553 | Avg score: -200.773 |\n",
"| Experiment: 1 Episode: 1682 | Score: -131.732 | Avg score: -201.783 |\n",
"| Experiment: 1 Episode: 1683 | Score: -245.688 | Avg score: -202.166 |\n",
"| Experiment: 1 Episode: 1684 | Score: -80.259 | Avg score: -201.145 |\n",
"| Experiment: 1 Episode: 1685 | Score: -336.757 | Avg score: -200.245 |\n",
"| Experiment: 1 Episode: 1686 | Score: -250.485 | Avg score: -200.571 |\n",
"| Experiment: 1 Episode: 1687 | Score: -249.002 | Avg score: -202.284 |\n",
"| Experiment: 1 Episode: 1688 | Score: -356.207 | Avg score: -205.297 |\n",
"| Experiment: 1 Episode: 1689 | Score: -140.253 | Avg score: -205.551 |\n",
"| Experiment: 1 Episode: 1690 | Score: -342.223 | Avg score: -205.144 |\n",
"| Experiment: 1 Episode: 1691 | Score: -129.231 | Avg score: -205.564 |\n",
"| Experiment: 1 Episode: 1692 | Score: -75.008 | Avg score: -202.472 |\n",
"| Experiment: 1 Episode: 1693 | Score: -69.344 | Avg score: -202.733 |\n",
"| Experiment: 1 Episode: 1694 | Score: -95.003 | Avg score: -200.817 |\n",
"| Experiment: 1 Episode: 1695 | Score: -76.476 | Avg score: -199.709 |\n",
"| Experiment: 1 Episode: 1696 | Score: -260.465 | Avg score: -200.717 |\n",
"| Experiment: 1 Episode: 1697 | Score: -145.690 | Avg score: -201.650 |\n",
"| Experiment: 1 Episode: 1698 | Score: -99.271 | Avg score: -199.554 |\n",
"| Experiment: 1 Episode: 1699 | Score: -67.903 | Avg score: -198.094 |\n",
"| Experiment: 1 Episode: 1700 | Score: -215.186 | Avg score: -199.141 |\n",
"| Experiment: 1 Episode: 1701 | Score: -161.238 | Avg score: -198.690 |\n",
"| Experiment: 1 Episode: 1702 | Score: -302.004 | Avg score: -200.643 |\n",
"| Experiment: 1 Episode: 1703 | Score: -328.825 | Avg score: -203.166 |\n",
"| Experiment: 1 Episode: 1704 | Score: -242.126 | Avg score: -204.743 |\n",
"| Experiment: 1 Episode: 1705 | Score: -135.467 | Avg score: -205.449 |\n",
"| Experiment: 1 Episode: 1706 | Score: -434.010 | Avg score: -205.998 |\n",
"| Experiment: 1 Episode: 1707 | Score: -112.957 | Avg score: -205.669 |\n",
"| Experiment: 1 Episode: 1708 | Score: -409.287 | Avg score: -206.736 |\n",
"| Experiment: 1 Episode: 1709 | Score: -87.637 | Avg score: -206.860 |\n",
"| Experiment: 1 Episode: 1710 | Score: -261.164 | Avg score: -208.723 |\n",
"| Experiment: 1 Episode: 1711 | Score: -381.648 | Avg score: -207.859 |\n",
"| Experiment: 1 Episode: 1712 | Score: -374.805 | Avg score: -210.994 |\n",
"| Experiment: 1 Episode: 1713 | Score: -367.143 | Avg score: -214.112 |\n",
"| Experiment: 1 Episode: 1714 | Score: -134.314 | Avg score: -214.789 |\n",
"| Experiment: 1 Episode: 1715 | Score: -88.479 | Avg score: -214.178 |\n",
"| Experiment: 1 Episode: 1716 | Score: -128.965 | Avg score: -213.883 |\n",
"| Experiment: 1 Episode: 1717 | Score: -278.321 | Avg score: -213.849 |\n",
"| Experiment: 1 Episode: 1718 | Score: -340.573 | Avg score: -214.439 |\n",
"| Experiment: 1 Episode: 1719 | Score: -111.424 | Avg score: -211.936 |\n",
"| Experiment: 1 Episode: 1720 | Score: -169.745 | Avg score: -210.909 |\n",
"| Experiment: 1 Episode: 1721 | Score: -327.134 | Avg score: -213.039 |\n",
"| Experiment: 1 Episode: 1722 | Score: -258.226 | Avg score: -209.527 |\n",
"| Experiment: 1 Episode: 1723 | Score: -92.029 | Avg score: -209.432 |\n",
"| Experiment: 1 Episode: 1724 | Score: -503.401 | Avg score: -211.351 |\n",
"| Experiment: 1 Episode: 1725 | Score: -174.735 | Avg score: -209.968 |\n",
"| Experiment: 1 Episode: 1726 | Score: -131.469 | Avg score: -210.300 |\n",
"| Experiment: 1 Episode: 1727 | Score: -443.420 | Avg score: -212.470 |\n",
"| Experiment: 1 Episode: 1728 | Score: -130.971 | Avg score: -213.153 |\n",
"| Experiment: 1 Episode: 1729 | Score: -262.973 | Avg score: -213.838 |\n",
"| Experiment: 1 Episode: 1730 | Score: -324.531 | Avg score: -214.001 |\n",
"| Experiment: 1 Episode: 1731 | Score: -106.163 | Avg score: -211.577 |\n",
"| Experiment: 1 Episode: 1732 | Score: -28.041 | Avg score: -211.221 |\n",
"| Experiment: 1 Episode: 1733 | Score: -270.031 | Avg score: -212.392 |\n",
"| Experiment: 1 Episode: 1734 | Score: -114.306 | Avg score: -210.595 |\n",
"| Experiment: 1 Episode: 1735 | Score: -339.986 | Avg score: -212.830 |\n",
"| Experiment: 1 Episode: 1736 | Score: -307.940 | Avg score: -212.835 |\n",
"| Experiment: 1 Episode: 1737 | Score: -234.615 | Avg score: -212.341 |\n",
"| Experiment: 1 Episode: 1738 | Score: -147.389 | Avg score: -213.174 |\n",
"| Experiment: 1 Episode: 1739 | Score: -35.727 | Avg score: -212.444 |\n",
"| Experiment: 1 Episode: 1740 | Score: -357.916 | Avg score: -215.283 |\n",
"| Experiment: 1 Episode: 1741 | Score: -323.280 | Avg score: -214.744 |\n",
"| Experiment: 1 Episode: 1742 | Score: -84.280 | Avg score: -214.399 |\n",
"| Experiment: 1 Episode: 1743 | Score: -289.505 | Avg score: -215.272 |\n",
"| Experiment: 1 Episode: 1744 | Score: -172.499 | Avg score: -215.632 |\n",
"| Experiment: 1 Episode: 1745 | Score: -427.961 | Avg score: -218.287 |\n",
"| Experiment: 1 Episode: 1746 | Score: -80.595 | Avg score: -215.226 |\n",
"| Experiment: 1 Episode: 1747 | Score: -360.144 | Avg score: -216.499 |\n",
"| Experiment: 1 Episode: 1748 | Score: -73.873 | Avg score: -213.082 |\n",
"| Experiment: 1 Episode: 1749 | Score: -460.073 | Avg score: -216.157 |\n",
"| Experiment: 1 Episode: 1750 | Score: -254.277 | Avg score: -216.202 |\n",
"| Experiment: 1 Episode: 1751 | Score: -426.396 | Avg score: -219.586 |\n",
"| Experiment: 1 Episode: 1752 | Score: -121.552 | Avg score: -218.311 |\n",
"| Experiment: 1 Episode: 1753 | Score: -154.564 | Avg score: -219.000 |\n",
"| Experiment: 1 Episode: 1754 | Score: -156.469 | Avg score: -219.588 |\n",
"| Experiment: 1 Episode: 1755 | Score: -47.711 | Avg score: -218.873 |\n",
"| Experiment: 1 Episode: 1756 | Score: -265.516 | Avg score: -218.929 |\n",
"| Experiment: 1 Episode: 1757 | Score: -353.995 | Avg score: -221.482 |\n",
"| Experiment: 1 Episode: 1758 | Score: -110.404 | Avg score: -219.260 |\n",
"| Experiment: 1 Episode: 1759 | Score: -231.422 | Avg score: -220.231 |\n",
"| Experiment: 1 Episode: 1760 | Score: -39.010 | Avg score: -221.863 |\n",
"| Experiment: 1 Episode: 1761 | Score: -440.218 | Avg score: -225.400 |\n",
"| Experiment: 1 Episode: 1762 | Score: -135.362 | Avg score: -225.713 |\n",
"| Experiment: 1 Episode: 1763 | Score: -141.375 | Avg score: -225.091 |\n",
"| Experiment: 1 Episode: 1764 | Score: -265.517 | Avg score: -226.260 |\n",
"| Experiment: 1 Episode: 1765 | Score: -353.804 | Avg score: -227.914 |\n",
"| Experiment: 1 Episode: 1766 | Score: -152.567 | Avg score: -226.505 |\n",
"| Experiment: 1 Episode: 1767 | Score: -73.114 | Avg score: -225.204 |\n",
"| Experiment: 1 Episode: 1768 | Score: -319.460 | Avg score: -226.132 |\n",
"| Experiment: 1 Episode: 1769 | Score: -375.816 | Avg score: -228.171 |\n",
"| Experiment: 1 Episode: 1770 | Score: -83.495 | Avg score: -226.233 |\n",
"| Experiment: 1 Episode: 1771 | Score: -270.569 | Avg score: -226.236 |\n",
"| Experiment: 1 Episode: 1772 | Score: -171.574 | Avg score: -225.032 |\n",
"| Experiment: 1 Episode: 1773 | Score: -277.888 | Avg score: -223.604 |\n",
"| Experiment: 1 Episode: 1774 | Score: -349.533 | Avg score: -223.402 |\n",
"| Experiment: 1 Episode: 1775 | Score: -358.982 | Avg score: -224.725 |\n",
"| Experiment: 1 Episode: 1776 | Score: -315.380 | Avg score: -225.762 |\n",
"| Experiment: 1 Episode: 1777 | Score: -414.490 | Avg score: -226.406 |\n",
"| Experiment: 1 Episode: 1778 | Score: -320.216 | Avg score: -226.436 |\n",
"| Experiment: 1 Episode: 1779 | Score: -231.324 | Avg score: -226.732 |\n",
"| Experiment: 1 Episode: 1780 | Score: -238.630 | Avg score: -226.487 |\n",
"| Experiment: 1 Episode: 1781 | Score: -273.682 | Avg score: -228.038 |\n",
"| Experiment: 1 Episode: 1782 | Score: -346.415 | Avg score: -230.185 |\n",
"| Experiment: 1 Episode: 1783 | Score: -474.359 | Avg score: -232.472 |\n",
"| Experiment: 1 Episode: 1784 | Score: -26.610 | Avg score: -231.935 |\n",
"| Experiment: 1 Episode: 1785 | Score: -126.246 | Avg score: -229.830 |\n",
"| Experiment: 1 Episode: 1786 | Score: -40.701 | Avg score: -227.732 |\n",
"| Experiment: 1 Episode: 1787 | Score: -298.356 | Avg score: -228.226 |\n",
"| Experiment: 1 Episode: 1788 | Score: -102.040 | Avg score: -225.684 |\n",
"| Experiment: 1 Episode: 1789 | Score: -281.099 | Avg score: -227.093 |\n",
"| Experiment: 1 Episode: 1790 | Score: -469.268 | Avg score: -228.363 |\n",
"| Experiment: 1 Episode: 1791 | Score: -99.611 | Avg score: -228.067 |\n",
"| Experiment: 1 Episode: 1792 | Score: -73.352 | Avg score: -228.051 |\n",
"| Experiment: 1 Episode: 1793 | Score: -348.951 | Avg score: -230.847 |\n",
"| Experiment: 1 Episode: 1794 | Score: -80.886 | Avg score: -230.705 |\n",
"| Experiment: 1 Episode: 1795 | Score: -312.311 | Avg score: -233.064 |\n",
"| Experiment: 1 Episode: 1796 | Score: -348.587 | Avg score: -233.945 |\n",
"| Experiment: 1 Episode: 1797 | Score: -9.904 | Avg score: -232.587 |\n",
"| Experiment: 1 Episode: 1798 | Score: -96.107 | Avg score: -232.555 |\n",
"| Experiment: 1 Episode: 1799 | Score: -53.941 | Avg score: -232.416 |\n",
"| Experiment: 1 Episode: 1800 | Score: -367.861 | Avg score: -233.943 |\n",
"| Experiment: 1 Episode: 1801 | Score: -379.059 | Avg score: -236.121 |\n",
"| Experiment: 1 Episode: 1802 | Score: -142.407 | Avg score: -234.525 |\n",
"| Experiment: 1 Episode: 1803 | Score: -256.992 | Avg score: -233.807 |\n",
"| Experiment: 1 Episode: 1804 | Score: -50.358 | Avg score: -231.889 |\n",
"| Experiment: 1 Episode: 1805 | Score: -352.407 | Avg score: -234.058 |\n",
"| Experiment: 1 Episode: 1806 | Score: -393.240 | Avg score: -233.651 |\n",
"| Experiment: 1 Episode: 1807 | Score: -290.600 | Avg score: -235.427 |\n",
"| Experiment: 1 Episode: 1808 | Score: -211.744 | Avg score: -233.452 |\n",
"| Experiment: 1 Episode: 1809 | Score: -120.213 | Avg score: -233.777 |\n",
"| Experiment: 1 Episode: 1810 | Score: -179.520 | Avg score: -232.961 |\n",
"| Experiment: 1 Episode: 1811 | Score: -113.509 | Avg score: -230.279 |\n",
"| Experiment: 1 Episode: 1812 | Score: -180.017 | Avg score: -228.332 |\n",
"| Experiment: 1 Episode: 1813 | Score: -151.452 | Avg score: -226.175 |\n",
"| Experiment: 1 Episode: 1814 | Score: -85.947 | Avg score: -225.691 |\n",
"| Experiment: 1 Episode: 1815 | Score: -483.267 | Avg score: -229.639 |\n",
"| Experiment: 1 Episode: 1816 | Score: -197.607 | Avg score: -230.325 |\n",
"| Experiment: 1 Episode: 1817 | Score: -102.630 | Avg score: -228.568 |\n",
"| Experiment: 1 Episode: 1818 | Score: -293.436 | Avg score: -228.097 |\n",
"| Experiment: 1 Episode: 1819 | Score: -93.887 | Avg score: -227.922 |\n",
"| Experiment: 1 Episode: 1820 | Score: -159.463 | Avg score: -227.819 |\n",
"| Experiment: 1 Episode: 1821 | Score: -315.853 | Avg score: -227.706 |\n",
"| Experiment: 1 Episode: 1822 | Score: -131.274 | Avg score: -226.436 |\n",
"| Experiment: 1 Episode: 1823 | Score: -152.314 | Avg score: -227.039 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 Episode: 1824 | Score: -78.482 | Avg score: -222.790 |\n",
"| Experiment: 1 Episode: 1825 | Score: -100.943 | Avg score: -222.052 |\n",
"| Experiment: 1 Episode: 1826 | Score: -296.016 | Avg score: -223.698 |\n",
"| Experiment: 1 Episode: 1827 | Score: -223.729 | Avg score: -221.501 |\n",
"| Experiment: 1 Episode: 1828 | Score: -267.940 | Avg score: -222.870 |\n",
"| Experiment: 1 Episode: 1829 | Score: -95.150 | Avg score: -221.192 |\n",
"| Experiment: 1 Episode: 1830 | Score: -126.223 | Avg score: -219.209 |\n",
"| Experiment: 1 Episode: 1831 | Score: -407.068 | Avg score: -222.218 |\n",
"| Experiment: 1 Episode: 1832 | Score: -264.012 | Avg score: -224.578 |\n",
"| Experiment: 1 Episode: 1833 | Score: -203.856 | Avg score: -223.916 |\n",
"| Experiment: 1 Episode: 1834 | Score: -167.934 | Avg score: -224.452 |\n",
"| Experiment: 1 Episode: 1835 | Score: -245.799 | Avg score: -223.511 |\n",
"| Experiment: 1 Episode: 1836 | Score: -249.069 | Avg score: -222.922 |\n",
"| Experiment: 1 Episode: 1837 | Score: -225.251 | Avg score: -222.828 |\n",
"| Experiment: 1 Episode: 1838 | Score: -318.086 | Avg score: -224.535 |\n",
"| Experiment: 1 Episode: 1839 | Score: -58.053 | Avg score: -224.759 |\n",
"| Experiment: 1 Episode: 1840 | Score: -171.168 | Avg score: -222.891 |\n",
"| Experiment: 1 Episode: 1841 | Score: -225.820 | Avg score: -221.916 |\n",
"| Experiment: 1 Episode: 1842 | Score: -316.886 | Avg score: -224.243 |\n",
"| Experiment: 1 Episode: 1843 | Score: -141.879 | Avg score: -222.766 |\n",
"| Experiment: 1 Episode: 1844 | Score: -228.770 | Avg score: -223.329 |\n",
"| Experiment: 1 Episode: 1845 | Score: -385.979 | Avg score: -222.909 |\n",
"| Experiment: 1 Episode: 1846 | Score: -446.215 | Avg score: -226.565 |\n",
"| Experiment: 1 Episode: 1847 | Score: -259.339 | Avg score: -225.557 |\n",
"| Experiment: 1 Episode: 1848 | Score: -99.048 | Avg score: -225.809 |\n",
"| Experiment: 1 Episode: 1849 | Score: -294.480 | Avg score: -224.153 |\n",
"| Experiment: 1 Episode: 1850 | Score: -122.106 | Avg score: -222.831 |\n",
"| Experiment: 1 Episode: 1851 | Score: -80.045 | Avg score: -219.368 |\n",
"| Experiment: 1 Episode: 1852 | Score: -366.433 | Avg score: -221.817 |\n",
"| Experiment: 1 Episode: 1853 | Score: -357.461 | Avg score: -223.846 |\n",
"| Experiment: 1 Episode: 1854 | Score: -382.995 | Avg score: -226.111 |\n",
"| Experiment: 1 Episode: 1855 | Score: -462.966 | Avg score: -230.263 |\n",
"| Experiment: 1 Episode: 1856 | Score: -84.783 | Avg score: -228.456 |\n",
"| Experiment: 1 Episode: 1857 | Score: -52.869 | Avg score: -225.445 |\n",
"| Experiment: 1 Episode: 1858 | Score: -317.921 | Avg score: -227.520 |\n",
"| Experiment: 1 Episode: 1859 | Score: -38.825 | Avg score: -225.594 |\n",
"| Experiment: 1 Episode: 1860 | Score: -134.658 | Avg score: -226.551 |\n",
"| Experiment: 1 Episode: 1861 | Score: -339.724 | Avg score: -225.546 |\n",
"| Experiment: 1 Episode: 1862 | Score: -223.602 | Avg score: -226.428 |\n",
"| Experiment: 1 Episode: 1863 | Score: -130.417 | Avg score: -226.318 |\n",
"| Experiment: 1 Episode: 1864 | Score: -451.667 | Avg score: -228.180 |\n",
"| Experiment: 1 Episode: 1865 | Score: -515.110 | Avg score: -229.793 |\n",
"| Experiment: 1 Episode: 1866 | Score: -331.629 | Avg score: -231.584 |\n",
"| Experiment: 1 Episode: 1867 | Score: -81.519 | Avg score: -231.668 |\n",
"| Experiment: 1 Episode: 1868 | Score: -211.058 | Avg score: -230.584 |\n",
"| Experiment: 1 Episode: 1869 | Score: -64.377 | Avg score: -227.469 |\n",
"| Experiment: 1 Episode: 1870 | Score: -286.196 | Avg score: -229.496 |\n",
"| Experiment: 1 Episode: 1871 | Score: -4.640 | Avg score: -226.837 |\n",
"| Experiment: 1 Episode: 1872 | Score: -412.954 | Avg score: -229.251 |\n",
"| Experiment: 1 Episode: 1873 | Score: -265.060 | Avg score: -229.122 |\n",
"| Experiment: 1 Episode: 1874 | Score: -85.812 | Avg score: -226.485 |\n",
"| Experiment: 1 Episode: 1875 | Score: -121.778 | Avg score: -224.113 |\n",
"| Experiment: 1 Episode: 1876 | Score: -196.315 | Avg score: -222.923 |\n",
"| Experiment: 1 Episode: 1877 | Score: -327.240 | Avg score: -222.050 |\n",
"| Experiment: 1 Episode: 1878 | Score: -250.860 | Avg score: -221.356 |\n",
"| Experiment: 1 Episode: 1879 | Score: -311.435 | Avg score: -222.158 |\n",
"| Experiment: 1 Episode: 1880 | Score: -297.270 | Avg score: -222.744 |\n",
"| Experiment: 1 Episode: 1881 | Score: -77.241 | Avg score: -220.780 |\n",
"| Experiment: 1 Episode: 1882 | Score: -132.928 | Avg score: -218.645 |\n",
"| Experiment: 1 Episode: 1883 | Score: -97.001 | Avg score: -214.871 |\n",
"| Experiment: 1 Episode: 1884 | Score: -248.430 | Avg score: -217.089 |\n",
"| Experiment: 1 Episode: 1885 | Score: -204.153 | Avg score: -217.868 |\n",
"| Experiment: 1 Episode: 1886 | Score: -177.502 | Avg score: -219.236 |\n",
"| Experiment: 1 Episode: 1887 | Score: -336.765 | Avg score: -219.620 |\n",
"| Experiment: 1 Episode: 1888 | Score: -168.722 | Avg score: -220.287 |\n",
"| Experiment: 1 Episode: 1889 | Score: -251.688 | Avg score: -219.993 |\n",
"| Experiment: 1 Episode: 1890 | Score: -207.741 | Avg score: -217.378 |\n",
"| Experiment: 1 Episode: 1891 | Score: -446.362 | Avg score: -220.845 |\n",
"| Experiment: 1 Episode: 1892 | Score: -68.508 | Avg score: -220.797 |\n",
"| Experiment: 1 Episode: 1893 | Score: -122.738 | Avg score: -218.535 |\n",
"| Experiment: 1 Episode: 1894 | Score: -69.433 | Avg score: -218.420 |\n",
"| Experiment: 1 Episode: 1895 | Score: -68.338 | Avg score: -215.981 |\n",
"| Experiment: 1 Episode: 1896 | Score: -229.199 | Avg score: -214.787 |\n",
"| Experiment: 1 Episode: 1897 | Score: -79.041 | Avg score: -215.478 |\n",
"| Experiment: 1 Episode: 1898 | Score: -306.611 | Avg score: -217.583 |\n",
"| Experiment: 1 Episode: 1899 | Score: -339.194 | Avg score: -220.436 |\n",
"| Experiment: 1 Episode: 1900 | Score: -281.693 | Avg score: -219.574 |\n",
"| Experiment: 1 Episode: 1901 | Score: -108.893 | Avg score: -216.872 |\n",
"| Experiment: 1 Episode: 1902 | Score: -480.270 | Avg score: -220.251 |\n",
"| Experiment: 1 Episode: 1903 | Score: -351.792 | Avg score: -221.199 |\n",
"| Experiment: 1 Episode: 1904 | Score: -124.344 | Avg score: -221.939 |\n",
"| Experiment: 1 Episode: 1905 | Score: -339.558 | Avg score: -221.810 |\n",
"| Experiment: 1 Episode: 1906 | Score: -429.544 | Avg score: -222.173 |\n",
"| Experiment: 1 Episode: 1907 | Score: -194.288 | Avg score: -221.210 |\n",
"| Experiment: 1 Episode: 1908 | Score: -313.030 | Avg score: -222.223 |\n",
"| Experiment: 1 Episode: 1909 | Score: -75.119 | Avg score: -221.772 |\n",
"| Experiment: 1 Episode: 1910 | Score: -76.963 | Avg score: -220.747 |\n",
"| Experiment: 1 Episode: 1911 | Score: -59.217 | Avg score: -220.204 |\n",
"| Experiment: 1 Episode: 1912 | Score: -407.591 | Avg score: -222.479 |\n",
"| Experiment: 1 Episode: 1913 | Score: -271.681 | Avg score: -223.682 |\n",
"| Experiment: 1 Episode: 1914 | Score: -284.448 | Avg score: -225.667 |\n",
"| Experiment: 1 Episode: 1915 | Score: -422.069 | Avg score: -225.055 |\n",
"| Experiment: 1 Episode: 1916 | Score: -88.973 | Avg score: -223.968 |\n",
"| Experiment: 1 Episode: 1917 | Score: -376.324 | Avg score: -226.705 |\n",
"| Experiment: 1 Episode: 1918 | Score: -119.479 | Avg score: -224.966 |\n",
"| Experiment: 1 Episode: 1919 | Score: -470.211 | Avg score: -228.729 |\n",
"| Experiment: 1 Episode: 1920 | Score: -59.387 | Avg score: -227.728 |\n",
"| Experiment: 1 Episode: 1921 | Score: -459.649 | Avg score: -229.166 |\n",
"| Experiment: 1 Episode: 1922 | Score: -82.425 | Avg score: -228.678 |\n",
"| Experiment: 1 Episode: 1923 | Score: -336.355 | Avg score: -230.518 |\n",
"| Experiment: 1 Episode: 1924 | Score: -102.053 | Avg score: -230.754 |\n",
"| Experiment: 1 Episode: 1925 | Score: -325.185 | Avg score: -232.996 |\n",
"| Experiment: 1 Episode: 1926 | Score: -29.824 | Avg score: -230.334 |\n",
"| Experiment: 1 Episode: 1927 | Score: -78.337 | Avg score: -228.880 |\n",
"| Experiment: 1 Episode: 1928 | Score: -337.678 | Avg score: -229.578 |\n",
"| Experiment: 1 Episode: 1929 | Score: -369.001 | Avg score: -232.316 |\n",
"| Experiment: 1 Episode: 1930 | Score: -301.935 | Avg score: -234.073 |\n",
"| Experiment: 1 Episode: 1931 | Score: -180.410 | Avg score: -231.807 |\n",
"| Experiment: 1 Episode: 1932 | Score: -99.295 | Avg score: -230.160 |\n",
"| Experiment: 1 Episode: 1933 | Score: -180.006 | Avg score: -229.921 |\n",
"| Experiment: 1 Episode: 1934 | Score: -93.884 | Avg score: -229.181 |\n",
"| Experiment: 1 Episode: 1935 | Score: -338.440 | Avg score: -230.107 |\n",
"| Experiment: 1 Episode: 1936 | Score: -243.195 | Avg score: -230.048 |\n",
"| Experiment: 1 Episode: 1937 | Score: -116.159 | Avg score: -228.957 |\n",
"| Experiment: 1 Episode: 1938 | Score: -396.006 | Avg score: -229.737 |\n",
"| Experiment: 1 Episode: 1939 | Score: -81.282 | Avg score: -229.969 |\n",
"| Experiment: 1 Episode: 1940 | Score: -215.638 | Avg score: -230.414 |\n",
"| Experiment: 1 Episode: 1941 | Score: -86.458 | Avg score: -229.020 |\n",
"| Experiment: 1 Episode: 1942 | Score: -102.713 | Avg score: -226.878 |\n",
"| Experiment: 1 Episode: 1943 | Score: -107.280 | Avg score: -226.532 |\n",
"| Experiment: 1 Episode: 1944 | Score: -148.534 | Avg score: -225.730 |\n",
"| Experiment: 1 Episode: 1945 | Score: -45.228 | Avg score: -222.322 |\n",
"| Experiment: 1 Episode: 1946 | Score: -321.326 | Avg score: -221.074 |\n",
"| Experiment: 1 Episode: 1947 | Score: -405.393 | Avg score: -222.534 |\n",
"| Experiment: 1 Episode: 1948 | Score: -259.959 | Avg score: -224.143 |\n",
"| Experiment: 1 Episode: 1949 | Score: -148.092 | Avg score: -222.679 |\n",
"| Experiment: 1 Episode: 1950 | Score: -131.158 | Avg score: -222.770 |\n",
"| Experiment: 1 Episode: 1951 | Score: -197.221 | Avg score: -223.942 |\n",
"| Experiment: 1 Episode: 1952 | Score: -285.393 | Avg score: -223.131 |\n",
"| Experiment: 1 Episode: 1953 | Score: -279.783 | Avg score: -222.354 |\n",
"| Experiment: 1 Episode: 1954 | Score: -357.592 | Avg score: -222.100 |\n",
"| Experiment: 1 Episode: 1955 | Score: -95.181 | Avg score: -218.423 |\n",
"| Experiment: 1 Episode: 1956 | Score: -207.852 | Avg score: -219.653 |\n",
"| Experiment: 1 Episode: 1957 | Score: -205.868 | Avg score: -221.183 |\n",
"| Experiment: 1 Episode: 1958 | Score: 145.221 | Avg score: -216.552 |\n",
"| Experiment: 1 Episode: 1959 | Score: -199.126 | Avg score: -218.155 |\n",
"| Experiment: 1 Episode: 1960 | Score: -125.631 | Avg score: -218.065 |\n",
"| Experiment: 1 Episode: 1961 | Score: -571.937 | Avg score: -220.387 |\n",
"| Experiment: 1 Episode: 1962 | Score: -189.552 | Avg score: -220.046 |\n",
"| Experiment: 1 Episode: 1963 | Score: -252.121 | Avg score: -221.263 |\n",
"| Experiment: 1 Episode: 1964 | Score: -86.426 | Avg score: -217.611 |\n",
"| Experiment: 1 Episode: 1965 | Score: -115.547 | Avg score: -213.615 |\n",
"| Experiment: 1 Episode: 1966 | Score: -104.185 | Avg score: -211.341 |\n",
"| Experiment: 1 Episode: 1967 | Score: -334.734 | Avg score: -213.873 |\n",
"| Experiment: 1 Episode: 1968 | Score: -122.347 | Avg score: -212.986 |\n",
"| Experiment: 1 Episode: 1969 | Score: -354.956 | Avg score: -215.892 |\n",
"| Experiment: 1 Episode: 1970 | Score: -36.323 | Avg score: -213.393 |\n",
"| Experiment: 1 Episode: 1971 | Score: -259.931 | Avg score: -215.946 |\n",
"| Experiment: 1 Episode: 1972 | Score: -266.308 | Avg score: -214.479 |\n",
"| Experiment: 1 Episode: 1973 | Score: -77.704 | Avg score: -212.606 |\n",
"| Experiment: 1 Episode: 1974 | Score: -119.229 | Avg score: -212.940 |\n",
"| Experiment: 1 Episode: 1975 | Score: -449.120 | Avg score: -216.213 |\n",
"| Experiment: 1 Episode: 1976 | Score: -174.712 | Avg score: -215.997 |\n",
"| Experiment: 1 Episode: 1977 | Score: -68.550 | Avg score: -213.410 |\n",
"| Experiment: 1 Episode: 1978 | Score: 7.728 | Avg score: -210.825 |\n",
"| Experiment: 1 Episode: 1979 | Score: -342.139 | Avg score: -211.132 |\n",
"| Experiment: 1 Episode: 1980 | Score: -104.466 | Avg score: -209.204 |\n",
"| Experiment: 1 Episode: 1981 | Score: -109.626 | Avg score: -209.527 |\n",
"| Experiment: 1 Episode: 1982 | Score: -362.528 | Avg score: -211.823 |\n",
"| Experiment: 1 Episode: 1983 | Score: -310.443 | Avg score: -213.958 |\n",
"| Experiment: 1 Episode: 1984 | Score: -144.138 | Avg score: -212.915 |\n",
"| Experiment: 1 Episode: 1985 | Score: -259.749 | Avg score: -213.471 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 Episode: 1986 | Score: -416.018 | Avg score: -215.856 |\n",
"| Experiment: 1 Episode: 1987 | Score: -80.516 | Avg score: -213.294 |\n",
"| Experiment: 1 Episode: 1988 | Score: -314.329 | Avg score: -214.750 |\n",
"| Experiment: 1 Episode: 1989 | Score: -328.110 | Avg score: -215.514 |\n",
"| Experiment: 1 Episode: 1990 | Score: -53.193 | Avg score: -213.968 |\n",
"| Experiment: 1 Episode: 1991 | Score: -0.946 | Avg score: -209.514 |\n",
"| Experiment: 1 Episode: 1992 | Score: -99.942 | Avg score: -209.829 |\n",
"| Experiment: 1 Episode: 1993 | Score: -45.437 | Avg score: -209.056 |\n",
"| Experiment: 1 Episode: 1994 | Score: -40.796 | Avg score: -208.769 |\n",
"| Experiment: 1 Episode: 1995 | Score: -202.090 | Avg score: -210.107 |\n",
"| Experiment: 1 Episode: 1996 | Score: -389.727 | Avg score: -211.712 |\n",
"| Experiment: 1 Episode: 1997 | Score: -108.773 | Avg score: -212.009 |\n",
"| Experiment: 1 Episode: 1998 | Score: -325.037 | Avg score: -212.194 |\n",
"| Experiment: 1 Episode: 1999 | Score: -66.185 | Avg score: -209.463 |\n"
]
}
],
"source": [
"scores = []\n",
"avg_scores = []\n",
"std_scores = []\n",
"\n",
"n_experiments = 2\n",
"n_episodes = 2000\n",
"max_steps = 300\n",
"\n",
"env = gym.make(\n",
" \"LunarLander-v2\",\n",
" continuous=True,\n",
" gravity=-10.0,\n",
" enable_wind=False,\n",
" wind_power=0.0,\n",
" turbulence_power=0.)\n",
"\n",
"\n",
"for ex_i in range(n_experiments):\n",
" scores.append([])\n",
" avg_scores.append([])\n",
" std_scores.append([])\n",
" for episode in range(n_episodes):\n",
" observation, info = env.reset()\n",
" score = 0\n",
" step = 0\n",
" done = False\n",
" \n",
" while not done:\n",
" step += 1\n",
" action = env.action_space.sample()\n",
" observation_, reward, terminated, truncated, info = env.step(action)\n",
" observation = observation_\n",
" score += reward\n",
" if truncated or terminated or step==max_steps:\n",
" done = True\n",
" scores[ex_i].append(score)\n",
" avg_scores[ex_i].append(np.mean(scores[ex_i][-100:]))\n",
" std_scores[ex_i].append(np.std(scores[ex_i][-100:]))\n",
" print(f'| Experiment: {ex_i:4} Episode: {episode:4} | Score: {score:2.3f} | Avg score: {avg_scores[ex_i][-1]:2.3f} |')\n",
"env.close()\n"
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "273d6627",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_results(avg_scores, std_scores)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "46377346",
"metadata": {},
"outputs": [],
"source": [
"# save the random agent results for future comparison\n",
"results_data_dict[\"Random\"] = [avg_scores, std_scores]"
]
},
{
"attachments": {
"REINFORCE_algorithm.png": {
"image/png": 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"
}
},
"cell_type": "markdown",
"id": "189ab55a",
"metadata": {},
"source": [
"## REINFORCE with continuous action space\n",
"Now we implement the REINFORCE algorithm as it was introduced in the Reinforcement Learning - An Introduction book (Chapter 13.3) in the case of a continuous action space (Chapter 13.7). The REINFORCE algorithm is specified in the figure below\n",
"\n",
"\n",
"So let's get started"
]
},
{
"cell_type": "markdown",
"id": "4b15a751",
"metadata": {},
"source": [
"### The Policy Network\n",
"For the case of a continuous action space, the policy network doesn't output a vector of probabilities, instead, it outputs the two moments $\\mu, \\sigma$ of a Gaussian distribution which we then sample for the agent's action."
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "544483ac",
"metadata": {},
"outputs": [],
"source": [
"class PolicyNetwork(nn.Module):\n",
" def __init__(self, observation_dim, action_dim, learning_rate):\n",
" super(PolicyNetwork, self).__init__()\n",
" self.fc1 = nn.Linear(in_features=observation_dim, out_features=256)\n",
" self.fc2 = nn.Linear(in_features=256, out_features=256)\n",
" self.mu = nn.Linear(in_features=256, out_features=action_dim)\n",
" self.sigma = nn.Linear(in_features=256, out_features=action_dim)\n",
" \n",
" self.optimizer = T.optim.Adam(params=self.parameters(), lr=learning_rate)\n",
" self.device = ('cuda:0' if T.cuda.is_available() else 'cpu')\n",
" self.to(self.device)\n",
" \n",
" def forward(self, x):\n",
" x = self.fc1(x)\n",
" x = F.relu(x)\n",
" x = self.fc2(x)\n",
" x = F.relu(x)\n",
" mu = self.mu(x)\n",
" sigma = self.sigma(x)\n",
" sigma = T.exp(sigma)\n",
" return mu, sigma"
]
},
{
"cell_type": "markdown",
"id": "235e4248",
"metadata": {},
"source": [
"Let's check the network's in and out dimensions. "
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "11b2a4ed",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Policy network input dims: torch.Size([3, 8])\n",
"Policy network output dims: (torch.Size([3, 2]), torch.Size([3, 2]))\n",
"Policy network output:\n",
"tensor([[ 0.3395, -0.0075],\n",
" [ 0.3395, -0.0075],\n",
" [ 0.3395, -0.0075]], grad_fn=)\n",
"tensor([[0.9377, 0.9265],\n",
" [0.9377, 0.9265],\n",
" [0.9377, 0.9265]], grad_fn=)\n"
]
}
],
"source": [
"# init a policy network\n",
"policy_net = PolicyNetwork(observation_dim=observation.shape[0], action_dim=action.shape[0], learning_rate=1e-4)\n",
"\n",
"# set an observation batch\n",
"observation_batch = np.repeat(np.expand_dims(observation, axis=0), 3, axis=0)\n",
"\n",
"# transform the batch into a torch.Tensor and send it to the network's device\n",
"x_in = T.Tensor(observation_batch).float().to(policy_net.device)\n",
"\n",
"# run the feed forward\n",
"mu, sigma = policy_net(x_in)\n",
"\n",
"print(f'Policy network input dims: {x_in.size()}')\n",
"print(f'Policy network output dims: {mu.size(), sigma.size()}')\n",
"print(f'Policy network output:\\n{mu}\\n{sigma}')"
]
},
{
"cell_type": "markdown",
"id": "566e2260",
"metadata": {},
"source": [
"Next, let us create the REINFORCE agent class."
]
},
{
"cell_type": "code",
"execution_count": 82,
"id": "0e6b55da",
"metadata": {},
"outputs": [],
"source": [
"class ReinforceAgent:\n",
" def __init__(self, observation_dim, action_dim, learning_rate, gamma):\n",
" self.lr = learning_rate\n",
" self.gamma = gamma\n",
" \n",
" # initialize policy network\n",
" self.policy = PolicyNetwork(observation_dim=observation_dim, \n",
" action_dim=action_dim, learning_rate=self.lr)\n",
" \n",
" # lists for saving trajectories\n",
" self.rewards = []\n",
" self.log_probs = []\n",
" self.reverse_log_probs = []\n",
" self.returns = []\n",
" \n",
" def reset(self):\n",
" self.rewards = []\n",
" self.log_probs = []\n",
" self.reverse_log_probs = []\n",
" self.returns = []\n",
" \n",
" def choose_action(self, observation): \n",
" # transform to torch.Tensor and send to device\n",
" observation = T.Tensor(observation).float().to(self.policy.device)\n",
" \n",
" # get the distribution parameters \n",
" mu, sigma = self.policy(observation)\n",
" \n",
" # create the distribution\n",
" m = T.distributions.Normal(loc=mu, scale=sigma)\n",
" \n",
" # sample an action\n",
" action = m.sample()\n",
" return T.tanh(action).detach().numpy(), m.log_prob(action)\n",
" \n",
" \n",
" def store_transition(self, reward, log_probs):\n",
" self.rewards.append(reward)\n",
" self.log_probs.append(log_probs)\n",
" \n",
" def end_episode_computation(self):\n",
" t = len(self.rewards)\n",
" G = 0\n",
" while t > 0:\n",
" t -= 1\n",
" \n",
" # compute the returns in reversed order\n",
" G = self.rewards[t] + self.gamma * G\n",
" self.returns.append(G)\n",
" self.reverse_log_probs.append(self.log_probs[t])\n",
" \n",
" def learn(self):\n",
" # transform to torch.Tensor and send to device\n",
" log_probs = T.stack(self.reverse_log_probs).float().to(self.policy.device)\n",
" returns = T.Tensor(self.returns).float().unsqueeze(dim=1).to(self.policy.device)\n",
" \n",
" # compute the loss and backpropogate\n",
" self.policy.optimizer.zero_grad()\n",
" loss = T.sum(-returns * log_probs).to(self.policy.device)\n",
" loss.backward()\n",
" self.policy.optimizer.step()"
]
},
{
"cell_type": "markdown",
"id": "0dbda4be",
"metadata": {},
"source": [
"Now we are ready to run a few experiments."
]
},
{
"cell_type": "code",
"execution_count": 91,
"id": "2d35bad4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 0 | Score: -127.67 | Avg Score: -127.67 |\n",
"| Experiment: 0 | Episode: 1 | Score: -144.98 | Avg Score: -136.32 |\n",
"| Experiment: 0 | Episode: 2 | Score: -462.32 | Avg Score: -244.99 |\n",
"| Experiment: 0 | Episode: 3 | Score: -410.41 | Avg Score: -286.34 |\n",
"| Experiment: 0 | Episode: 4 | Score: -419.26 | Avg Score: -312.93 |\n",
"| Experiment: 0 | Episode: 5 | Score: -111.86 | Avg Score: -279.42 |\n",
"| Experiment: 0 | Episode: 6 | Score: -103.64 | Avg Score: -254.31 |\n",
"| Experiment: 0 | Episode: 7 | Score: -157.01 | Avg Score: -242.14 |\n",
"| Experiment: 0 | Episode: 8 | Score: -382.64 | Avg Score: -257.75 |\n",
"| Experiment: 0 | Episode: 9 | Score: -427.21 | Avg Score: -274.7 |\n",
"| Experiment: 0 | Episode: 10 | Score: -328.67 | Avg Score: -279.61 |\n",
"| Experiment: 0 | Episode: 11 | Score: -98.0 | Avg Score: -264.47 |\n",
"| Experiment: 0 | Episode: 12 | Score: -15.54 | Avg Score: -245.32 |\n",
"| Experiment: 0 | Episode: 13 | Score: -202.99 | Avg Score: -242.3 |\n",
"| Experiment: 0 | Episode: 14 | Score: -86.51 | Avg Score: -231.91 |\n",
"| Experiment: 0 | Episode: 15 | Score: -107.88 | Avg Score: -224.16 |\n",
"| Experiment: 0 | Episode: 16 | Score: -392.99 | Avg Score: -234.09 |\n",
"| Experiment: 0 | Episode: 17 | Score: -427.58 | Avg Score: -244.84 |\n",
"| Experiment: 0 | Episode: 18 | Score: -127.39 | Avg Score: -238.66 |\n",
"| Experiment: 0 | Episode: 19 | Score: -357.89 | Avg Score: -244.62 |\n",
"| Experiment: 0 | Episode: 20 | Score: -182.64 | Avg Score: -241.67 |\n",
"| Experiment: 0 | Episode: 21 | Score: -384.6 | Avg Score: -248.17 |\n",
"| Experiment: 0 | Episode: 22 | Score: -328.29 | Avg Score: -251.65 |\n",
"| Experiment: 0 | Episode: 23 | Score: -199.83 | Avg Score: -249.49 |\n",
"| Experiment: 0 | Episode: 24 | Score: -354.78 | Avg Score: -253.7 |\n",
"| Experiment: 0 | Episode: 25 | Score: -228.1 | Avg Score: -252.72 |\n",
"| Experiment: 0 | Episode: 26 | Score: -181.57 | Avg Score: -250.08 |\n",
"| Experiment: 0 | Episode: 27 | Score: -441.36 | Avg Score: -256.92 |\n",
"| Experiment: 0 | Episode: 28 | Score: -217.07 | Avg Score: -255.54 |\n",
"| Experiment: 0 | Episode: 29 | Score: -326.0 | Avg Score: -257.89 |\n",
"| Experiment: 0 | Episode: 30 | Score: -27.67 | Avg Score: -250.46 |\n",
"| Experiment: 0 | Episode: 31 | Score: -157.85 | Avg Score: -247.57 |\n",
"| Experiment: 0 | Episode: 32 | Score: -450.85 | Avg Score: -253.73 |\n",
"| Experiment: 0 | Episode: 33 | Score: -384.92 | Avg Score: -257.59 |\n",
"| Experiment: 0 | Episode: 34 | Score: -301.48 | Avg Score: -258.84 |\n",
"| Experiment: 0 | Episode: 35 | Score: -146.67 | Avg Score: -255.73 |\n",
"| Experiment: 0 | Episode: 36 | Score: -232.8 | Avg Score: -255.11 |\n",
"| Experiment: 0 | Episode: 37 | Score: -411.05 | Avg Score: -259.21 |\n",
"| Experiment: 0 | Episode: 38 | Score: -70.81 | Avg Score: -254.38 |\n",
"| Experiment: 0 | Episode: 39 | Score: -271.32 | Avg Score: -254.8 |\n",
"| Experiment: 0 | Episode: 40 | Score: -68.51 | Avg Score: -250.26 |\n",
"| Experiment: 0 | Episode: 41 | Score: -382.52 | Avg Score: -253.41 |\n",
"| Experiment: 0 | Episode: 42 | Score: -29.49 | Avg Score: -248.2 |\n",
"| Experiment: 0 | Episode: 43 | Score: -186.01 | Avg Score: -246.79 |\n",
"| Experiment: 0 | Episode: 44 | Score: -453.01 | Avg Score: -251.37 |\n",
"| Experiment: 0 | Episode: 45 | Score: -182.33 | Avg Score: -249.87 |\n",
"| Experiment: 0 | Episode: 46 | Score: -91.21 | Avg Score: -246.49 |\n",
"| Experiment: 0 | Episode: 47 | Score: -111.98 | Avg Score: -243.69 |\n",
"| Experiment: 0 | Episode: 48 | Score: -291.31 | Avg Score: -244.66 |\n",
"| Experiment: 0 | Episode: 49 | Score: -213.74 | Avg Score: -244.04 |\n",
"| Experiment: 0 | Episode: 50 | Score: -526.72 | Avg Score: -249.59 |\n",
"| Experiment: 0 | Episode: 51 | Score: -99.82 | Avg Score: -246.71 |\n",
"| Experiment: 0 | Episode: 52 | Score: -273.57 | Avg Score: -247.21 |\n",
"| Experiment: 0 | Episode: 53 | Score: -231.42 | Avg Score: -246.92 |\n",
"| Experiment: 0 | Episode: 54 | Score: -190.82 | Avg Score: -245.9 |\n",
"| Experiment: 0 | Episode: 55 | Score: -79.1 | Avg Score: -242.92 |\n",
"| Experiment: 0 | Episode: 56 | Score: -223.08 | Avg Score: -242.58 |\n",
"| Experiment: 0 | Episode: 57 | Score: -36.33 | Avg Score: -239.02 |\n",
"| Experiment: 0 | Episode: 58 | Score: -190.66 | Avg Score: -238.2 |\n",
"| Experiment: 0 | Episode: 59 | Score: -118.01 | Avg Score: -236.2 |\n",
"| Experiment: 0 | Episode: 60 | Score: -38.58 | Avg Score: -232.96 |\n",
"| Experiment: 0 | Episode: 61 | Score: -351.97 | Avg Score: -234.88 |\n",
"| Experiment: 0 | Episode: 62 | Score: -54.12 | Avg Score: -232.01 |\n",
"| Experiment: 0 | Episode: 63 | Score: -237.96 | Avg Score: -232.1 |\n",
"| Experiment: 0 | Episode: 64 | Score: -180.14 | Avg Score: -231.3 |\n",
"| Experiment: 0 | Episode: 65 | Score: -203.72 | Avg Score: -230.88 |\n",
"| Experiment: 0 | Episode: 66 | Score: -94.34 | Avg Score: -228.84 |\n",
"| Experiment: 0 | Episode: 67 | Score: -438.91 | Avg Score: -231.93 |\n",
"| Experiment: 0 | Episode: 68 | Score: -256.31 | Avg Score: -232.29 |\n",
"| Experiment: 0 | Episode: 69 | Score: -380.74 | Avg Score: -234.41 |\n",
"| Experiment: 0 | Episode: 70 | Score: -64.25 | Avg Score: -232.01 |\n",
"| Experiment: 0 | Episode: 71 | Score: -303.17 | Avg Score: -233.0 |\n",
"| Experiment: 0 | Episode: 72 | Score: -282.58 | Avg Score: -233.68 |\n",
"| Experiment: 0 | Episode: 73 | Score: -415.62 | Avg Score: -236.14 |\n",
"| Experiment: 0 | Episode: 74 | Score: -85.29 | Avg Score: -234.13 |\n",
"| Experiment: 0 | Episode: 75 | Score: -201.48 | Avg Score: -233.7 |\n",
"| Experiment: 0 | Episode: 76 | Score: -64.11 | Avg Score: -231.49 |\n",
"| Experiment: 0 | Episode: 77 | Score: -16.03 | Avg Score: -228.73 |\n",
"| Experiment: 0 | Episode: 78 | Score: -218.57 | Avg Score: -228.6 |\n",
"| Experiment: 0 | Episode: 79 | Score: -42.37 | Avg Score: -226.28 |\n",
"| Experiment: 0 | Episode: 80 | Score: -86.15 | Avg Score: -224.55 |\n",
"| Experiment: 0 | Episode: 81 | Score: -51.96 | Avg Score: -222.44 |\n",
"| Experiment: 0 | Episode: 82 | Score: -62.85 | Avg Score: -220.52 |\n",
"| Experiment: 0 | Episode: 83 | Score: -166.87 | Avg Score: -219.88 |\n",
"| Experiment: 0 | Episode: 84 | Score: -127.22 | Avg Score: -218.79 |\n",
"| Experiment: 0 | Episode: 85 | Score: -109.21 | Avg Score: -217.52 |\n",
"| Experiment: 0 | Episode: 86 | Score: -94.07 | Avg Score: -216.1 |\n",
"| Experiment: 0 | Episode: 87 | Score: -95.87 | Avg Score: -214.73 |\n",
"| Experiment: 0 | Episode: 88 | Score: -506.43 | Avg Score: -218.01 |\n",
"| Experiment: 0 | Episode: 89 | Score: -279.6 | Avg Score: -218.69 |\n",
"| Experiment: 0 | Episode: 90 | Score: -197.86 | Avg Score: -218.46 |\n",
"| Experiment: 0 | Episode: 91 | Score: -266.5 | Avg Score: -218.98 |\n",
"| Experiment: 0 | Episode: 92 | Score: -315.54 | Avg Score: -220.02 |\n",
"| Experiment: 0 | Episode: 93 | Score: -140.35 | Avg Score: -219.18 |\n",
"| Experiment: 0 | Episode: 94 | Score: -305.92 | Avg Score: -220.09 |\n",
"| Experiment: 0 | Episode: 95 | Score: -165.34 | Avg Score: -219.52 |\n",
"| Experiment: 0 | Episode: 96 | Score: -124.63 | Avg Score: -218.54 |\n",
"| Experiment: 0 | Episode: 97 | Score: -309.37 | Avg Score: -219.47 |\n",
"| Experiment: 0 | Episode: 98 | Score: -77.26 | Avg Score: -218.03 |\n",
"| Experiment: 0 | Episode: 99 | Score: -43.38 | Avg Score: -216.28 |\n",
"| Experiment: 0 | Episode: 100 | Score: -412.65 | Avg Score: -219.13 |\n",
"| Experiment: 0 | Episode: 101 | Score: -299.55 | Avg Score: -220.68 |\n",
"| Experiment: 0 | Episode: 102 | Score: -71.21 | Avg Score: -216.77 |\n",
"| Experiment: 0 | Episode: 103 | Score: -5.45 | Avg Score: -212.72 |\n",
"| Experiment: 0 | Episode: 104 | Score: -66.85 | Avg Score: -209.19 |\n",
"| Experiment: 0 | Episode: 105 | Score: -223.74 | Avg Score: -210.31 |\n",
"| Experiment: 0 | Episode: 106 | Score: -310.96 | Avg Score: -212.39 |\n",
"| Experiment: 0 | Episode: 107 | Score: -116.19 | Avg Score: -211.98 |\n",
"| Experiment: 0 | Episode: 108 | Score: -110.0 | Avg Score: -209.25 |\n",
"| Experiment: 0 | Episode: 109 | Score: -435.51 | Avg Score: -209.34 |\n",
"| Experiment: 0 | Episode: 110 | Score: -209.74 | Avg Score: -208.15 |\n",
"| Experiment: 0 | Episode: 111 | Score: -204.47 | Avg Score: -209.21 |\n",
"| Experiment: 0 | Episode: 112 | Score: -147.63 | Avg Score: -210.53 |\n",
"| Experiment: 0 | Episode: 113 | Score: -226.97 | Avg Score: -210.77 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 114 | Score: -418.39 | Avg Score: -214.09 |\n",
"| Experiment: 0 | Episode: 115 | Score: -314.9 | Avg Score: -216.16 |\n",
"| Experiment: 0 | Episode: 116 | Score: -88.26 | Avg Score: -213.11 |\n",
"| Experiment: 0 | Episode: 117 | Score: -240.13 | Avg Score: -211.24 |\n",
"| Experiment: 0 | Episode: 118 | Score: -232.1 | Avg Score: -212.29 |\n",
"| Experiment: 0 | Episode: 119 | Score: -137.36 | Avg Score: -210.08 |\n",
"| Experiment: 0 | Episode: 120 | Score: -468.35 | Avg Score: -212.94 |\n",
"| Experiment: 0 | Episode: 121 | Score: -111.14 | Avg Score: -210.2 |\n",
"| Experiment: 0 | Episode: 122 | Score: -142.73 | Avg Score: -208.35 |\n",
"| Experiment: 0 | Episode: 123 | Score: -97.63 | Avg Score: -207.33 |\n",
"| Experiment: 0 | Episode: 124 | Score: -181.91 | Avg Score: -205.6 |\n",
"| Experiment: 0 | Episode: 125 | Score: -351.23 | Avg Score: -206.83 |\n",
"| Experiment: 0 | Episode: 126 | Score: -409.81 | Avg Score: -209.11 |\n",
"| Experiment: 0 | Episode: 127 | Score: -90.78 | Avg Score: -205.6 |\n",
"| Experiment: 0 | Episode: 128 | Score: -371.84 | Avg Score: -207.15 |\n",
"| Experiment: 0 | Episode: 129 | Score: -350.72 | Avg Score: -207.4 |\n",
"| Experiment: 0 | Episode: 130 | Score: -110.72 | Avg Score: -208.23 |\n",
"| Experiment: 0 | Episode: 131 | Score: -415.0 | Avg Score: -210.8 |\n",
"| Experiment: 0 | Episode: 132 | Score: -373.18 | Avg Score: -210.02 |\n",
"| Experiment: 0 | Episode: 133 | Score: -234.43 | Avg Score: -208.52 |\n",
"| Experiment: 0 | Episode: 134 | Score: -240.57 | Avg Score: -207.91 |\n",
"| Experiment: 0 | Episode: 135 | Score: -432.45 | Avg Score: -210.77 |\n",
"| Experiment: 0 | Episode: 136 | Score: -115.19 | Avg Score: -209.59 |\n",
"| Experiment: 0 | Episode: 137 | Score: -171.32 | Avg Score: -207.19 |\n",
"| Experiment: 0 | Episode: 138 | Score: -156.83 | Avg Score: -208.06 |\n",
"| Experiment: 0 | Episode: 139 | Score: -290.73 | Avg Score: -208.25 |\n",
"| Experiment: 0 | Episode: 140 | Score: -325.11 | Avg Score: -210.82 |\n",
"| Experiment: 0 | Episode: 141 | Score: -287.89 | Avg Score: -209.87 |\n",
"| Experiment: 0 | Episode: 142 | Score: -80.74 | Avg Score: -210.38 |\n",
"| Experiment: 0 | Episode: 143 | Score: -188.86 | Avg Score: -210.41 |\n",
"| Experiment: 0 | Episode: 144 | Score: -76.3 | Avg Score: -206.64 |\n",
"| Experiment: 0 | Episode: 145 | Score: -255.02 | Avg Score: -207.37 |\n",
"| Experiment: 0 | Episode: 146 | Score: -242.05 | Avg Score: -208.88 |\n",
"| Experiment: 0 | Episode: 147 | Score: -281.72 | Avg Score: -210.58 |\n",
"| Experiment: 0 | Episode: 148 | Score: -342.1 | Avg Score: -211.08 |\n",
"| Experiment: 0 | Episode: 149 | Score: -216.82 | Avg Score: -211.11 |\n",
"| Experiment: 0 | Episode: 150 | Score: -26.18 | Avg Score: -206.11 |\n",
"| Experiment: 0 | Episode: 151 | Score: -117.93 | Avg Score: -206.29 |\n",
"| Experiment: 0 | Episode: 152 | Score: -92.0 | Avg Score: -204.47 |\n",
"| Experiment: 0 | Episode: 153 | Score: -432.32 | Avg Score: -206.48 |\n",
"| Experiment: 0 | Episode: 154 | Score: -363.56 | Avg Score: -208.21 |\n",
"| Experiment: 0 | Episode: 155 | Score: -46.75 | Avg Score: -207.89 |\n",
"| Experiment: 0 | Episode: 156 | Score: -72.76 | Avg Score: -206.38 |\n",
"| Experiment: 0 | Episode: 157 | Score: -177.51 | Avg Score: -207.8 |\n",
"| Experiment: 0 | Episode: 158 | Score: -165.11 | Avg Score: -207.54 |\n",
"| Experiment: 0 | Episode: 159 | Score: -75.74 | Avg Score: -207.12 |\n",
"| Experiment: 0 | Episode: 160 | Score: -102.84 | Avg Score: -207.76 |\n",
"| Experiment: 0 | Episode: 161 | Score: -176.89 | Avg Score: -206.01 |\n",
"| Experiment: 0 | Episode: 162 | Score: -1.69 | Avg Score: -205.48 |\n",
"| Experiment: 0 | Episode: 163 | Score: -75.61 | Avg Score: -203.86 |\n",
"| Experiment: 0 | Episode: 164 | Score: -148.86 | Avg Score: -203.55 |\n",
"| Experiment: 0 | Episode: 165 | Score: -216.12 | Avg Score: -203.67 |\n",
"| Experiment: 0 | Episode: 166 | Score: -137.82 | Avg Score: -204.11 |\n",
"| Experiment: 0 | Episode: 167 | Score: -368.86 | Avg Score: -203.41 |\n",
"| Experiment: 0 | Episode: 168 | Score: -34.7 | Avg Score: -201.19 |\n",
"| Experiment: 0 | Episode: 169 | Score: -71.8 | Avg Score: -198.1 |\n",
"| Experiment: 0 | Episode: 170 | Score: -354.87 | Avg Score: -201.01 |\n",
"| Experiment: 0 | Episode: 171 | Score: -363.75 | Avg Score: -201.61 |\n",
"| Experiment: 0 | Episode: 172 | Score: -150.15 | Avg Score: -200.29 |\n",
"| Experiment: 0 | Episode: 173 | Score: -412.09 | Avg Score: -200.25 |\n",
"| Experiment: 0 | Episode: 174 | Score: -61.53 | Avg Score: -200.02 |\n",
"| Experiment: 0 | Episode: 175 | Score: -162.05 | Avg Score: -199.62 |\n",
"| Experiment: 0 | Episode: 176 | Score: -102.96 | Avg Score: -200.01 |\n",
"| Experiment: 0 | Episode: 177 | Score: -32.56 | Avg Score: -200.18 |\n",
"| Experiment: 0 | Episode: 178 | Score: -126.39 | Avg Score: -199.25 |\n",
"| Experiment: 0 | Episode: 179 | Score: -112.82 | Avg Score: -199.96 |\n",
"| Experiment: 0 | Episode: 180 | Score: -280.26 | Avg Score: -201.9 |\n",
"| Experiment: 0 | Episode: 181 | Score: -87.06 | Avg Score: -202.25 |\n",
"| Experiment: 0 | Episode: 182 | Score: -199.56 | Avg Score: -203.62 |\n",
"| Experiment: 0 | Episode: 183 | Score: -40.48 | Avg Score: -202.35 |\n",
"| Experiment: 0 | Episode: 184 | Score: -209.41 | Avg Score: -203.18 |\n",
"| Experiment: 0 | Episode: 185 | Score: -86.39 | Avg Score: -202.95 |\n",
"| Experiment: 0 | Episode: 186 | Score: -292.71 | Avg Score: -204.93 |\n",
"| Experiment: 0 | Episode: 187 | Score: -396.51 | Avg Score: -207.94 |\n",
"| Experiment: 0 | Episode: 188 | Score: -172.75 | Avg Score: -204.6 |\n",
"| Experiment: 0 | Episode: 189 | Score: -159.12 | Avg Score: -203.4 |\n",
"| Experiment: 0 | Episode: 190 | Score: -152.49 | Avg Score: -202.94 |\n",
"| Experiment: 0 | Episode: 191 | Score: -67.29 | Avg Score: -200.95 |\n",
"| Experiment: 0 | Episode: 192 | Score: -92.16 | Avg Score: -198.72 |\n",
"| Experiment: 0 | Episode: 193 | Score: -179.02 | Avg Score: -199.11 |\n",
"| Experiment: 0 | Episode: 194 | Score: -113.31 | Avg Score: -197.18 |\n",
"| Experiment: 0 | Episode: 195 | Score: -335.62 | Avg Score: -198.88 |\n",
"| Experiment: 0 | Episode: 196 | Score: -268.91 | Avg Score: -200.32 |\n",
"| Experiment: 0 | Episode: 197 | Score: -166.16 | Avg Score: -198.89 |\n",
"| Experiment: 0 | Episode: 198 | Score: -56.01 | Avg Score: -198.68 |\n",
"| Experiment: 0 | Episode: 199 | Score: -71.47 | Avg Score: -198.96 |\n",
"| Experiment: 0 | Episode: 200 | Score: -205.67 | Avg Score: -196.89 |\n",
"| Experiment: 0 | Episode: 201 | Score: -201.4 | Avg Score: -195.91 |\n",
"| Experiment: 0 | Episode: 202 | Score: -167.8 | Avg Score: -196.88 |\n",
"| Experiment: 0 | Episode: 203 | Score: -158.16 | Avg Score: -198.4 |\n",
"| Experiment: 0 | Episode: 204 | Score: -45.73 | Avg Score: -198.19 |\n",
"| Experiment: 0 | Episode: 205 | Score: -178.44 | Avg Score: -197.74 |\n",
"| Experiment: 0 | Episode: 206 | Score: -319.99 | Avg Score: -197.83 |\n",
"| Experiment: 0 | Episode: 207 | Score: -280.41 | Avg Score: -199.47 |\n",
"| Experiment: 0 | Episode: 208 | Score: -20.04 | Avg Score: -198.57 |\n",
"| Experiment: 0 | Episode: 209 | Score: -61.88 | Avg Score: -194.84 |\n",
"| Experiment: 0 | Episode: 210 | Score: -80.21 | Avg Score: -193.54 |\n",
"| Experiment: 0 | Episode: 211 | Score: -221.97 | Avg Score: -193.72 |\n",
"| Experiment: 0 | Episode: 212 | Score: -129.64 | Avg Score: -193.54 |\n",
"| Experiment: 0 | Episode: 213 | Score: -96.91 | Avg Score: -192.23 |\n",
"| Experiment: 0 | Episode: 214 | Score: -188.89 | Avg Score: -189.94 |\n",
"| Experiment: 0 | Episode: 215 | Score: -178.1 | Avg Score: -188.57 |\n",
"| Experiment: 0 | Episode: 216 | Score: -100.61 | Avg Score: -188.69 |\n",
"| Experiment: 0 | Episode: 217 | Score: -104.06 | Avg Score: -187.33 |\n",
"| Experiment: 0 | Episode: 218 | Score: -101.07 | Avg Score: -186.02 |\n",
"| Experiment: 0 | Episode: 219 | Score: -74.76 | Avg Score: -185.4 |\n",
"| Experiment: 0 | Episode: 220 | Score: -392.55 | Avg Score: -184.64 |\n",
"| Experiment: 0 | Episode: 221 | Score: -364.77 | Avg Score: -187.18 |\n",
"| Experiment: 0 | Episode: 222 | Score: -103.62 | Avg Score: -186.79 |\n",
"| Experiment: 0 | Episode: 223 | Score: -204.6 | Avg Score: -187.85 |\n",
"| Experiment: 0 | Episode: 224 | Score: -172.96 | Avg Score: -187.77 |\n",
"| Experiment: 0 | Episode: 225 | Score: -177.05 | Avg Score: -186.02 |\n",
"| Experiment: 0 | Episode: 226 | Score: -264.5 | Avg Score: -184.57 |\n"
]
},
{
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"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 227 | Score: -249.19 | Avg Score: -186.15 |\n",
"| Experiment: 0 | Episode: 228 | Score: -124.75 | Avg Score: -183.68 |\n",
"| Experiment: 0 | Episode: 229 | Score: -283.03 | Avg Score: -183.01 |\n",
"| Experiment: 0 | Episode: 230 | Score: -67.86 | Avg Score: -182.58 |\n",
"| Experiment: 0 | Episode: 231 | Score: -160.45 | Avg Score: -180.03 |\n",
"| Experiment: 0 | Episode: 232 | Score: -86.94 | Avg Score: -177.17 |\n",
"| Experiment: 0 | Episode: 233 | Score: -35.04 | Avg Score: -175.18 |\n",
"| Experiment: 0 | Episode: 234 | Score: -11.16 | Avg Score: -172.88 |\n",
"| Experiment: 0 | Episode: 235 | Score: -93.72 | Avg Score: -169.5 |\n",
"| Experiment: 0 | Episode: 236 | Score: -105.22 | Avg Score: -169.4 |\n",
"| Experiment: 0 | Episode: 237 | Score: -117.29 | Avg Score: -168.85 |\n",
"| Experiment: 0 | Episode: 238 | Score: -83.79 | Avg Score: -168.12 |\n",
"| Experiment: 0 | Episode: 239 | Score: -215.29 | Avg Score: -167.37 |\n",
"| Experiment: 0 | Episode: 240 | Score: -340.58 | Avg Score: -167.52 |\n",
"| Experiment: 0 | Episode: 241 | Score: -201.11 | Avg Score: -166.66 |\n",
"| Experiment: 0 | Episode: 242 | Score: -87.3 | Avg Score: -166.72 |\n",
"| Experiment: 0 | Episode: 243 | Score: -41.26 | Avg Score: -165.25 |\n",
"| Experiment: 0 | Episode: 244 | Score: -324.15 | Avg Score: -167.73 |\n",
"| Experiment: 0 | Episode: 245 | Score: -115.43 | Avg Score: -166.33 |\n",
"| Experiment: 0 | Episode: 246 | Score: -134.29 | Avg Score: -165.25 |\n",
"| Experiment: 0 | Episode: 247 | Score: -375.78 | Avg Score: -166.19 |\n",
"| Experiment: 0 | Episode: 248 | Score: -140.58 | Avg Score: -164.18 |\n",
"| Experiment: 0 | Episode: 249 | Score: -26.8 | Avg Score: -162.28 |\n",
"| Experiment: 0 | Episode: 250 | Score: -105.25 | Avg Score: -163.07 |\n",
"| Experiment: 0 | Episode: 251 | Score: -82.76 | Avg Score: -162.72 |\n",
"| Experiment: 0 | Episode: 252 | Score: -56.73 | Avg Score: -162.36 |\n",
"| Experiment: 0 | Episode: 253 | Score: -62.29 | Avg Score: -158.66 |\n",
"| Experiment: 0 | Episode: 254 | Score: -149.39 | Avg Score: -156.52 |\n",
"| Experiment: 0 | Episode: 255 | Score: -179.72 | Avg Score: -157.85 |\n",
"| Experiment: 0 | Episode: 256 | Score: -437.47 | Avg Score: -161.5 |\n",
"| Experiment: 0 | Episode: 257 | Score: -158.0 | Avg Score: -161.3 |\n",
"| Experiment: 0 | Episode: 258 | Score: -190.08 | Avg Score: -161.55 |\n",
"| Experiment: 0 | Episode: 259 | Score: -180.82 | Avg Score: -162.6 |\n",
"| Experiment: 0 | Episode: 260 | Score: -191.61 | Avg Score: -163.49 |\n",
"| Experiment: 0 | Episode: 261 | Score: -92.06 | Avg Score: -162.64 |\n",
"| Experiment: 0 | Episode: 262 | Score: -180.85 | Avg Score: -164.43 |\n",
"| Experiment: 0 | Episode: 263 | Score: -203.03 | Avg Score: -165.71 |\n",
"| Experiment: 0 | Episode: 264 | Score: -26.55 | Avg Score: -164.49 |\n",
"| Experiment: 0 | Episode: 265 | Score: -195.14 | Avg Score: -164.28 |\n",
"| Experiment: 0 | Episode: 266 | Score: -177.74 | Avg Score: -164.67 |\n",
"| Experiment: 0 | Episode: 267 | Score: -259.51 | Avg Score: -163.58 |\n",
"| Experiment: 0 | Episode: 268 | Score: -136.14 | Avg Score: -164.6 |\n",
"| Experiment: 0 | Episode: 269 | Score: -97.18 | Avg Score: -164.85 |\n",
"| Experiment: 0 | Episode: 270 | Score: -104.61 | Avg Score: -162.35 |\n",
"| Experiment: 0 | Episode: 271 | Score: -188.12 | Avg Score: -160.59 |\n",
"| Experiment: 0 | Episode: 272 | Score: -134.19 | Avg Score: -160.43 |\n",
"| Experiment: 0 | Episode: 273 | Score: -152.07 | Avg Score: -157.83 |\n",
"| Experiment: 0 | Episode: 274 | Score: -118.27 | Avg Score: -158.4 |\n",
"| Experiment: 0 | Episode: 275 | Score: -70.48 | Avg Score: -157.48 |\n",
"| Experiment: 0 | Episode: 276 | Score: -312.87 | Avg Score: -159.58 |\n",
"| Experiment: 0 | Episode: 277 | Score: -273.12 | Avg Score: -161.99 |\n",
"| Experiment: 0 | Episode: 278 | Score: -162.2 | Avg Score: -162.35 |\n",
"| Experiment: 0 | Episode: 279 | Score: -60.94 | Avg Score: -161.83 |\n",
"| Experiment: 0 | Episode: 280 | Score: -356.25 | Avg Score: -162.59 |\n",
"| Experiment: 0 | Episode: 281 | Score: -216.04 | Avg Score: -163.88 |\n",
"| Experiment: 0 | Episode: 282 | Score: -368.21 | Avg Score: -165.56 |\n",
"| Experiment: 0 | Episode: 283 | Score: -132.07 | Avg Score: -166.48 |\n",
"| Experiment: 0 | Episode: 284 | Score: -38.65 | Avg Score: -164.77 |\n",
"| Experiment: 0 | Episode: 285 | Score: -271.16 | Avg Score: -166.62 |\n",
"| Experiment: 0 | Episode: 286 | Score: -49.92 | Avg Score: -164.19 |\n",
"| Experiment: 0 | Episode: 287 | Score: -82.72 | Avg Score: -161.05 |\n",
"| Experiment: 0 | Episode: 288 | Score: -170.88 | Avg Score: -161.03 |\n",
"| Experiment: 0 | Episode: 289 | Score: -125.45 | Avg Score: -160.7 |\n",
"| Experiment: 0 | Episode: 290 | Score: -111.72 | Avg Score: -160.29 |\n",
"| Experiment: 0 | Episode: 291 | Score: -440.0 | Avg Score: -164.02 |\n",
"| Experiment: 0 | Episode: 292 | Score: -135.14 | Avg Score: -164.45 |\n",
"| Experiment: 0 | Episode: 293 | Score: -54.49 | Avg Score: -163.2 |\n",
"| Experiment: 0 | Episode: 294 | Score: -191.35 | Avg Score: -163.98 |\n",
"| Experiment: 0 | Episode: 295 | Score: -235.57 | Avg Score: -162.98 |\n",
"| Experiment: 0 | Episode: 296 | Score: -83.58 | Avg Score: -161.13 |\n",
"| Experiment: 0 | Episode: 297 | Score: -206.1 | Avg Score: -161.53 |\n",
"| Experiment: 0 | Episode: 298 | Score: -231.85 | Avg Score: -163.29 |\n",
"| Experiment: 0 | Episode: 299 | Score: -115.6 | Avg Score: -163.73 |\n",
"| Experiment: 0 | Episode: 300 | Score: -109.37 | Avg Score: -162.76 |\n",
"| Experiment: 0 | Episode: 301 | Score: -145.34 | Avg Score: -162.2 |\n",
"| Experiment: 0 | Episode: 302 | Score: -214.27 | Avg Score: -162.67 |\n",
"| Experiment: 0 | Episode: 303 | Score: -263.96 | Avg Score: -163.73 |\n",
"| Experiment: 0 | Episode: 304 | Score: -78.37 | Avg Score: -164.05 |\n",
"| Experiment: 0 | Episode: 305 | Score: -110.16 | Avg Score: -163.37 |\n",
"| Experiment: 0 | Episode: 306 | Score: -49.22 | Avg Score: -160.66 |\n",
"| Experiment: 0 | Episode: 307 | Score: -79.83 | Avg Score: -158.66 |\n",
"| Experiment: 0 | Episode: 308 | Score: -239.36 | Avg Score: -160.85 |\n",
"| Experiment: 0 | Episode: 309 | Score: -272.4 | Avg Score: -162.95 |\n",
"| Experiment: 0 | Episode: 310 | Score: -117.92 | Avg Score: -163.33 |\n",
"| Experiment: 0 | Episode: 311 | Score: -92.59 | Avg Score: -162.04 |\n",
"| Experiment: 0 | Episode: 312 | Score: -65.54 | Avg Score: -161.4 |\n",
"| Experiment: 0 | Episode: 313 | Score: -428.79 | Avg Score: -164.72 |\n",
"| Experiment: 0 | Episode: 314 | Score: -269.7 | Avg Score: -165.52 |\n",
"| Experiment: 0 | Episode: 315 | Score: -132.66 | Avg Score: -165.07 |\n",
"| Experiment: 0 | Episode: 316 | Score: -100.42 | Avg Score: -165.07 |\n",
"| Experiment: 0 | Episode: 317 | Score: -49.33 | Avg Score: -164.52 |\n",
"| Experiment: 0 | Episode: 318 | Score: -513.81 | Avg Score: -168.65 |\n",
"| Experiment: 0 | Episode: 319 | Score: -322.78 | Avg Score: -171.13 |\n",
"| Experiment: 0 | Episode: 320 | Score: -302.11 | Avg Score: -170.22 |\n",
"| Experiment: 0 | Episode: 321 | Score: -230.07 | Avg Score: -168.88 |\n",
"| Experiment: 0 | Episode: 322 | Score: -121.29 | Avg Score: -169.05 |\n",
"| Experiment: 0 | Episode: 323 | Score: -292.56 | Avg Score: -169.93 |\n",
"| Experiment: 0 | Episode: 324 | Score: -58.24 | Avg Score: -168.79 |\n",
"| Experiment: 0 | Episode: 325 | Score: -163.64 | Avg Score: -168.65 |\n",
"| Experiment: 0 | Episode: 326 | Score: -278.72 | Avg Score: -168.79 |\n",
"| Experiment: 0 | Episode: 327 | Score: -296.47 | Avg Score: -169.27 |\n",
"| Experiment: 0 | Episode: 328 | Score: -178.84 | Avg Score: -169.81 |\n",
"| Experiment: 0 | Episode: 329 | Score: -321.46 | Avg Score: -170.19 |\n",
"| Experiment: 0 | Episode: 330 | Score: -335.39 | Avg Score: -172.87 |\n",
"| Experiment: 0 | Episode: 331 | Score: -176.01 | Avg Score: -173.02 |\n",
"| Experiment: 0 | Episode: 332 | Score: -79.64 | Avg Score: -172.95 |\n",
"| Experiment: 0 | Episode: 333 | Score: -163.79 | Avg Score: -174.24 |\n",
"| Experiment: 0 | Episode: 334 | Score: -120.21 | Avg Score: -175.33 |\n",
"| Experiment: 0 | Episode: 335 | Score: -8.97 | Avg Score: -174.48 |\n",
"| Experiment: 0 | Episode: 336 | Score: -328.03 | Avg Score: -176.71 |\n",
"| Experiment: 0 | Episode: 337 | Score: -177.5 | Avg Score: -177.31 |\n",
"| Experiment: 0 | Episode: 338 | Score: -121.29 | Avg Score: -177.69 |\n",
"| Experiment: 0 | Episode: 339 | Score: -61.92 | Avg Score: -176.15 |\n",
"| Experiment: 0 | Episode: 340 | Score: -197.9 | Avg Score: -174.72 |\n",
"| Experiment: 0 | Episode: 341 | Score: -69.07 | Avg Score: -173.4 |\n",
"| Experiment: 0 | Episode: 342 | Score: -194.02 | Avg Score: -174.47 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 343 | Score: -109.85 | Avg Score: -175.16 |\n",
"| Experiment: 0 | Episode: 344 | Score: -111.68 | Avg Score: -173.03 |\n",
"| Experiment: 0 | Episode: 345 | Score: -312.17 | Avg Score: -175.0 |\n",
"| Experiment: 0 | Episode: 346 | Score: -66.4 | Avg Score: -174.32 |\n",
"| Experiment: 0 | Episode: 347 | Score: -25.09 | Avg Score: -170.81 |\n",
"| Experiment: 0 | Episode: 348 | Score: -85.99 | Avg Score: -170.27 |\n",
"| Experiment: 0 | Episode: 349 | Score: -103.26 | Avg Score: -171.03 |\n",
"| Experiment: 0 | Episode: 350 | Score: -198.55 | Avg Score: -171.97 |\n",
"| Experiment: 0 | Episode: 351 | Score: -227.03 | Avg Score: -173.41 |\n",
"| Experiment: 0 | Episode: 352 | Score: -46.21 | Avg Score: -173.3 |\n",
"| Experiment: 0 | Episode: 353 | Score: -495.09 | Avg Score: -177.63 |\n",
"| Experiment: 0 | Episode: 354 | Score: -49.42 | Avg Score: -176.63 |\n",
"| Experiment: 0 | Episode: 355 | Score: -146.01 | Avg Score: -176.29 |\n",
"| Experiment: 0 | Episode: 356 | Score: -116.57 | Avg Score: -173.09 |\n",
"| Experiment: 0 | Episode: 357 | Score: -100.68 | Avg Score: -172.51 |\n",
"| Experiment: 0 | Episode: 358 | Score: -46.97 | Avg Score: -171.08 |\n",
"| Experiment: 0 | Episode: 359 | Score: -108.73 | Avg Score: -170.36 |\n",
"| Experiment: 0 | Episode: 360 | Score: -143.17 | Avg Score: -169.88 |\n",
"| Experiment: 0 | Episode: 361 | Score: -62.84 | Avg Score: -169.58 |\n",
"| Experiment: 0 | Episode: 362 | Score: -108.32 | Avg Score: -168.86 |\n",
"| Experiment: 0 | Episode: 363 | Score: -133.13 | Avg Score: -168.16 |\n",
"| Experiment: 0 | Episode: 364 | Score: -0.08 | Avg Score: -167.89 |\n",
"| Experiment: 0 | Episode: 365 | Score: -154.85 | Avg Score: -167.49 |\n",
"| Experiment: 0 | Episode: 366 | Score: -96.38 | Avg Score: -166.68 |\n",
"| Experiment: 0 | Episode: 367 | Score: -59.32 | Avg Score: -164.68 |\n",
"| Experiment: 0 | Episode: 368 | Score: -125.78 | Avg Score: -164.57 |\n",
"| Experiment: 0 | Episode: 369 | Score: -70.69 | Avg Score: -164.31 |\n",
"| Experiment: 0 | Episode: 370 | Score: -134.84 | Avg Score: -164.61 |\n",
"| Experiment: 0 | Episode: 371 | Score: -232.74 | Avg Score: -165.06 |\n",
"| Experiment: 0 | Episode: 372 | Score: -69.9 | Avg Score: -164.41 |\n",
"| Experiment: 0 | Episode: 373 | Score: -187.9 | Avg Score: -164.77 |\n",
"| Experiment: 0 | Episode: 374 | Score: -235.14 | Avg Score: -165.94 |\n",
"| Experiment: 0 | Episode: 375 | Score: -245.13 | Avg Score: -167.69 |\n",
"| Experiment: 0 | Episode: 376 | Score: -182.12 | Avg Score: -166.38 |\n",
"| Experiment: 0 | Episode: 377 | Score: -119.93 | Avg Score: -164.85 |\n",
"| Experiment: 0 | Episode: 378 | Score: 126.5 | Avg Score: -161.96 |\n",
"| Experiment: 0 | Episode: 379 | Score: -58.07 | Avg Score: -161.93 |\n",
"| Experiment: 0 | Episode: 380 | Score: -152.41 | Avg Score: -159.89 |\n",
"| Experiment: 0 | Episode: 381 | Score: -208.99 | Avg Score: -159.82 |\n",
"| Experiment: 0 | Episode: 382 | Score: -205.19 | Avg Score: -158.19 |\n",
"| Experiment: 0 | Episode: 383 | Score: -72.53 | Avg Score: -157.6 |\n",
"| Experiment: 0 | Episode: 384 | Score: -108.65 | Avg Score: -158.3 |\n",
"| Experiment: 0 | Episode: 385 | Score: -94.29 | Avg Score: -156.53 |\n",
"| Experiment: 0 | Episode: 386 | Score: -292.45 | Avg Score: -158.95 |\n",
"| Experiment: 0 | Episode: 387 | Score: -128.5 | Avg Score: -159.41 |\n",
"| Experiment: 0 | Episode: 388 | Score: -271.39 | Avg Score: -160.42 |\n",
"| Experiment: 0 | Episode: 389 | Score: -31.44 | Avg Score: -159.48 |\n",
"| Experiment: 0 | Episode: 390 | Score: -73.53 | Avg Score: -159.1 |\n",
"| Experiment: 0 | Episode: 391 | Score: -149.68 | Avg Score: -156.19 |\n",
"| Experiment: 0 | Episode: 392 | Score: -120.99 | Avg Score: -156.05 |\n",
"| Experiment: 0 | Episode: 393 | Score: -385.94 | Avg Score: -159.37 |\n",
"| Experiment: 0 | Episode: 394 | Score: -101.77 | Avg Score: -158.47 |\n",
"| Experiment: 0 | Episode: 395 | Score: 9.44 | Avg Score: -156.02 |\n",
"| Experiment: 0 | Episode: 396 | Score: -87.52 | Avg Score: -156.06 |\n",
"| Experiment: 0 | Episode: 397 | Score: -137.61 | Avg Score: -155.37 |\n",
"| Experiment: 0 | Episode: 398 | Score: -228.06 | Avg Score: -155.34 |\n",
"| Experiment: 0 | Episode: 399 | Score: -130.11 | Avg Score: -155.48 |\n",
"| Experiment: 0 | Episode: 400 | Score: -247.58 | Avg Score: -156.86 |\n",
"| Experiment: 0 | Episode: 401 | Score: -318.92 | Avg Score: -158.6 |\n",
"| Experiment: 0 | Episode: 402 | Score: -89.62 | Avg Score: -157.35 |\n",
"| Experiment: 0 | Episode: 403 | Score: -68.65 | Avg Score: -155.4 |\n",
"| Experiment: 0 | Episode: 404 | Score: -88.82 | Avg Score: -155.5 |\n",
"| Experiment: 0 | Episode: 405 | Score: -197.7 | Avg Score: -156.38 |\n",
"| Experiment: 0 | Episode: 406 | Score: -68.3 | Avg Score: -156.57 |\n",
"| Experiment: 0 | Episode: 407 | Score: -90.86 | Avg Score: -156.68 |\n",
"| Experiment: 0 | Episode: 408 | Score: -143.42 | Avg Score: -155.72 |\n",
"| Experiment: 0 | Episode: 409 | Score: -336.59 | Avg Score: -156.36 |\n",
"| Experiment: 0 | Episode: 410 | Score: -87.39 | Avg Score: -156.06 |\n",
"| Experiment: 0 | Episode: 411 | Score: -36.63 | Avg Score: -155.5 |\n",
"| Experiment: 0 | Episode: 412 | Score: -268.4 | Avg Score: -157.53 |\n",
"| Experiment: 0 | Episode: 413 | Score: -97.83 | Avg Score: -154.22 |\n",
"| Experiment: 0 | Episode: 414 | Score: -81.46 | Avg Score: -152.33 |\n",
"| Experiment: 0 | Episode: 415 | Score: -37.13 | Avg Score: -151.38 |\n",
"| Experiment: 0 | Episode: 416 | Score: 153.29 | Avg Score: -148.84 |\n",
"| Experiment: 0 | Episode: 417 | Score: -136.61 | Avg Score: -149.71 |\n",
"| Experiment: 0 | Episode: 418 | Score: -46.12 | Avg Score: -145.04 |\n",
"| Experiment: 0 | Episode: 419 | Score: -4.54 | Avg Score: -141.86 |\n",
"| Experiment: 0 | Episode: 420 | Score: -221.66 | Avg Score: -141.05 |\n",
"| Experiment: 0 | Episode: 421 | Score: -292.94 | Avg Score: -141.68 |\n",
"| Experiment: 0 | Episode: 422 | Score: -96.02 | Avg Score: -141.43 |\n",
"| Experiment: 0 | Episode: 423 | Score: -259.77 | Avg Score: -141.1 |\n",
"| Experiment: 0 | Episode: 424 | Score: -34.96 | Avg Score: -140.87 |\n",
"| Experiment: 0 | Episode: 425 | Score: -96.12 | Avg Score: -140.19 |\n",
"| Experiment: 0 | Episode: 426 | Score: -58.02 | Avg Score: -137.98 |\n",
"| Experiment: 0 | Episode: 427 | Score: 12.99 | Avg Score: -134.89 |\n",
"| Experiment: 0 | Episode: 428 | Score: -87.73 | Avg Score: -133.98 |\n",
"| Experiment: 0 | Episode: 429 | Score: -126.94 | Avg Score: -132.03 |\n",
"| Experiment: 0 | Episode: 430 | Score: -84.45 | Avg Score: -129.52 |\n",
"| Experiment: 0 | Episode: 431 | Score: -71.98 | Avg Score: -128.48 |\n",
"| Experiment: 0 | Episode: 432 | Score: -143.96 | Avg Score: -129.13 |\n",
"| Experiment: 0 | Episode: 433 | Score: -247.19 | Avg Score: -129.96 |\n",
"| Experiment: 0 | Episode: 434 | Score: -108.81 | Avg Score: -129.85 |\n",
"| Experiment: 0 | Episode: 435 | Score: -139.82 | Avg Score: -131.16 |\n",
"| Experiment: 0 | Episode: 436 | Score: -308.01 | Avg Score: -130.96 |\n",
"| Experiment: 0 | Episode: 437 | Score: -310.53 | Avg Score: -132.29 |\n",
"| Experiment: 0 | Episode: 438 | Score: -69.19 | Avg Score: -131.76 |\n",
"| Experiment: 0 | Episode: 439 | Score: -99.8 | Avg Score: -132.14 |\n",
"| Experiment: 0 | Episode: 440 | Score: -93.23 | Avg Score: -131.1 |\n",
"| Experiment: 0 | Episode: 441 | Score: -124.56 | Avg Score: -131.65 |\n",
"| Experiment: 0 | Episode: 442 | Score: -104.1 | Avg Score: -130.75 |\n",
"| Experiment: 0 | Episode: 443 | Score: -123.25 | Avg Score: -130.89 |\n",
"| Experiment: 0 | Episode: 444 | Score: -48.39 | Avg Score: -130.25 |\n",
"| Experiment: 0 | Episode: 445 | Score: -102.73 | Avg Score: -128.16 |\n",
"| Experiment: 0 | Episode: 446 | Score: -126.51 | Avg Score: -128.76 |\n",
"| Experiment: 0 | Episode: 447 | Score: -75.72 | Avg Score: -129.27 |\n",
"| Experiment: 0 | Episode: 448 | Score: -295.69 | Avg Score: -131.36 |\n",
"| Experiment: 0 | Episode: 449 | Score: -194.84 | Avg Score: -132.28 |\n",
"| Experiment: 0 | Episode: 450 | Score: -216.66 | Avg Score: -132.46 |\n",
"| Experiment: 0 | Episode: 451 | Score: -66.64 | Avg Score: -130.86 |\n",
"| Experiment: 0 | Episode: 452 | Score: -55.19 | Avg Score: -130.95 |\n",
"| Experiment: 0 | Episode: 453 | Score: -121.03 | Avg Score: -127.21 |\n",
"| Experiment: 0 | Episode: 454 | Score: -150.86 | Avg Score: -128.22 |\n",
"| Experiment: 0 | Episode: 455 | Score: -159.41 | Avg Score: -128.35 |\n",
"| Experiment: 0 | Episode: 456 | Score: -75.7 | Avg Score: -127.95 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 457 | Score: -206.24 | Avg Score: -129.0 |\n",
"| Experiment: 0 | Episode: 458 | Score: -157.65 | Avg Score: -130.11 |\n",
"| Experiment: 0 | Episode: 459 | Score: -155.64 | Avg Score: -130.58 |\n",
"| Experiment: 0 | Episode: 460 | Score: -224.49 | Avg Score: -131.39 |\n",
"| Experiment: 0 | Episode: 461 | Score: -116.54 | Avg Score: -131.93 |\n",
"| Experiment: 0 | Episode: 462 | Score: -86.45 | Avg Score: -131.71 |\n",
"| Experiment: 0 | Episode: 463 | Score: -71.14 | Avg Score: -131.09 |\n",
"| Experiment: 0 | Episode: 464 | Score: -287.01 | Avg Score: -133.96 |\n",
"| Experiment: 0 | Episode: 465 | Score: -265.87 | Avg Score: -135.07 |\n",
"| Experiment: 0 | Episode: 466 | Score: -48.02 | Avg Score: -134.58 |\n",
"| Experiment: 0 | Episode: 467 | Score: -100.13 | Avg Score: -134.99 |\n",
"| Experiment: 0 | Episode: 468 | Score: -108.51 | Avg Score: -134.82 |\n",
"| Experiment: 0 | Episode: 469 | Score: -222.78 | Avg Score: -136.34 |\n",
"| Experiment: 0 | Episode: 470 | Score: -85.63 | Avg Score: -135.85 |\n",
"| Experiment: 0 | Episode: 471 | Score: -87.42 | Avg Score: -134.4 |\n",
"| Experiment: 0 | Episode: 472 | Score: -63.54 | Avg Score: -134.33 |\n",
"| Experiment: 0 | Episode: 473 | Score: -175.11 | Avg Score: -134.2 |\n",
"| Experiment: 0 | Episode: 474 | Score: -101.87 | Avg Score: -132.87 |\n",
"| Experiment: 0 | Episode: 475 | Score: -122.82 | Avg Score: -131.65 |\n",
"| Experiment: 0 | Episode: 476 | Score: -88.64 | Avg Score: -130.71 |\n",
"| Experiment: 0 | Episode: 477 | Score: -103.92 | Avg Score: -130.55 |\n",
"| Experiment: 0 | Episode: 478 | Score: -96.22 | Avg Score: -132.78 |\n",
"| Experiment: 0 | Episode: 479 | Score: -58.22 | Avg Score: -132.78 |\n",
"| Experiment: 0 | Episode: 480 | Score: -162.42 | Avg Score: -132.88 |\n",
"| Experiment: 0 | Episode: 481 | Score: -157.11 | Avg Score: -132.36 |\n",
"| Experiment: 0 | Episode: 482 | Score: -327.68 | Avg Score: -133.59 |\n",
"| Experiment: 0 | Episode: 483 | Score: -81.34 | Avg Score: -133.68 |\n",
"| Experiment: 0 | Episode: 484 | Score: -144.52 | Avg Score: -134.03 |\n",
"| Experiment: 0 | Episode: 485 | Score: -172.53 | Avg Score: -134.82 |\n",
"| Experiment: 0 | Episode: 486 | Score: -78.23 | Avg Score: -132.67 |\n",
"| Experiment: 0 | Episode: 487 | Score: -83.12 | Avg Score: -132.22 |\n",
"| Experiment: 0 | Episode: 488 | Score: -63.64 | Avg Score: -130.14 |\n",
"| Experiment: 0 | Episode: 489 | Score: -155.75 | Avg Score: -131.39 |\n",
"| Experiment: 0 | Episode: 490 | Score: -110.39 | Avg Score: -131.76 |\n",
"| Experiment: 0 | Episode: 491 | Score: -144.37 | Avg Score: -131.7 |\n",
"| Experiment: 0 | Episode: 492 | Score: -184.76 | Avg Score: -132.34 |\n",
"| Experiment: 0 | Episode: 493 | Score: -24.79 | Avg Score: -128.73 |\n",
"| Experiment: 0 | Episode: 494 | Score: -253.12 | Avg Score: -130.24 |\n",
"| Experiment: 0 | Episode: 495 | Score: -259.41 | Avg Score: -132.93 |\n",
"| Experiment: 0 | Episode: 496 | Score: -80.09 | Avg Score: -132.86 |\n",
"| Experiment: 0 | Episode: 497 | Score: -99.1 | Avg Score: -132.47 |\n",
"| Experiment: 0 | Episode: 498 | Score: -210.76 | Avg Score: -132.3 |\n",
"| Experiment: 0 | Episode: 499 | Score: -96.99 | Avg Score: -131.97 |\n",
"| Experiment: 0 | Episode: 500 | Score: -196.33 | Avg Score: -131.45 |\n",
"| Experiment: 0 | Episode: 501 | Score: -29.76 | Avg Score: -128.56 |\n",
"| Experiment: 0 | Episode: 502 | Score: -99.18 | Avg Score: -128.66 |\n",
"| Experiment: 0 | Episode: 503 | Score: -273.26 | Avg Score: -130.7 |\n",
"| Experiment: 0 | Episode: 504 | Score: -147.57 | Avg Score: -131.29 |\n",
"| Experiment: 0 | Episode: 505 | Score: -87.63 | Avg Score: -130.19 |\n",
"| Experiment: 0 | Episode: 506 | Score: -87.75 | Avg Score: -130.39 |\n",
"| Experiment: 0 | Episode: 507 | Score: -89.77 | Avg Score: -130.37 |\n",
"| Experiment: 0 | Episode: 508 | Score: -214.16 | Avg Score: -131.08 |\n",
"| Experiment: 0 | Episode: 509 | Score: -302.86 | Avg Score: -130.75 |\n",
"| Experiment: 0 | Episode: 510 | Score: -73.01 | Avg Score: -130.6 |\n",
"| Experiment: 0 | Episode: 511 | Score: -305.87 | Avg Score: -133.29 |\n",
"| Experiment: 0 | Episode: 512 | Score: -262.69 | Avg Score: -133.24 |\n",
"| Experiment: 0 | Episode: 513 | Score: -97.9 | Avg Score: -133.24 |\n",
"| Experiment: 0 | Episode: 514 | Score: -135.72 | Avg Score: -133.78 |\n",
"| Experiment: 0 | Episode: 515 | Score: -91.38 | Avg Score: -134.32 |\n",
"| Experiment: 0 | Episode: 516 | Score: -54.57 | Avg Score: -136.4 |\n",
"| Experiment: 0 | Episode: 517 | Score: -188.27 | Avg Score: -136.92 |\n",
"| Experiment: 0 | Episode: 518 | Score: -258.83 | Avg Score: -139.04 |\n",
"| Experiment: 0 | Episode: 519 | Score: -105.77 | Avg Score: -140.06 |\n",
"| Experiment: 0 | Episode: 520 | Score: -140.92 | Avg Score: -139.25 |\n",
"| Experiment: 0 | Episode: 521 | Score: -140.03 | Avg Score: -137.72 |\n",
"| Experiment: 0 | Episode: 522 | Score: -87.22 | Avg Score: -137.63 |\n",
"| Experiment: 0 | Episode: 523 | Score: -93.41 | Avg Score: -135.97 |\n",
"| Experiment: 0 | Episode: 524 | Score: -198.51 | Avg Score: -137.6 |\n",
"| Experiment: 0 | Episode: 525 | Score: -173.16 | Avg Score: -138.37 |\n",
"| Experiment: 0 | Episode: 526 | Score: -42.21 | Avg Score: -138.22 |\n",
"| Experiment: 0 | Episode: 527 | Score: -420.89 | Avg Score: -142.56 |\n",
"| Experiment: 0 | Episode: 528 | Score: -94.83 | Avg Score: -142.63 |\n",
"| Experiment: 0 | Episode: 529 | Score: -300.19 | Avg Score: -144.36 |\n",
"| Experiment: 0 | Episode: 530 | Score: -167.22 | Avg Score: -145.19 |\n",
"| Experiment: 0 | Episode: 531 | Score: -102.69 | Avg Score: -145.49 |\n",
"| Experiment: 0 | Episode: 532 | Score: -39.66 | Avg Score: -144.45 |\n",
"| Experiment: 0 | Episode: 533 | Score: -74.66 | Avg Score: -142.73 |\n",
"| Experiment: 0 | Episode: 534 | Score: -176.88 | Avg Score: -143.41 |\n",
"| Experiment: 0 | Episode: 535 | Score: -81.1 | Avg Score: -142.82 |\n",
"| Experiment: 0 | Episode: 536 | Score: -63.79 | Avg Score: -140.38 |\n",
"| Experiment: 0 | Episode: 537 | Score: -206.39 | Avg Score: -139.34 |\n",
"| Experiment: 0 | Episode: 538 | Score: -264.53 | Avg Score: -141.29 |\n",
"| Experiment: 0 | Episode: 539 | Score: -85.41 | Avg Score: -141.14 |\n",
"| Experiment: 0 | Episode: 540 | Score: -91.94 | Avg Score: -141.13 |\n",
"| Experiment: 0 | Episode: 541 | Score: -143.28 | Avg Score: -141.32 |\n",
"| Experiment: 0 | Episode: 542 | Score: -86.74 | Avg Score: -141.15 |\n",
"| Experiment: 0 | Episode: 543 | Score: -59.38 | Avg Score: -140.51 |\n",
"| Experiment: 0 | Episode: 544 | Score: -86.23 | Avg Score: -140.89 |\n",
"| Experiment: 0 | Episode: 545 | Score: -128.11 | Avg Score: -141.14 |\n",
"| Experiment: 0 | Episode: 546 | Score: -126.13 | Avg Score: -141.14 |\n",
"| Experiment: 0 | Episode: 547 | Score: -69.53 | Avg Score: -141.07 |\n",
"| Experiment: 0 | Episode: 548 | Score: -102.88 | Avg Score: -139.15 |\n",
"| Experiment: 0 | Episode: 549 | Score: -132.37 | Avg Score: -138.52 |\n",
"| Experiment: 0 | Episode: 550 | Score: -75.27 | Avg Score: -137.11 |\n",
"| Experiment: 0 | Episode: 551 | Score: -67.3 | Avg Score: -137.11 |\n",
"| Experiment: 0 | Episode: 552 | Score: -139.92 | Avg Score: -137.96 |\n",
"| Experiment: 0 | Episode: 553 | Score: -215.23 | Avg Score: -138.9 |\n",
"| Experiment: 0 | Episode: 554 | Score: -76.12 | Avg Score: -138.15 |\n",
"| Experiment: 0 | Episode: 555 | Score: -48.6 | Avg Score: -137.05 |\n",
"| Experiment: 0 | Episode: 556 | Score: -266.54 | Avg Score: -138.96 |\n",
"| Experiment: 0 | Episode: 557 | Score: -242.83 | Avg Score: -139.32 |\n",
"| Experiment: 0 | Episode: 558 | Score: -117.24 | Avg Score: -138.92 |\n",
"| Experiment: 0 | Episode: 559 | Score: -246.55 | Avg Score: -139.83 |\n",
"| Experiment: 0 | Episode: 560 | Score: -112.04 | Avg Score: -138.7 |\n",
"| Experiment: 0 | Episode: 561 | Score: -156.38 | Avg Score: -139.1 |\n",
"| Experiment: 0 | Episode: 562 | Score: -139.85 | Avg Score: -139.63 |\n",
"| Experiment: 0 | Episode: 563 | Score: -39.46 | Avg Score: -139.32 |\n",
"| Experiment: 0 | Episode: 564 | Score: -160.41 | Avg Score: -138.05 |\n",
"| Experiment: 0 | Episode: 565 | Score: -57.37 | Avg Score: -135.97 |\n",
"| Experiment: 0 | Episode: 566 | Score: -176.63 | Avg Score: -137.25 |\n",
"| Experiment: 0 | Episode: 567 | Score: -108.46 | Avg Score: -137.34 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 568 | Score: -89.04 | Avg Score: -137.14 |\n",
"| Experiment: 0 | Episode: 569 | Score: -95.0 | Avg Score: -135.86 |\n",
"| Experiment: 0 | Episode: 570 | Score: -47.03 | Avg Score: -135.48 |\n",
"| Experiment: 0 | Episode: 571 | Score: -172.47 | Avg Score: -136.33 |\n",
"| Experiment: 0 | Episode: 572 | Score: -133.36 | Avg Score: -137.03 |\n",
"| Experiment: 0 | Episode: 573 | Score: -167.2 | Avg Score: -136.95 |\n",
"| Experiment: 0 | Episode: 574 | Score: -93.68 | Avg Score: -136.86 |\n",
"| Experiment: 0 | Episode: 575 | Score: -190.66 | Avg Score: -137.54 |\n",
"| Experiment: 0 | Episode: 576 | Score: -55.56 | Avg Score: -137.21 |\n",
"| Experiment: 0 | Episode: 577 | Score: -172.17 | Avg Score: -137.89 |\n",
"| Experiment: 0 | Episode: 578 | Score: 25.56 | Avg Score: -136.68 |\n",
"| Experiment: 0 | Episode: 579 | Score: -118.76 | Avg Score: -137.28 |\n",
"| Experiment: 0 | Episode: 580 | Score: -184.42 | Avg Score: -137.5 |\n",
"| Experiment: 0 | Episode: 581 | Score: -65.39 | Avg Score: -136.59 |\n",
"| Experiment: 0 | Episode: 582 | Score: -186.38 | Avg Score: -135.17 |\n",
"| Experiment: 0 | Episode: 583 | Score: -127.2 | Avg Score: -135.63 |\n",
"| Experiment: 0 | Episode: 584 | Score: -74.97 | Avg Score: -134.94 |\n",
"| Experiment: 0 | Episode: 585 | Score: -184.86 | Avg Score: -135.06 |\n",
"| Experiment: 0 | Episode: 586 | Score: -248.81 | Avg Score: -136.76 |\n",
"| Experiment: 0 | Episode: 587 | Score: -100.65 | Avg Score: -136.94 |\n",
"| Experiment: 0 | Episode: 588 | Score: -60.24 | Avg Score: -136.91 |\n",
"| Experiment: 0 | Episode: 589 | Score: -112.78 | Avg Score: -136.48 |\n",
"| Experiment: 0 | Episode: 590 | Score: -41.61 | Avg Score: -135.79 |\n",
"| Experiment: 0 | Episode: 591 | Score: -44.34 | Avg Score: -134.79 |\n",
"| Experiment: 0 | Episode: 592 | Score: -127.88 | Avg Score: -134.22 |\n",
"| Experiment: 0 | Episode: 593 | Score: -137.32 | Avg Score: -135.34 |\n",
"| Experiment: 0 | Episode: 594 | Score: -31.13 | Avg Score: -133.12 |\n",
"| Experiment: 0 | Episode: 595 | Score: -72.59 | Avg Score: -131.26 |\n",
"| Experiment: 0 | Episode: 596 | Score: -305.4 | Avg Score: -133.51 |\n",
"| Experiment: 0 | Episode: 597 | Score: -239.14 | Avg Score: -134.91 |\n",
"| Experiment: 0 | Episode: 598 | Score: -166.47 | Avg Score: -134.47 |\n",
"| Experiment: 0 | Episode: 599 | Score: -139.45 | Avg Score: -134.89 |\n",
"| Experiment: 0 | Episode: 600 | Score: -116.48 | Avg Score: -134.09 |\n",
"| Experiment: 0 | Episode: 601 | Score: -260.79 | Avg Score: -136.4 |\n",
"| Experiment: 0 | Episode: 602 | Score: -105.74 | Avg Score: -136.47 |\n",
"| Experiment: 0 | Episode: 603 | Score: -407.78 | Avg Score: -137.81 |\n",
"| Experiment: 0 | Episode: 604 | Score: -131.48 | Avg Score: -137.65 |\n",
"| Experiment: 0 | Episode: 605 | Score: -295.53 | Avg Score: -139.73 |\n",
"| Experiment: 0 | Episode: 606 | Score: -224.58 | Avg Score: -141.1 |\n",
"| Experiment: 0 | Episode: 607 | Score: -272.69 | Avg Score: -142.93 |\n",
"| Experiment: 0 | Episode: 608 | Score: -90.19 | Avg Score: -141.69 |\n",
"| Experiment: 0 | Episode: 609 | Score: -53.47 | Avg Score: -139.2 |\n",
"| Experiment: 0 | Episode: 610 | Score: -55.62 | Avg Score: -139.02 |\n",
"| Experiment: 0 | Episode: 611 | Score: -217.74 | Avg Score: -138.14 |\n",
"| Experiment: 0 | Episode: 612 | Score: -238.5 | Avg Score: -137.9 |\n",
"| Experiment: 0 | Episode: 613 | Score: -205.09 | Avg Score: -138.97 |\n",
"| Experiment: 0 | Episode: 614 | Score: -96.98 | Avg Score: -138.58 |\n",
"| Experiment: 0 | Episode: 615 | Score: -6.89 | Avg Score: -137.74 |\n",
"| Experiment: 0 | Episode: 616 | Score: -246.1 | Avg Score: -139.65 |\n",
"| Experiment: 0 | Episode: 617 | Score: -263.86 | Avg Score: -140.41 |\n",
"| Experiment: 0 | Episode: 618 | Score: -98.49 | Avg Score: -138.81 |\n",
"| Experiment: 0 | Episode: 619 | Score: -289.69 | Avg Score: -140.65 |\n",
"| Experiment: 0 | Episode: 620 | Score: -2.38 | Avg Score: -139.26 |\n",
"| Experiment: 0 | Episode: 621 | Score: -83.28 | Avg Score: -138.69 |\n",
"| Experiment: 0 | Episode: 622 | Score: -107.22 | Avg Score: -138.89 |\n",
"| Experiment: 0 | Episode: 623 | Score: -199.13 | Avg Score: -139.95 |\n",
"| Experiment: 0 | Episode: 624 | Score: -336.67 | Avg Score: -141.33 |\n",
"| Experiment: 0 | Episode: 625 | Score: -71.85 | Avg Score: -140.32 |\n",
"| Experiment: 0 | Episode: 626 | Score: -137.47 | Avg Score: -141.27 |\n",
"| Experiment: 0 | Episode: 627 | Score: -329.05 | Avg Score: -140.35 |\n",
"| Experiment: 0 | Episode: 628 | Score: -194.07 | Avg Score: -141.34 |\n",
"| Experiment: 0 | Episode: 629 | Score: -145.3 | Avg Score: -139.8 |\n",
"| Experiment: 0 | Episode: 630 | Score: -102.72 | Avg Score: -139.15 |\n",
"| Experiment: 0 | Episode: 631 | Score: -69.85 | Avg Score: -138.82 |\n",
"| Experiment: 0 | Episode: 632 | Score: -114.69 | Avg Score: -139.57 |\n",
"| Experiment: 0 | Episode: 633 | Score: -64.78 | Avg Score: -139.47 |\n",
"| Experiment: 0 | Episode: 634 | Score: -0.12 | Avg Score: -137.71 |\n",
"| Experiment: 0 | Episode: 635 | Score: -120.57 | Avg Score: -138.1 |\n",
"| Experiment: 0 | Episode: 636 | Score: -287.45 | Avg Score: -140.34 |\n",
"| Experiment: 0 | Episode: 637 | Score: -303.01 | Avg Score: -141.3 |\n",
"| Experiment: 0 | Episode: 638 | Score: -249.46 | Avg Score: -141.15 |\n",
"| Experiment: 0 | Episode: 639 | Score: -296.05 | Avg Score: -143.26 |\n",
"| Experiment: 0 | Episode: 640 | Score: -36.95 | Avg Score: -142.71 |\n",
"| Experiment: 0 | Episode: 641 | Score: -84.52 | Avg Score: -142.12 |\n",
"| Experiment: 0 | Episode: 642 | Score: -290.97 | Avg Score: -144.16 |\n",
"| Experiment: 0 | Episode: 643 | Score: -83.46 | Avg Score: -144.41 |\n",
"| Experiment: 0 | Episode: 644 | Score: -126.93 | Avg Score: -144.81 |\n",
"| Experiment: 0 | Episode: 645 | Score: -81.29 | Avg Score: -144.34 |\n",
"| Experiment: 0 | Episode: 646 | Score: -340.01 | Avg Score: -146.48 |\n",
"| Experiment: 0 | Episode: 647 | Score: -99.74 | Avg Score: -146.79 |\n",
"| Experiment: 0 | Episode: 648 | Score: -138.18 | Avg Score: -147.14 |\n",
"| Experiment: 0 | Episode: 649 | Score: -147.84 | Avg Score: -147.29 |\n",
"| Experiment: 0 | Episode: 650 | Score: -278.78 | Avg Score: -149.33 |\n",
"| Experiment: 0 | Episode: 651 | Score: -191.61 | Avg Score: -150.57 |\n",
"| Experiment: 0 | Episode: 652 | Score: -117.4 | Avg Score: -150.35 |\n",
"| Experiment: 0 | Episode: 653 | Score: -17.71 | Avg Score: -148.37 |\n",
"| Experiment: 0 | Episode: 654 | Score: -78.61 | Avg Score: -148.4 |\n",
"| Experiment: 0 | Episode: 655 | Score: -373.1 | Avg Score: -151.64 |\n",
"| Experiment: 0 | Episode: 656 | Score: -53.44 | Avg Score: -149.51 |\n",
"| Experiment: 0 | Episode: 657 | Score: -97.99 | Avg Score: -148.06 |\n",
"| Experiment: 0 | Episode: 658 | Score: -169.01 | Avg Score: -148.58 |\n",
"| Experiment: 0 | Episode: 659 | Score: -106.96 | Avg Score: -147.18 |\n",
"| Experiment: 0 | Episode: 660 | Score: -82.05 | Avg Score: -146.88 |\n",
"| Experiment: 0 | Episode: 661 | Score: -354.84 | Avg Score: -148.87 |\n",
"| Experiment: 0 | Episode: 662 | Score: -125.09 | Avg Score: -148.72 |\n",
"| Experiment: 0 | Episode: 663 | Score: -50.13 | Avg Score: -148.83 |\n",
"| Experiment: 0 | Episode: 664 | Score: -86.86 | Avg Score: -148.09 |\n",
"| Experiment: 0 | Episode: 665 | Score: -93.83 | Avg Score: -148.46 |\n",
"| Experiment: 0 | Episode: 666 | Score: -125.36 | Avg Score: -147.94 |\n",
"| Experiment: 0 | Episode: 667 | Score: 19.76 | Avg Score: -146.66 |\n",
"| Experiment: 0 | Episode: 668 | Score: -114.45 | Avg Score: -146.92 |\n",
"| Experiment: 0 | Episode: 669 | Score: -101.73 | Avg Score: -146.98 |\n",
"| Experiment: 0 | Episode: 670 | Score: -205.53 | Avg Score: -148.57 |\n",
"| Experiment: 0 | Episode: 671 | Score: -132.81 | Avg Score: -148.17 |\n",
"| Experiment: 0 | Episode: 672 | Score: -110.08 | Avg Score: -147.94 |\n",
"| Experiment: 0 | Episode: 673 | Score: -187.83 | Avg Score: -148.14 |\n",
"| Experiment: 0 | Episode: 674 | Score: -201.75 | Avg Score: -149.23 |\n",
"| Experiment: 0 | Episode: 675 | Score: -56.05 | Avg Score: -147.88 |\n",
"| Experiment: 0 | Episode: 676 | Score: -128.73 | Avg Score: -148.61 |\n",
"| Experiment: 0 | Episode: 677 | Score: -59.5 | Avg Score: -147.48 |\n",
"| Experiment: 0 | Episode: 678 | Score: -67.86 | Avg Score: -148.42 |\n",
"| Experiment: 0 | Episode: 679 | Score: -60.76 | Avg Score: -147.84 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 680 | Score: -46.84 | Avg Score: -146.46 |\n",
"| Experiment: 0 | Episode: 681 | Score: -137.69 | Avg Score: -147.19 |\n",
"| Experiment: 0 | Episode: 682 | Score: -78.37 | Avg Score: -146.11 |\n",
"| Experiment: 0 | Episode: 683 | Score: -249.36 | Avg Score: -147.33 |\n",
"| Experiment: 0 | Episode: 684 | Score: -99.09 | Avg Score: -147.57 |\n",
"| Experiment: 0 | Episode: 685 | Score: -157.72 | Avg Score: -147.3 |\n",
"| Experiment: 0 | Episode: 686 | Score: -177.27 | Avg Score: -146.58 |\n",
"| Experiment: 0 | Episode: 687 | Score: -93.94 | Avg Score: -146.51 |\n",
"| Experiment: 0 | Episode: 688 | Score: -30.41 | Avg Score: -146.22 |\n",
"| Experiment: 0 | Episode: 689 | Score: -165.32 | Avg Score: -146.74 |\n",
"| Experiment: 0 | Episode: 690 | Score: -115.92 | Avg Score: -147.48 |\n",
"| Experiment: 0 | Episode: 691 | Score: -81.84 | Avg Score: -147.86 |\n",
"| Experiment: 0 | Episode: 692 | Score: -37.51 | Avg Score: -146.96 |\n",
"| Experiment: 0 | Episode: 693 | Score: -275.05 | Avg Score: -148.33 |\n",
"| Experiment: 0 | Episode: 694 | Score: -143.19 | Avg Score: -149.45 |\n",
"| Experiment: 0 | Episode: 695 | Score: -187.8 | Avg Score: -150.61 |\n",
"| Experiment: 0 | Episode: 696 | Score: -74.1 | Avg Score: -148.29 |\n",
"| Experiment: 0 | Episode: 697 | Score: -190.98 | Avg Score: -147.81 |\n",
"| Experiment: 0 | Episode: 698 | Score: -235.49 | Avg Score: -148.5 |\n",
"| Experiment: 0 | Episode: 699 | Score: -86.1 | Avg Score: -147.97 |\n",
"| Experiment: 0 | Episode: 700 | Score: -69.2 | Avg Score: -147.5 |\n",
"| Experiment: 0 | Episode: 701 | Score: -132.1 | Avg Score: -146.21 |\n",
"| Experiment: 0 | Episode: 702 | Score: -42.49 | Avg Score: -145.58 |\n",
"| Experiment: 0 | Episode: 703 | Score: -110.17 | Avg Score: -142.6 |\n",
"| Experiment: 0 | Episode: 704 | Score: -198.87 | Avg Score: -143.27 |\n",
"| Experiment: 0 | Episode: 705 | Score: -71.29 | Avg Score: -141.03 |\n",
"| Experiment: 0 | Episode: 706 | Score: -82.93 | Avg Score: -139.61 |\n",
"| Experiment: 0 | Episode: 707 | Score: -79.38 | Avg Score: -137.68 |\n",
"| Experiment: 0 | Episode: 708 | Score: -93.4 | Avg Score: -137.71 |\n",
"| Experiment: 0 | Episode: 709 | Score: -162.3 | Avg Score: -138.8 |\n",
"| Experiment: 0 | Episode: 710 | Score: -69.94 | Avg Score: -138.95 |\n",
"| Experiment: 0 | Episode: 711 | Score: -72.37 | Avg Score: -137.49 |\n",
"| Experiment: 0 | Episode: 712 | Score: -69.7 | Avg Score: -135.8 |\n",
"| Experiment: 0 | Episode: 713 | Score: -92.95 | Avg Score: -134.68 |\n",
"| Experiment: 0 | Episode: 714 | Score: -94.35 | Avg Score: -134.66 |\n",
"| Experiment: 0 | Episode: 715 | Score: -89.77 | Avg Score: -135.48 |\n",
"| Experiment: 0 | Episode: 716 | Score: -90.55 | Avg Score: -133.93 |\n",
"| Experiment: 0 | Episode: 717 | Score: -62.72 | Avg Score: -131.92 |\n",
"| Experiment: 0 | Episode: 718 | Score: -91.98 | Avg Score: -131.85 |\n",
"| Experiment: 0 | Episode: 719 | Score: -94.45 | Avg Score: -129.9 |\n",
"| Experiment: 0 | Episode: 720 | Score: -71.42 | Avg Score: -130.59 |\n",
"| Experiment: 0 | Episode: 721 | Score: -51.81 | Avg Score: -130.28 |\n",
"| Experiment: 0 | Episode: 722 | Score: -209.91 | Avg Score: -131.3 |\n",
"| Experiment: 0 | Episode: 723 | Score: -175.8 | Avg Score: -131.07 |\n",
"| Experiment: 0 | Episode: 724 | Score: -92.1 | Avg Score: -128.62 |\n",
"| Experiment: 0 | Episode: 725 | Score: -83.18 | Avg Score: -128.74 |\n",
"| Experiment: 0 | Episode: 726 | Score: -104.72 | Avg Score: -128.41 |\n",
"| Experiment: 0 | Episode: 727 | Score: -77.12 | Avg Score: -125.89 |\n",
"| Experiment: 0 | Episode: 728 | Score: -58.32 | Avg Score: -124.53 |\n",
"| Experiment: 0 | Episode: 729 | Score: -146.65 | Avg Score: -124.55 |\n",
"| Experiment: 0 | Episode: 730 | Score: -142.73 | Avg Score: -124.95 |\n",
"| Experiment: 0 | Episode: 731 | Score: -229.55 | Avg Score: -126.54 |\n",
"| Experiment: 0 | Episode: 732 | Score: -171.0 | Avg Score: -127.11 |\n",
"| Experiment: 0 | Episode: 733 | Score: -41.34 | Avg Score: -126.87 |\n",
"| Experiment: 0 | Episode: 734 | Score: -56.7 | Avg Score: -127.44 |\n",
"| Experiment: 0 | Episode: 735 | Score: -115.25 | Avg Score: -127.38 |\n",
"| Experiment: 0 | Episode: 736 | Score: -125.68 | Avg Score: -125.77 |\n",
"| Experiment: 0 | Episode: 737 | Score: -94.62 | Avg Score: -123.68 |\n",
"| Experiment: 0 | Episode: 738 | Score: -67.08 | Avg Score: -121.86 |\n",
"| Experiment: 0 | Episode: 739 | Score: -90.5 | Avg Score: -119.8 |\n",
"| Experiment: 0 | Episode: 740 | Score: -88.22 | Avg Score: -120.32 |\n",
"| Experiment: 0 | Episode: 741 | Score: -74.28 | Avg Score: -120.21 |\n",
"| Experiment: 0 | Episode: 742 | Score: -3.5 | Avg Score: -117.34 |\n",
"| Experiment: 0 | Episode: 743 | Score: -116.11 | Avg Score: -117.67 |\n",
"| Experiment: 0 | Episode: 744 | Score: -82.99 | Avg Score: -117.23 |\n",
"| Experiment: 0 | Episode: 745 | Score: -62.81 | Avg Score: -117.04 |\n",
"| Experiment: 0 | Episode: 746 | Score: -90.93 | Avg Score: -114.55 |\n",
"| Experiment: 0 | Episode: 747 | Score: -134.28 | Avg Score: -114.9 |\n",
"| Experiment: 0 | Episode: 748 | Score: -160.73 | Avg Score: -115.12 |\n",
"| Experiment: 0 | Episode: 749 | Score: -38.04 | Avg Score: -114.02 |\n",
"| Experiment: 0 | Episode: 750 | Score: -78.51 | Avg Score: -112.02 |\n",
"| Experiment: 0 | Episode: 751 | Score: -69.86 | Avg Score: -110.8 |\n",
"| Experiment: 0 | Episode: 752 | Score: -306.86 | Avg Score: -112.7 |\n",
"| Experiment: 0 | Episode: 753 | Score: -112.28 | Avg Score: -113.64 |\n",
"| Experiment: 0 | Episode: 754 | Score: -277.34 | Avg Score: -115.63 |\n",
"| Experiment: 0 | Episode: 755 | Score: -47.97 | Avg Score: -112.38 |\n",
"| Experiment: 0 | Episode: 756 | Score: -125.4 | Avg Score: -113.1 |\n",
"| Experiment: 0 | Episode: 757 | Score: -265.05 | Avg Score: -114.77 |\n",
"| Experiment: 0 | Episode: 758 | Score: -80.89 | Avg Score: -113.89 |\n",
"| Experiment: 0 | Episode: 759 | Score: -104.53 | Avg Score: -113.86 |\n",
"| Experiment: 0 | Episode: 760 | Score: -125.55 | Avg Score: -114.3 |\n",
"| Experiment: 0 | Episode: 761 | Score: -46.65 | Avg Score: -111.22 |\n",
"| Experiment: 0 | Episode: 762 | Score: -52.52 | Avg Score: -110.49 |\n",
"| Experiment: 0 | Episode: 763 | Score: -51.89 | Avg Score: -110.51 |\n",
"| Experiment: 0 | Episode: 764 | Score: -188.09 | Avg Score: -111.52 |\n",
"| Experiment: 0 | Episode: 765 | Score: -74.96 | Avg Score: -111.33 |\n",
"| Experiment: 0 | Episode: 766 | Score: -36.24 | Avg Score: -110.44 |\n",
"| Experiment: 0 | Episode: 767 | Score: -72.91 | Avg Score: -111.37 |\n",
"| Experiment: 0 | Episode: 768 | Score: -80.04 | Avg Score: -111.02 |\n",
"| Experiment: 0 | Episode: 769 | Score: -88.61 | Avg Score: -110.89 |\n",
"| Experiment: 0 | Episode: 770 | Score: -133.2 | Avg Score: -110.17 |\n",
"| Experiment: 0 | Episode: 771 | Score: -84.87 | Avg Score: -109.69 |\n",
"| Experiment: 0 | Episode: 772 | Score: -194.44 | Avg Score: -110.53 |\n",
"| Experiment: 0 | Episode: 773 | Score: -66.34 | Avg Score: -109.32 |\n",
"| Experiment: 0 | Episode: 774 | Score: -56.95 | Avg Score: -107.87 |\n",
"| Experiment: 0 | Episode: 775 | Score: -133.54 | Avg Score: -108.65 |\n",
"| Experiment: 0 | Episode: 776 | Score: -151.64 | Avg Score: -108.87 |\n",
"| Experiment: 0 | Episode: 777 | Score: -60.24 | Avg Score: -108.88 |\n",
"| Experiment: 0 | Episode: 778 | Score: -190.53 | Avg Score: -110.11 |\n",
"| Experiment: 0 | Episode: 779 | Score: -101.56 | Avg Score: -110.52 |\n",
"| Experiment: 0 | Episode: 780 | Score: -70.53 | Avg Score: -110.75 |\n",
"| Experiment: 0 | Episode: 781 | Score: -65.29 | Avg Score: -110.03 |\n",
"| Experiment: 0 | Episode: 782 | Score: -63.91 | Avg Score: -109.89 |\n",
"| Experiment: 0 | Episode: 783 | Score: -121.15 | Avg Score: -108.6 |\n",
"| Experiment: 0 | Episode: 784 | Score: -48.98 | Avg Score: -108.1 |\n",
"| Experiment: 0 | Episode: 785 | Score: -94.42 | Avg Score: -107.47 |\n",
"| Experiment: 0 | Episode: 786 | Score: -43.78 | Avg Score: -106.13 |\n",
"| Experiment: 0 | Episode: 787 | Score: -68.69 | Avg Score: -105.88 |\n",
"| Experiment: 0 | Episode: 788 | Score: -78.54 | Avg Score: -106.36 |\n",
"| Experiment: 0 | Episode: 789 | Score: -25.24 | Avg Score: -104.96 |\n",
"| Experiment: 0 | Episode: 790 | Score: -66.03 | Avg Score: -104.46 |\n",
"| Experiment: 0 | Episode: 791 | Score: -297.2 | Avg Score: -106.62 |\n",
"| Experiment: 0 | Episode: 792 | Score: -32.26 | Avg Score: -106.56 |\n",
"| Experiment: 0 | Episode: 793 | Score: -90.18 | Avg Score: -104.72 |\n",
"| Experiment: 0 | Episode: 794 | Score: -88.04 | Avg Score: -104.16 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 795 | Score: -73.44 | Avg Score: -103.02 |\n",
"| Experiment: 0 | Episode: 796 | Score: -123.74 | Avg Score: -103.52 |\n",
"| Experiment: 0 | Episode: 797 | Score: -45.76 | Avg Score: -102.06 |\n",
"| Experiment: 0 | Episode: 798 | Score: -74.78 | Avg Score: -100.46 |\n",
"| Experiment: 0 | Episode: 799 | Score: -87.15 | Avg Score: -100.47 |\n",
"| Experiment: 0 | Episode: 800 | Score: -74.06 | Avg Score: -100.52 |\n",
"| Experiment: 0 | Episode: 801 | Score: -56.22 | Avg Score: -99.76 |\n",
"| Experiment: 0 | Episode: 802 | Score: -57.35 | Avg Score: -99.91 |\n",
"| Experiment: 0 | Episode: 803 | Score: -50.02 | Avg Score: -99.3 |\n",
"| Experiment: 0 | Episode: 804 | Score: -97.49 | Avg Score: -98.29 |\n",
"| Experiment: 0 | Episode: 805 | Score: -147.78 | Avg Score: -99.06 |\n",
"| Experiment: 0 | Episode: 806 | Score: -134.76 | Avg Score: -99.57 |\n",
"| Experiment: 0 | Episode: 807 | Score: -20.88 | Avg Score: -98.99 |\n",
"| Experiment: 0 | Episode: 808 | Score: -159.5 | Avg Score: -99.65 |\n",
"| Experiment: 0 | Episode: 809 | Score: -64.26 | Avg Score: -98.67 |\n",
"| Experiment: 0 | Episode: 810 | Score: -210.84 | Avg Score: -100.08 |\n",
"| Experiment: 0 | Episode: 811 | Score: -61.06 | Avg Score: -99.97 |\n",
"| Experiment: 0 | Episode: 812 | Score: -137.15 | Avg Score: -100.64 |\n",
"| Experiment: 0 | Episode: 813 | Score: -167.93 | Avg Score: -101.39 |\n",
"| Experiment: 0 | Episode: 814 | Score: -163.53 | Avg Score: -102.08 |\n",
"| Experiment: 0 | Episode: 815 | Score: -82.67 | Avg Score: -102.01 |\n",
"| Experiment: 0 | Episode: 816 | Score: -99.99 | Avg Score: -102.11 |\n",
"| Experiment: 0 | Episode: 817 | Score: -91.26 | Avg Score: -102.39 |\n",
"| Experiment: 0 | Episode: 818 | Score: -112.19 | Avg Score: -102.59 |\n",
"| Experiment: 0 | Episode: 819 | Score: -66.54 | Avg Score: -102.31 |\n",
"| Experiment: 0 | Episode: 820 | Score: -116.27 | Avg Score: -102.76 |\n",
"| Experiment: 0 | Episode: 821 | Score: -88.08 | Avg Score: -103.13 |\n",
"| Experiment: 0 | Episode: 822 | Score: -158.91 | Avg Score: -102.62 |\n",
"| Experiment: 0 | Episode: 823 | Score: -41.75 | Avg Score: -101.27 |\n",
"| Experiment: 0 | Episode: 824 | Score: -293.76 | Avg Score: -103.29 |\n",
"| Experiment: 0 | Episode: 825 | Score: -68.88 | Avg Score: -103.15 |\n",
"| Experiment: 0 | Episode: 826 | Score: -62.91 | Avg Score: -102.73 |\n",
"| Experiment: 0 | Episode: 827 | Score: -213.86 | Avg Score: -104.1 |\n",
"| Experiment: 0 | Episode: 828 | Score: -64.55 | Avg Score: -104.16 |\n",
"| Experiment: 0 | Episode: 829 | Score: -113.06 | Avg Score: -103.82 |\n",
"| Experiment: 0 | Episode: 830 | Score: -107.56 | Avg Score: -103.47 |\n",
"| Experiment: 0 | Episode: 831 | Score: -64.06 | Avg Score: -101.82 |\n",
"| Experiment: 0 | Episode: 832 | Score: -42.23 | Avg Score: -100.53 |\n",
"| Experiment: 0 | Episode: 833 | Score: -285.01 | Avg Score: -102.97 |\n",
"| Experiment: 0 | Episode: 834 | Score: -39.66 | Avg Score: -102.8 |\n",
"| Experiment: 0 | Episode: 835 | Score: -73.36 | Avg Score: -102.38 |\n",
"| Experiment: 0 | Episode: 836 | Score: -93.2 | Avg Score: -102.05 |\n",
"| Experiment: 0 | Episode: 837 | Score: -79.04 | Avg Score: -101.9 |\n",
"| Experiment: 0 | Episode: 838 | Score: -21.87 | Avg Score: -101.44 |\n",
"| Experiment: 0 | Episode: 839 | Score: -183.16 | Avg Score: -102.37 |\n",
"| Experiment: 0 | Episode: 840 | Score: -92.03 | Avg Score: -102.41 |\n",
"| Experiment: 0 | Episode: 841 | Score: -56.21 | Avg Score: -102.23 |\n",
"| Experiment: 0 | Episode: 842 | Score: -311.61 | Avg Score: -105.31 |\n",
"| Experiment: 0 | Episode: 843 | Score: -56.06 | Avg Score: -104.71 |\n",
"| Experiment: 0 | Episode: 844 | Score: -57.17 | Avg Score: -104.45 |\n",
"| Experiment: 0 | Episode: 845 | Score: -220.98 | Avg Score: -106.03 |\n",
"| Experiment: 0 | Episode: 846 | Score: -71.84 | Avg Score: -105.84 |\n",
"| Experiment: 0 | Episode: 847 | Score: -172.22 | Avg Score: -106.22 |\n",
"| Experiment: 0 | Episode: 848 | Score: -93.96 | Avg Score: -105.55 |\n",
"| Experiment: 0 | Episode: 849 | Score: -223.05 | Avg Score: -107.4 |\n",
"| Experiment: 0 | Episode: 850 | Score: -95.04 | Avg Score: -107.57 |\n",
"| Experiment: 0 | Episode: 851 | Score: -242.77 | Avg Score: -109.3 |\n",
"| Experiment: 0 | Episode: 852 | Score: -50.43 | Avg Score: -106.73 |\n",
"| Experiment: 0 | Episode: 853 | Score: -85.03 | Avg Score: -106.46 |\n",
"| Experiment: 0 | Episode: 854 | Score: -104.68 | Avg Score: -104.73 |\n",
"| Experiment: 0 | Episode: 855 | Score: -4.12 | Avg Score: -104.3 |\n",
"| Experiment: 0 | Episode: 856 | Score: -115.83 | Avg Score: -104.2 |\n",
"| Experiment: 0 | Episode: 857 | Score: -59.25 | Avg Score: -102.14 |\n",
"| Experiment: 0 | Episode: 858 | Score: -124.61 | Avg Score: -102.58 |\n",
"| Experiment: 0 | Episode: 859 | Score: -108.19 | Avg Score: -102.62 |\n",
"| Experiment: 0 | Episode: 860 | Score: -69.4 | Avg Score: -102.05 |\n",
"| Experiment: 0 | Episode: 861 | Score: -89.05 | Avg Score: -102.48 |\n",
"| Experiment: 0 | Episode: 862 | Score: -265.38 | Avg Score: -104.61 |\n",
"| Experiment: 0 | Episode: 863 | Score: -54.95 | Avg Score: -104.64 |\n",
"| Experiment: 0 | Episode: 864 | Score: -125.3 | Avg Score: -104.01 |\n",
"| Experiment: 0 | Episode: 865 | Score: -259.39 | Avg Score: -105.85 |\n",
"| Experiment: 0 | Episode: 866 | Score: -94.5 | Avg Score: -106.44 |\n",
"| Experiment: 0 | Episode: 867 | Score: -99.63 | Avg Score: -106.7 |\n",
"| Experiment: 0 | Episode: 868 | Score: -108.38 | Avg Score: -106.99 |\n",
"| Experiment: 0 | Episode: 869 | Score: -69.96 | Avg Score: -106.8 |\n",
"| Experiment: 0 | Episode: 870 | Score: -95.86 | Avg Score: -106.43 |\n",
"| Experiment: 0 | Episode: 871 | Score: -76.27 | Avg Score: -106.34 |\n",
"| Experiment: 0 | Episode: 872 | Score: -116.43 | Avg Score: -105.56 |\n",
"| Experiment: 0 | Episode: 873 | Score: -51.59 | Avg Score: -105.41 |\n",
"| Experiment: 0 | Episode: 874 | Score: -125.35 | Avg Score: -106.1 |\n",
"| Experiment: 0 | Episode: 875 | Score: -73.05 | Avg Score: -105.49 |\n",
"| Experiment: 0 | Episode: 876 | Score: -94.18 | Avg Score: -104.92 |\n",
"| Experiment: 0 | Episode: 877 | Score: -90.77 | Avg Score: -105.22 |\n",
"| Experiment: 0 | Episode: 878 | Score: -62.26 | Avg Score: -103.94 |\n",
"| Experiment: 0 | Episode: 879 | Score: -114.41 | Avg Score: -104.07 |\n",
"| Experiment: 0 | Episode: 880 | Score: -32.94 | Avg Score: -103.69 |\n",
"| Experiment: 0 | Episode: 881 | Score: -129.07 | Avg Score: -104.33 |\n",
"| Experiment: 0 | Episode: 882 | Score: -220.71 | Avg Score: -105.9 |\n",
"| Experiment: 0 | Episode: 883 | Score: -131.2 | Avg Score: -106.0 |\n",
"| Experiment: 0 | Episode: 884 | Score: -30.24 | Avg Score: -105.81 |\n",
"| Experiment: 0 | Episode: 885 | Score: -97.8 | Avg Score: -105.85 |\n",
"| Experiment: 0 | Episode: 886 | Score: -88.69 | Avg Score: -106.3 |\n",
"| Experiment: 0 | Episode: 887 | Score: -92.39 | Avg Score: -106.53 |\n",
"| Experiment: 0 | Episode: 888 | Score: -129.19 | Avg Score: -107.04 |\n",
"| Experiment: 0 | Episode: 889 | Score: -112.58 | Avg Score: -107.91 |\n",
"| Experiment: 0 | Episode: 890 | Score: -116.88 | Avg Score: -108.42 |\n",
"| Experiment: 0 | Episode: 891 | Score: -114.44 | Avg Score: -106.59 |\n",
"| Experiment: 0 | Episode: 892 | Score: -60.37 | Avg Score: -106.87 |\n",
"| Experiment: 0 | Episode: 893 | Score: -82.49 | Avg Score: -106.8 |\n",
"| Experiment: 0 | Episode: 894 | Score: -219.44 | Avg Score: -108.11 |\n",
"| Experiment: 0 | Episode: 895 | Score: -64.97 | Avg Score: -108.03 |\n",
"| Experiment: 0 | Episode: 896 | Score: -156.83 | Avg Score: -108.36 |\n",
"| Experiment: 0 | Episode: 897 | Score: 23.67 | Avg Score: -107.66 |\n",
"| Experiment: 0 | Episode: 898 | Score: -90.85 | Avg Score: -107.82 |\n",
"| Experiment: 0 | Episode: 899 | Score: -85.15 | Avg Score: -107.8 |\n",
"| Experiment: 0 | Episode: 900 | Score: -147.2 | Avg Score: -108.54 |\n",
"| Experiment: 0 | Episode: 901 | Score: -55.24 | Avg Score: -108.53 |\n",
"| Experiment: 0 | Episode: 902 | Score: -100.82 | Avg Score: -108.96 |\n",
"| Experiment: 0 | Episode: 903 | Score: -126.46 | Avg Score: -109.73 |\n",
"| Experiment: 0 | Episode: 904 | Score: -178.84 | Avg Score: -110.54 |\n",
"| Experiment: 0 | Episode: 905 | Score: -133.01 | Avg Score: -110.39 |\n",
"| Experiment: 0 | Episode: 906 | Score: -184.86 | Avg Score: -110.89 |\n",
"| Experiment: 0 | Episode: 907 | Score: -143.68 | Avg Score: -112.12 |\n",
"| Experiment: 0 | Episode: 908 | Score: -106.78 | Avg Score: -111.59 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 909 | Score: -58.7 | Avg Score: -111.54 |\n",
"| Experiment: 0 | Episode: 910 | Score: -35.91 | Avg Score: -109.79 |\n",
"| Experiment: 0 | Episode: 911 | Score: -110.89 | Avg Score: -110.29 |\n",
"| Experiment: 0 | Episode: 912 | Score: -88.86 | Avg Score: -109.8 |\n",
"| Experiment: 0 | Episode: 913 | Score: -71.5 | Avg Score: -108.84 |\n",
"| Experiment: 0 | Episode: 914 | Score: -60.19 | Avg Score: -107.81 |\n",
"| Experiment: 0 | Episode: 915 | Score: -196.35 | Avg Score: -108.94 |\n",
"| Experiment: 0 | Episode: 916 | Score: -200.37 | Avg Score: -109.95 |\n",
"| Experiment: 0 | Episode: 917 | Score: -104.16 | Avg Score: -110.07 |\n",
"| Experiment: 0 | Episode: 918 | Score: -125.4 | Avg Score: -110.21 |\n",
"| Experiment: 0 | Episode: 919 | Score: -37.31 | Avg Score: -109.91 |\n",
"| Experiment: 0 | Episode: 920 | Score: -156.64 | Avg Score: -110.32 |\n",
"| Experiment: 0 | Episode: 921 | Score: -64.09 | Avg Score: -110.08 |\n",
"| Experiment: 0 | Episode: 922 | Score: -80.92 | Avg Score: -109.3 |\n",
"| Experiment: 0 | Episode: 923 | Score: -88.93 | Avg Score: -109.77 |\n",
"| Experiment: 0 | Episode: 924 | Score: -75.09 | Avg Score: -107.58 |\n",
"| Experiment: 0 | Episode: 925 | Score: -311.36 | Avg Score: -110.01 |\n",
"| Experiment: 0 | Episode: 926 | Score: -211.85 | Avg Score: -111.5 |\n",
"| Experiment: 0 | Episode: 927 | Score: -58.21 | Avg Score: -109.94 |\n",
"| Experiment: 0 | Episode: 928 | Score: -242.37 | Avg Score: -111.72 |\n",
"| Experiment: 0 | Episode: 929 | Score: -69.5 | Avg Score: -111.28 |\n",
"| Experiment: 0 | Episode: 930 | Score: -90.05 | Avg Score: -111.11 |\n",
"| Experiment: 0 | Episode: 931 | Score: -113.92 | Avg Score: -111.61 |\n",
"| Experiment: 0 | Episode: 932 | Score: -198.48 | Avg Score: -113.17 |\n",
"| Experiment: 0 | Episode: 933 | Score: -81.07 | Avg Score: -111.13 |\n",
"| Experiment: 0 | Episode: 934 | Score: -61.47 | Avg Score: -111.35 |\n",
"| Experiment: 0 | Episode: 935 | Score: -65.31 | Avg Score: -111.27 |\n",
"| Experiment: 0 | Episode: 936 | Score: -31.95 | Avg Score: -110.66 |\n",
"| Experiment: 0 | Episode: 937 | Score: -36.84 | Avg Score: -110.23 |\n",
"| Experiment: 0 | Episode: 938 | Score: -104.74 | Avg Score: -111.06 |\n",
"| Experiment: 0 | Episode: 939 | Score: -99.25 | Avg Score: -110.22 |\n",
"| Experiment: 0 | Episode: 940 | Score: -225.99 | Avg Score: -111.56 |\n",
"| Experiment: 0 | Episode: 941 | Score: -90.0 | Avg Score: -111.9 |\n",
"| Experiment: 0 | Episode: 942 | Score: -195.92 | Avg Score: -110.74 |\n",
"| Experiment: 0 | Episode: 943 | Score: -102.86 | Avg Score: -111.21 |\n",
"| Experiment: 0 | Episode: 944 | Score: -71.36 | Avg Score: -111.35 |\n",
"| Experiment: 0 | Episode: 945 | Score: -76.0 | Avg Score: -109.9 |\n",
"| Experiment: 0 | Episode: 946 | Score: -131.65 | Avg Score: -110.5 |\n",
"| Experiment: 0 | Episode: 947 | Score: -10.23 | Avg Score: -108.88 |\n",
"| Experiment: 0 | Episode: 948 | Score: -69.48 | Avg Score: -108.64 |\n",
"| Experiment: 0 | Episode: 949 | Score: -115.22 | Avg Score: -107.56 |\n",
"| Experiment: 0 | Episode: 950 | Score: -88.61 | Avg Score: -107.49 |\n",
"| Experiment: 0 | Episode: 951 | Score: -122.14 | Avg Score: -106.29 |\n",
"| Experiment: 0 | Episode: 952 | Score: -147.82 | Avg Score: -107.26 |\n",
"| Experiment: 0 | Episode: 953 | Score: -77.22 | Avg Score: -107.18 |\n",
"| Experiment: 0 | Episode: 954 | Score: -85.07 | Avg Score: -106.99 |\n",
"| Experiment: 0 | Episode: 955 | Score: -82.99 | Avg Score: -107.78 |\n",
"| Experiment: 0 | Episode: 956 | Score: -79.5 | Avg Score: -107.41 |\n",
"| Experiment: 0 | Episode: 957 | Score: -88.02 | Avg Score: -107.7 |\n",
"| Experiment: 0 | Episode: 958 | Score: -99.96 | Avg Score: -107.45 |\n",
"| Experiment: 0 | Episode: 959 | Score: -71.86 | Avg Score: -107.09 |\n",
"| Experiment: 0 | Episode: 960 | Score: -59.15 | Avg Score: -106.99 |\n",
"| Experiment: 0 | Episode: 961 | Score: -60.13 | Avg Score: -106.7 |\n",
"| Experiment: 0 | Episode: 962 | Score: -78.16 | Avg Score: -104.83 |\n",
"| Experiment: 0 | Episode: 963 | Score: -40.71 | Avg Score: -104.68 |\n",
"| Experiment: 0 | Episode: 964 | Score: -51.71 | Avg Score: -103.95 |\n",
"| Experiment: 0 | Episode: 965 | Score: -152.18 | Avg Score: -102.88 |\n",
"| Experiment: 0 | Episode: 966 | Score: -48.23 | Avg Score: -102.41 |\n",
"| Experiment: 0 | Episode: 967 | Score: -221.67 | Avg Score: -103.63 |\n",
"| Experiment: 0 | Episode: 968 | Score: -137.14 | Avg Score: -103.92 |\n",
"| Experiment: 0 | Episode: 969 | Score: -79.86 | Avg Score: -104.02 |\n",
"| Experiment: 0 | Episode: 970 | Score: -58.2 | Avg Score: -103.64 |\n",
"| Experiment: 0 | Episode: 971 | Score: -62.05 | Avg Score: -103.5 |\n",
"| Experiment: 0 | Episode: 972 | Score: -64.82 | Avg Score: -102.99 |\n",
"| Experiment: 0 | Episode: 973 | Score: -100.05 | Avg Score: -103.47 |\n",
"| Experiment: 0 | Episode: 974 | Score: -66.56 | Avg Score: -102.88 |\n",
"| Experiment: 0 | Episode: 975 | Score: -103.48 | Avg Score: -103.19 |\n",
"| Experiment: 0 | Episode: 976 | Score: -127.5 | Avg Score: -103.52 |\n",
"| Experiment: 0 | Episode: 977 | Score: -64.99 | Avg Score: -103.26 |\n",
"| Experiment: 0 | Episode: 978 | Score: -123.14 | Avg Score: -103.87 |\n",
"| Experiment: 0 | Episode: 979 | Score: -131.66 | Avg Score: -104.04 |\n",
"| Experiment: 0 | Episode: 980 | Score: -98.15 | Avg Score: -104.7 |\n",
"| Experiment: 0 | Episode: 981 | Score: -71.08 | Avg Score: -104.12 |\n",
"| Experiment: 0 | Episode: 982 | Score: -73.19 | Avg Score: -102.64 |\n",
"| Experiment: 0 | Episode: 983 | Score: -143.6 | Avg Score: -102.76 |\n",
"| Experiment: 0 | Episode: 984 | Score: -125.77 | Avg Score: -103.72 |\n",
"| Experiment: 0 | Episode: 985 | Score: -83.17 | Avg Score: -103.57 |\n",
"| Experiment: 0 | Episode: 986 | Score: -64.99 | Avg Score: -103.34 |\n",
"| Experiment: 0 | Episode: 987 | Score: -68.85 | Avg Score: -103.1 |\n",
"| Experiment: 0 | Episode: 988 | Score: -88.89 | Avg Score: -102.7 |\n",
"| Experiment: 0 | Episode: 989 | Score: -91.06 | Avg Score: -102.48 |\n",
"| Experiment: 0 | Episode: 990 | Score: -94.59 | Avg Score: -102.26 |\n",
"| Experiment: 0 | Episode: 991 | Score: -83.88 | Avg Score: -101.95 |\n",
"| Experiment: 0 | Episode: 992 | Score: -60.38 | Avg Score: -101.95 |\n",
"| Experiment: 0 | Episode: 993 | Score: -35.9 | Avg Score: -101.49 |\n",
"| Experiment: 0 | Episode: 994 | Score: -64.22 | Avg Score: -99.94 |\n",
"| Experiment: 0 | Episode: 995 | Score: -65.77 | Avg Score: -99.94 |\n",
"| Experiment: 0 | Episode: 996 | Score: -112.35 | Avg Score: -99.5 |\n",
"| Experiment: 0 | Episode: 997 | Score: -52.23 | Avg Score: -100.26 |\n",
"| Experiment: 0 | Episode: 998 | Score: -79.86 | Avg Score: -100.15 |\n",
"| Experiment: 0 | Episode: 999 | Score: -45.01 | Avg Score: -99.75 |\n",
"| Experiment: 0 | Episode: 1000 | Score: -106.46 | Avg Score: -99.34 |\n",
"| Experiment: 0 | Episode: 1001 | Score: -51.92 | Avg Score: -99.31 |\n",
"| Experiment: 0 | Episode: 1002 | Score: -93.34 | Avg Score: -99.23 |\n",
"| Experiment: 0 | Episode: 1003 | Score: -29.04 | Avg Score: -98.26 |\n",
"| Experiment: 0 | Episode: 1004 | Score: -165.68 | Avg Score: -98.13 |\n",
"| Experiment: 0 | Episode: 1005 | Score: -87.75 | Avg Score: -97.67 |\n",
"| Experiment: 0 | Episode: 1006 | Score: -156.56 | Avg Score: -97.39 |\n",
"| Experiment: 0 | Episode: 1007 | Score: -82.85 | Avg Score: -96.78 |\n",
"| Experiment: 0 | Episode: 1008 | Score: 7.39 | Avg Score: -95.64 |\n",
"| Experiment: 0 | Episode: 1009 | Score: -88.79 | Avg Score: -95.94 |\n",
"| Experiment: 0 | Episode: 1010 | Score: -100.63 | Avg Score: -96.59 |\n",
"| Experiment: 0 | Episode: 1011 | Score: -57.43 | Avg Score: -96.05 |\n",
"| Experiment: 0 | Episode: 1012 | Score: -76.4 | Avg Score: -95.93 |\n",
"| Experiment: 0 | Episode: 1013 | Score: -175.43 | Avg Score: -96.97 |\n",
"| Experiment: 0 | Episode: 1014 | Score: -77.33 | Avg Score: -97.14 |\n",
"| Experiment: 0 | Episode: 1015 | Score: -53.14 | Avg Score: -95.71 |\n",
"| Experiment: 0 | Episode: 1016 | Score: -142.86 | Avg Score: -95.13 |\n",
"| Experiment: 0 | Episode: 1017 | Score: -256.37 | Avg Score: -96.66 |\n",
"| Experiment: 0 | Episode: 1018 | Score: -138.51 | Avg Score: -96.79 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 1019 | Score: -76.57 | Avg Score: -97.18 |\n",
"| Experiment: 0 | Episode: 1020 | Score: -118.89 | Avg Score: -96.8 |\n",
"| Experiment: 0 | Episode: 1021 | Score: -176.9 | Avg Score: -97.93 |\n",
"| Experiment: 0 | Episode: 1022 | Score: -84.66 | Avg Score: -97.97 |\n",
"| Experiment: 0 | Episode: 1023 | Score: -38.52 | Avg Score: -97.46 |\n",
"| Experiment: 0 | Episode: 1024 | Score: -65.81 | Avg Score: -97.37 |\n",
"| Experiment: 0 | Episode: 1025 | Score: -69.48 | Avg Score: -94.95 |\n",
"| Experiment: 0 | Episode: 1026 | Score: -53.3 | Avg Score: -93.37 |\n",
"| Experiment: 0 | Episode: 1027 | Score: -127.63 | Avg Score: -94.06 |\n",
"| Experiment: 0 | Episode: 1028 | Score: -76.43 | Avg Score: -92.4 |\n",
"| Experiment: 0 | Episode: 1029 | Score: -141.48 | Avg Score: -93.12 |\n",
"| Experiment: 0 | Episode: 1030 | Score: -116.68 | Avg Score: -93.39 |\n",
"| Experiment: 0 | Episode: 1031 | Score: -232.78 | Avg Score: -94.58 |\n",
"| Experiment: 0 | Episode: 1032 | Score: -104.77 | Avg Score: -93.64 |\n",
"| Experiment: 0 | Episode: 1033 | Score: -171.54 | Avg Score: -94.54 |\n",
"| Experiment: 0 | Episode: 1034 | Score: -71.61 | Avg Score: -94.64 |\n",
"| Experiment: 0 | Episode: 1035 | Score: -57.9 | Avg Score: -94.57 |\n",
"| Experiment: 0 | Episode: 1036 | Score: -88.67 | Avg Score: -95.14 |\n",
"| Experiment: 0 | Episode: 1037 | Score: -89.3 | Avg Score: -95.66 |\n",
"| Experiment: 0 | Episode: 1038 | Score: -87.76 | Avg Score: -95.49 |\n",
"| Experiment: 0 | Episode: 1039 | Score: -46.34 | Avg Score: -94.96 |\n",
"| Experiment: 0 | Episode: 1040 | Score: -67.42 | Avg Score: -93.38 |\n",
"| Experiment: 0 | Episode: 1041 | Score: -57.54 | Avg Score: -93.05 |\n",
"| Experiment: 0 | Episode: 1042 | Score: -50.3 | Avg Score: -91.6 |\n",
"| Experiment: 0 | Episode: 1043 | Score: -144.82 | Avg Score: -92.02 |\n",
"| Experiment: 0 | Episode: 1044 | Score: -121.26 | Avg Score: -92.52 |\n",
"| Experiment: 0 | Episode: 1045 | Score: -58.81 | Avg Score: -92.34 |\n",
"| Experiment: 0 | Episode: 1046 | Score: -65.63 | Avg Score: -91.68 |\n",
"| Experiment: 0 | Episode: 1047 | Score: -273.9 | Avg Score: -94.32 |\n",
"| Experiment: 0 | Episode: 1048 | Score: -129.37 | Avg Score: -94.92 |\n",
"| Experiment: 0 | Episode: 1049 | Score: -249.79 | Avg Score: -96.26 |\n",
"| Experiment: 0 | Episode: 1050 | Score: -131.44 | Avg Score: -96.69 |\n",
"| Experiment: 0 | Episode: 1051 | Score: -77.55 | Avg Score: -96.25 |\n",
"| Experiment: 0 | Episode: 1052 | Score: -6.66 | Avg Score: -94.84 |\n",
"| Experiment: 0 | Episode: 1053 | Score: -183.36 | Avg Score: -95.9 |\n",
"| Experiment: 0 | Episode: 1054 | Score: -140.6 | Avg Score: -96.45 |\n",
"| Experiment: 0 | Episode: 1055 | Score: -73.78 | Avg Score: -96.36 |\n",
"| Experiment: 0 | Episode: 1056 | Score: -93.31 | Avg Score: -96.5 |\n",
"| Experiment: 0 | Episode: 1057 | Score: -115.46 | Avg Score: -96.77 |\n",
"| Experiment: 0 | Episode: 1058 | Score: -45.34 | Avg Score: -96.23 |\n",
"| Experiment: 0 | Episode: 1059 | Score: -64.91 | Avg Score: -96.16 |\n",
"| Experiment: 0 | Episode: 1060 | Score: -85.78 | Avg Score: -96.42 |\n",
"| Experiment: 0 | Episode: 1061 | Score: -153.1 | Avg Score: -97.35 |\n",
"| Experiment: 0 | Episode: 1062 | Score: -142.26 | Avg Score: -97.99 |\n",
"| Experiment: 0 | Episode: 1063 | Score: -200.39 | Avg Score: -99.59 |\n",
"| Experiment: 0 | Episode: 1064 | Score: -76.23 | Avg Score: -99.84 |\n",
"| Experiment: 0 | Episode: 1065 | Score: -35.12 | Avg Score: -98.67 |\n",
"| Experiment: 0 | Episode: 1066 | Score: -78.86 | Avg Score: -98.97 |\n",
"| Experiment: 0 | Episode: 1067 | Score: -144.9 | Avg Score: -98.2 |\n",
"| Experiment: 0 | Episode: 1068 | Score: -61.47 | Avg Score: -97.45 |\n",
"| Experiment: 0 | Episode: 1069 | Score: -146.84 | Avg Score: -98.12 |\n",
"| Experiment: 0 | Episode: 1070 | Score: -123.77 | Avg Score: -98.77 |\n",
"| Experiment: 0 | Episode: 1071 | Score: -103.01 | Avg Score: -99.18 |\n",
"| Experiment: 0 | Episode: 1072 | Score: -108.93 | Avg Score: -99.62 |\n",
"| Experiment: 0 | Episode: 1073 | Score: -54.85 | Avg Score: -99.17 |\n",
"| Experiment: 0 | Episode: 1074 | Score: -109.26 | Avg Score: -99.6 |\n",
"| Experiment: 0 | Episode: 1075 | Score: -47.87 | Avg Score: -99.04 |\n",
"| Experiment: 0 | Episode: 1076 | Score: -64.59 | Avg Score: -98.41 |\n",
"| Experiment: 0 | Episode: 1077 | Score: -129.03 | Avg Score: -99.05 |\n",
"| Experiment: 0 | Episode: 1078 | Score: -184.68 | Avg Score: -99.67 |\n",
"| Experiment: 0 | Episode: 1079 | Score: -43.86 | Avg Score: -98.79 |\n",
"| Experiment: 0 | Episode: 1080 | Score: -82.3 | Avg Score: -98.63 |\n",
"| Experiment: 0 | Episode: 1081 | Score: -189.63 | Avg Score: -99.82 |\n",
"| Experiment: 0 | Episode: 1082 | Score: -84.75 | Avg Score: -99.93 |\n",
"| Experiment: 0 | Episode: 1083 | Score: -203.17 | Avg Score: -100.53 |\n",
"| Experiment: 0 | Episode: 1084 | Score: -34.28 | Avg Score: -99.61 |\n",
"| Experiment: 0 | Episode: 1085 | Score: -124.51 | Avg Score: -100.03 |\n",
"| Experiment: 0 | Episode: 1086 | Score: -75.41 | Avg Score: -100.13 |\n",
"| Experiment: 0 | Episode: 1087 | Score: -96.47 | Avg Score: -100.41 |\n",
"| Experiment: 0 | Episode: 1088 | Score: -21.03 | Avg Score: -99.73 |\n",
"| Experiment: 0 | Episode: 1089 | Score: -136.7 | Avg Score: -100.19 |\n",
"| Experiment: 0 | Episode: 1090 | Score: -39.81 | Avg Score: -99.64 |\n",
"| Experiment: 0 | Episode: 1091 | Score: -76.76 | Avg Score: -99.57 |\n",
"| Experiment: 0 | Episode: 1092 | Score: -118.6 | Avg Score: -100.15 |\n",
"| Experiment: 0 | Episode: 1093 | Score: -114.49 | Avg Score: -100.93 |\n",
"| Experiment: 0 | Episode: 1094 | Score: -134.09 | Avg Score: -101.63 |\n",
"| Experiment: 0 | Episode: 1095 | Score: -138.53 | Avg Score: -102.36 |\n",
"| Experiment: 0 | Episode: 1096 | Score: -123.89 | Avg Score: -102.48 |\n",
"| Experiment: 0 | Episode: 1097 | Score: -86.27 | Avg Score: -102.82 |\n",
"| Experiment: 0 | Episode: 1098 | Score: -126.17 | Avg Score: -103.28 |\n",
"| Experiment: 0 | Episode: 1099 | Score: -111.86 | Avg Score: -103.95 |\n",
"| Experiment: 0 | Episode: 1100 | Score: -47.72 | Avg Score: -103.36 |\n",
"| Experiment: 0 | Episode: 1101 | Score: -62.97 | Avg Score: -103.47 |\n",
"| Experiment: 0 | Episode: 1102 | Score: -93.43 | Avg Score: -103.47 |\n",
"| Experiment: 0 | Episode: 1103 | Score: -114.54 | Avg Score: -104.33 |\n",
"| Experiment: 0 | Episode: 1104 | Score: -75.44 | Avg Score: -103.43 |\n",
"| Experiment: 0 | Episode: 1105 | Score: -104.07 | Avg Score: -103.59 |\n",
"| Experiment: 0 | Episode: 1106 | Score: -129.34 | Avg Score: -103.32 |\n",
"| Experiment: 0 | Episode: 1107 | Score: -106.74 | Avg Score: -103.55 |\n",
"| Experiment: 0 | Episode: 1108 | Score: -103.45 | Avg Score: -104.66 |\n",
"| Experiment: 0 | Episode: 1109 | Score: -51.08 | Avg Score: -104.29 |\n",
"| Experiment: 0 | Episode: 1110 | Score: -94.69 | Avg Score: -104.23 |\n",
"| Experiment: 0 | Episode: 1111 | Score: -126.42 | Avg Score: -104.92 |\n",
"| Experiment: 0 | Episode: 1112 | Score: -63.07 | Avg Score: -104.78 |\n",
"| Experiment: 0 | Episode: 1113 | Score: -90.67 | Avg Score: -103.94 |\n",
"| Experiment: 0 | Episode: 1114 | Score: -157.01 | Avg Score: -104.73 |\n",
"| Experiment: 0 | Episode: 1115 | Score: -62.92 | Avg Score: -104.83 |\n",
"| Experiment: 0 | Episode: 1116 | Score: -34.87 | Avg Score: -103.75 |\n",
"| Experiment: 0 | Episode: 1117 | Score: -198.27 | Avg Score: -103.17 |\n",
"| Experiment: 0 | Episode: 1118 | Score: -86.28 | Avg Score: -102.65 |\n",
"| Experiment: 0 | Episode: 1119 | Score: -70.46 | Avg Score: -102.59 |\n",
"| Experiment: 0 | Episode: 1120 | Score: -70.43 | Avg Score: -102.1 |\n",
"| Experiment: 0 | Episode: 1121 | Score: -26.84 | Avg Score: -100.6 |\n",
"| Experiment: 0 | Episode: 1122 | Score: -134.64 | Avg Score: -101.1 |\n",
"| Experiment: 0 | Episode: 1123 | Score: -81.76 | Avg Score: -101.53 |\n",
"| Experiment: 0 | Episode: 1124 | Score: -56.54 | Avg Score: -101.44 |\n",
"| Experiment: 0 | Episode: 1125 | Score: -226.39 | Avg Score: -103.01 |\n",
"| Experiment: 0 | Episode: 1126 | Score: -116.14 | Avg Score: -103.64 |\n",
"| Experiment: 0 | Episode: 1127 | Score: -42.8 | Avg Score: -102.79 |\n",
"| Experiment: 0 | Episode: 1128 | Score: -141.34 | Avg Score: -103.44 |\n",
"| Experiment: 0 | Episode: 1129 | Score: -19.26 | Avg Score: -102.22 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 1130 | Score: -62.86 | Avg Score: -101.68 |\n",
"| Experiment: 0 | Episode: 1131 | Score: -124.17 | Avg Score: -100.59 |\n",
"| Experiment: 0 | Episode: 1132 | Score: -107.03 | Avg Score: -100.62 |\n",
"| Experiment: 0 | Episode: 1133 | Score: -97.08 | Avg Score: -99.87 |\n",
"| Experiment: 0 | Episode: 1134 | Score: -226.69 | Avg Score: -101.42 |\n",
"| Experiment: 0 | Episode: 1135 | Score: -182.85 | Avg Score: -102.67 |\n",
"| Experiment: 0 | Episode: 1136 | Score: -119.25 | Avg Score: -102.98 |\n",
"| Experiment: 0 | Episode: 1137 | Score: -256.0 | Avg Score: -104.64 |\n",
"| Experiment: 0 | Episode: 1138 | Score: -21.23 | Avg Score: -103.98 |\n",
"| Experiment: 0 | Episode: 1139 | Score: -75.79 | Avg Score: -104.27 |\n",
"| Experiment: 0 | Episode: 1140 | Score: -181.42 | Avg Score: -105.41 |\n",
"| Experiment: 0 | Episode: 1141 | Score: -111.57 | Avg Score: -105.95 |\n",
"| Experiment: 0 | Episode: 1142 | Score: -52.09 | Avg Score: -105.97 |\n",
"| Experiment: 0 | Episode: 1143 | Score: 0.67 | Avg Score: -104.52 |\n",
"| Experiment: 0 | Episode: 1144 | Score: -63.88 | Avg Score: -103.94 |\n",
"| Experiment: 0 | Episode: 1145 | Score: -101.43 | Avg Score: -104.37 |\n",
"| Experiment: 0 | Episode: 1146 | Score: -207.68 | Avg Score: -105.79 |\n",
"| Experiment: 0 | Episode: 1147 | Score: -98.57 | Avg Score: -104.04 |\n",
"| Experiment: 0 | Episode: 1148 | Score: -124.94 | Avg Score: -103.99 |\n",
"| Experiment: 0 | Episode: 1149 | Score: -134.43 | Avg Score: -102.84 |\n",
"| Experiment: 0 | Episode: 1150 | Score: -83.61 | Avg Score: -102.36 |\n",
"| Experiment: 0 | Episode: 1151 | Score: 18.74 | Avg Score: -101.4 |\n",
"| Experiment: 0 | Episode: 1152 | Score: -173.43 | Avg Score: -103.06 |\n",
"| Experiment: 0 | Episode: 1153 | Score: -57.06 | Avg Score: -101.8 |\n",
"| Experiment: 0 | Episode: 1154 | Score: -79.74 | Avg Score: -101.19 |\n",
"| Experiment: 0 | Episode: 1155 | Score: -204.92 | Avg Score: -102.5 |\n",
"| Experiment: 0 | Episode: 1156 | Score: -70.95 | Avg Score: -102.28 |\n",
"| Experiment: 0 | Episode: 1157 | Score: -68.52 | Avg Score: -101.81 |\n",
"| Experiment: 0 | Episode: 1158 | Score: -155.31 | Avg Score: -102.91 |\n",
"| Experiment: 0 | Episode: 1159 | Score: -90.92 | Avg Score: -103.17 |\n",
"| Experiment: 0 | Episode: 1160 | Score: -59.37 | Avg Score: -102.91 |\n",
"| Experiment: 0 | Episode: 1161 | Score: -34.69 | Avg Score: -101.72 |\n",
"| Experiment: 0 | Episode: 1162 | Score: -19.85 | Avg Score: -100.5 |\n",
"| Experiment: 0 | Episode: 1163 | Score: -44.09 | Avg Score: -98.94 |\n",
"| Experiment: 0 | Episode: 1164 | Score: -212.4 | Avg Score: -100.3 |\n",
"| Experiment: 0 | Episode: 1165 | Score: -63.81 | Avg Score: -100.58 |\n",
"| Experiment: 0 | Episode: 1166 | Score: -79.58 | Avg Score: -100.59 |\n",
"| Experiment: 0 | Episode: 1167 | Score: -96.94 | Avg Score: -100.11 |\n",
"| Experiment: 0 | Episode: 1168 | Score: -53.9 | Avg Score: -100.04 |\n",
"| Experiment: 0 | Episode: 1169 | Score: -121.12 | Avg Score: -99.78 |\n",
"| Experiment: 0 | Episode: 1170 | Score: -83.35 | Avg Score: -99.37 |\n",
"| Experiment: 0 | Episode: 1171 | Score: -67.32 | Avg Score: -99.02 |\n",
"| Experiment: 0 | Episode: 1172 | Score: -74.12 | Avg Score: -98.67 |\n",
"| Experiment: 0 | Episode: 1173 | Score: -116.4 | Avg Score: -99.29 |\n",
"| Experiment: 0 | Episode: 1174 | Score: -47.09 | Avg Score: -98.66 |\n",
"| Experiment: 0 | Episode: 1175 | Score: -174.4 | Avg Score: -99.93 |\n",
"| Experiment: 0 | Episode: 1176 | Score: -62.99 | Avg Score: -99.91 |\n",
"| Experiment: 0 | Episode: 1177 | Score: -12.39 | Avg Score: -98.75 |\n",
"| Experiment: 0 | Episode: 1178 | Score: -91.42 | Avg Score: -97.81 |\n",
"| Experiment: 0 | Episode: 1179 | Score: -84.77 | Avg Score: -98.22 |\n",
"| Experiment: 0 | Episode: 1180 | Score: -108.45 | Avg Score: -98.48 |\n",
"| Experiment: 0 | Episode: 1181 | Score: -44.68 | Avg Score: -97.04 |\n",
"| Experiment: 0 | Episode: 1182 | Score: -43.6 | Avg Score: -96.62 |\n",
"| Experiment: 0 | Episode: 1183 | Score: -209.54 | Avg Score: -96.69 |\n",
"| Experiment: 0 | Episode: 1184 | Score: -189.06 | Avg Score: -98.24 |\n",
"| Experiment: 0 | Episode: 1185 | Score: -124.33 | Avg Score: -98.23 |\n",
"| Experiment: 0 | Episode: 1186 | Score: -69.29 | Avg Score: -98.17 |\n",
"| Experiment: 0 | Episode: 1187 | Score: -121.29 | Avg Score: -98.42 |\n",
"| Experiment: 0 | Episode: 1188 | Score: -21.63 | Avg Score: -98.43 |\n",
"| Experiment: 0 | Episode: 1189 | Score: -71.57 | Avg Score: -97.78 |\n",
"| Experiment: 0 | Episode: 1190 | Score: -183.45 | Avg Score: -99.21 |\n",
"| Experiment: 0 | Episode: 1191 | Score: -50.23 | Avg Score: -98.95 |\n",
"| Experiment: 0 | Episode: 1192 | Score: -39.85 | Avg Score: -98.16 |\n",
"| Experiment: 0 | Episode: 1193 | Score: -89.87 | Avg Score: -97.91 |\n",
"| Experiment: 0 | Episode: 1194 | Score: -74.7 | Avg Score: -97.32 |\n",
"| Experiment: 0 | Episode: 1195 | Score: -13.52 | Avg Score: -96.07 |\n",
"| Experiment: 0 | Episode: 1196 | Score: -33.3 | Avg Score: -95.16 |\n",
"| Experiment: 0 | Episode: 1197 | Score: -70.91 | Avg Score: -95.01 |\n",
"| Experiment: 0 | Episode: 1198 | Score: -28.87 | Avg Score: -94.04 |\n",
"| Experiment: 0 | Episode: 1199 | Score: -138.45 | Avg Score: -94.3 |\n",
"| Experiment: 0 | Episode: 1200 | Score: -165.41 | Avg Score: -95.48 |\n",
"| Experiment: 0 | Episode: 1201 | Score: -154.99 | Avg Score: -96.4 |\n",
"| Experiment: 0 | Episode: 1202 | Score: -61.93 | Avg Score: -96.08 |\n",
"| Experiment: 0 | Episode: 1203 | Score: -113.86 | Avg Score: -96.08 |\n",
"| Experiment: 0 | Episode: 1204 | Score: 1.35 | Avg Score: -95.31 |\n",
"| Experiment: 0 | Episode: 1205 | Score: -105.96 | Avg Score: -95.33 |\n",
"| Experiment: 0 | Episode: 1206 | Score: -60.96 | Avg Score: -94.64 |\n",
"| Experiment: 0 | Episode: 1207 | Score: -90.32 | Avg Score: -94.48 |\n",
"| Experiment: 0 | Episode: 1208 | Score: -146.04 | Avg Score: -94.91 |\n",
"| Experiment: 0 | Episode: 1209 | Score: -67.13 | Avg Score: -95.07 |\n",
"| Experiment: 0 | Episode: 1210 | Score: -33.42 | Avg Score: -94.45 |\n",
"| Experiment: 0 | Episode: 1211 | Score: -119.56 | Avg Score: -94.39 |\n",
"| Experiment: 0 | Episode: 1212 | Score: -57.24 | Avg Score: -94.33 |\n",
"| Experiment: 0 | Episode: 1213 | Score: -123.19 | Avg Score: -94.65 |\n",
"| Experiment: 0 | Episode: 1214 | Score: -153.44 | Avg Score: -94.62 |\n",
"| Experiment: 0 | Episode: 1215 | Score: -56.72 | Avg Score: -94.55 |\n",
"| Experiment: 0 | Episode: 1216 | Score: -79.49 | Avg Score: -95.0 |\n",
"| Experiment: 0 | Episode: 1217 | Score: -38.3 | Avg Score: -93.4 |\n",
"| Experiment: 0 | Episode: 1218 | Score: -219.99 | Avg Score: -94.74 |\n",
"| Experiment: 0 | Episode: 1219 | Score: -83.77 | Avg Score: -94.87 |\n",
"| Experiment: 0 | Episode: 1220 | Score: -200.71 | Avg Score: -96.17 |\n",
"| Experiment: 0 | Episode: 1221 | Score: -67.89 | Avg Score: -96.58 |\n",
"| Experiment: 0 | Episode: 1222 | Score: -97.15 | Avg Score: -96.21 |\n",
"| Experiment: 0 | Episode: 1223 | Score: -26.75 | Avg Score: -95.66 |\n",
"| Experiment: 0 | Episode: 1224 | Score: -188.5 | Avg Score: -96.98 |\n",
"| Experiment: 0 | Episode: 1225 | Score: -67.94 | Avg Score: -95.39 |\n",
"| Experiment: 0 | Episode: 1226 | Score: -173.55 | Avg Score: -95.97 |\n",
"| Experiment: 0 | Episode: 1227 | Score: -51.52 | Avg Score: -96.06 |\n",
"| Experiment: 0 | Episode: 1228 | Score: -65.27 | Avg Score: -95.3 |\n",
"| Experiment: 0 | Episode: 1229 | Score: -84.71 | Avg Score: -95.95 |\n",
"| Experiment: 0 | Episode: 1230 | Score: -68.7 | Avg Score: -96.01 |\n",
"| Experiment: 0 | Episode: 1231 | Score: -47.67 | Avg Score: -95.24 |\n",
"| Experiment: 0 | Episode: 1232 | Score: -172.04 | Avg Score: -95.89 |\n",
"| Experiment: 0 | Episode: 1233 | Score: -73.69 | Avg Score: -95.66 |\n",
"| Experiment: 0 | Episode: 1234 | Score: -101.71 | Avg Score: -94.41 |\n",
"| Experiment: 0 | Episode: 1235 | Score: -220.99 | Avg Score: -94.79 |\n",
"| Experiment: 0 | Episode: 1236 | Score: -103.94 | Avg Score: -94.64 |\n",
"| Experiment: 0 | Episode: 1237 | Score: -110.54 | Avg Score: -93.18 |\n",
"| Experiment: 0 | Episode: 1238 | Score: -79.57 | Avg Score: -93.77 |\n",
"| Experiment: 0 | Episode: 1239 | Score: -58.5 | Avg Score: -93.59 |\n",
"| Experiment: 0 | Episode: 1240 | Score: -295.16 | Avg Score: -94.73 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 1241 | Score: -109.8 | Avg Score: -94.71 |\n",
"| Experiment: 0 | Episode: 1242 | Score: -55.82 | Avg Score: -94.75 |\n",
"| Experiment: 0 | Episode: 1243 | Score: -108.71 | Avg Score: -95.84 |\n",
"| Experiment: 0 | Episode: 1244 | Score: -153.31 | Avg Score: -96.74 |\n",
"| Experiment: 0 | Episode: 1245 | Score: -40.95 | Avg Score: -96.13 |\n",
"| Experiment: 0 | Episode: 1246 | Score: -113.83 | Avg Score: -95.2 |\n",
"| Experiment: 0 | Episode: 1247 | Score: -96.48 | Avg Score: -95.17 |\n",
"| Experiment: 0 | Episode: 1248 | Score: -204.47 | Avg Score: -95.97 |\n",
"| Experiment: 0 | Episode: 1249 | Score: -79.19 | Avg Score: -95.42 |\n",
"| Experiment: 0 | Episode: 1250 | Score: -150.96 | Avg Score: -96.09 |\n",
"| Experiment: 0 | Episode: 1251 | Score: -46.72 | Avg Score: -96.75 |\n",
"| Experiment: 0 | Episode: 1252 | Score: -94.12 | Avg Score: -95.95 |\n",
"| Experiment: 0 | Episode: 1253 | Score: -89.48 | Avg Score: -96.28 |\n",
"| Experiment: 0 | Episode: 1254 | Score: -21.91 | Avg Score: -95.7 |\n",
"| Experiment: 0 | Episode: 1255 | Score: -77.58 | Avg Score: -94.43 |\n",
"| Experiment: 0 | Episode: 1256 | Score: -53.97 | Avg Score: -94.26 |\n",
"| Experiment: 0 | Episode: 1257 | Score: -60.13 | Avg Score: -94.17 |\n",
"| Experiment: 0 | Episode: 1258 | Score: -128.01 | Avg Score: -93.9 |\n",
"| Experiment: 0 | Episode: 1259 | Score: -127.01 | Avg Score: -94.26 |\n",
"| Experiment: 0 | Episode: 1260 | Score: -135.0 | Avg Score: -95.02 |\n",
"| Experiment: 0 | Episode: 1261 | Score: -36.68 | Avg Score: -95.04 |\n",
"| Experiment: 0 | Episode: 1262 | Score: -125.66 | Avg Score: -96.09 |\n",
"| Experiment: 0 | Episode: 1263 | Score: -110.7 | Avg Score: -96.76 |\n",
"| Experiment: 0 | Episode: 1264 | Score: -75.69 | Avg Score: -95.39 |\n",
"| Experiment: 0 | Episode: 1265 | Score: -161.38 | Avg Score: -96.37 |\n",
"| Experiment: 0 | Episode: 1266 | Score: -187.86 | Avg Score: -97.45 |\n",
"| Experiment: 0 | Episode: 1267 | Score: -65.96 | Avg Score: -97.14 |\n",
"| Experiment: 0 | Episode: 1268 | Score: -80.21 | Avg Score: -97.4 |\n",
"| Experiment: 0 | Episode: 1269 | Score: -39.4 | Avg Score: -96.59 |\n",
"| Experiment: 0 | Episode: 1270 | Score: -155.25 | Avg Score: -97.31 |\n",
"| Experiment: 0 | Episode: 1271 | Score: -77.56 | Avg Score: -97.41 |\n",
"| Experiment: 0 | Episode: 1272 | Score: -58.38 | Avg Score: -97.25 |\n",
"| Experiment: 0 | Episode: 1273 | Score: -52.05 | Avg Score: -96.61 |\n",
"| Experiment: 0 | Episode: 1274 | Score: -100.46 | Avg Score: -97.14 |\n",
"| Experiment: 0 | Episode: 1275 | Score: -150.38 | Avg Score: -96.9 |\n",
"| Experiment: 0 | Episode: 1276 | Score: -176.47 | Avg Score: -98.04 |\n",
"| Experiment: 0 | Episode: 1277 | Score: -76.81 | Avg Score: -98.68 |\n",
"| Experiment: 0 | Episode: 1278 | Score: -41.08 | Avg Score: -98.18 |\n",
"| Experiment: 0 | Episode: 1279 | Score: -155.52 | Avg Score: -98.88 |\n",
"| Experiment: 0 | Episode: 1280 | Score: -106.69 | Avg Score: -98.87 |\n",
"| Experiment: 0 | Episode: 1281 | Score: -142.93 | Avg Score: -99.85 |\n",
"| Experiment: 0 | Episode: 1282 | Score: -96.66 | Avg Score: -100.38 |\n",
"| Experiment: 0 | Episode: 1283 | Score: -53.56 | Avg Score: -98.82 |\n",
"| Experiment: 0 | Episode: 1284 | Score: -71.63 | Avg Score: -97.65 |\n",
"| Experiment: 0 | Episode: 1285 | Score: -75.39 | Avg Score: -97.16 |\n",
"| Experiment: 0 | Episode: 1286 | Score: -87.28 | Avg Score: -97.34 |\n",
"| Experiment: 0 | Episode: 1287 | Score: -88.06 | Avg Score: -97.0 |\n",
"| Experiment: 0 | Episode: 1288 | Score: -41.17 | Avg Score: -97.2 |\n",
"| Experiment: 0 | Episode: 1289 | Score: -91.11 | Avg Score: -97.39 |\n",
"| Experiment: 0 | Episode: 1290 | Score: -57.6 | Avg Score: -96.14 |\n",
"| Experiment: 0 | Episode: 1291 | Score: -144.44 | Avg Score: -97.08 |\n",
"| Experiment: 0 | Episode: 1292 | Score: -110.99 | Avg Score: -97.79 |\n",
"| Experiment: 0 | Episode: 1293 | Score: -72.52 | Avg Score: -97.62 |\n",
"| Experiment: 0 | Episode: 1294 | Score: -101.61 | Avg Score: -97.89 |\n",
"| Experiment: 0 | Episode: 1295 | Score: -117.53 | Avg Score: -98.93 |\n",
"| Experiment: 0 | Episode: 1296 | Score: -60.24 | Avg Score: -99.19 |\n",
"| Experiment: 0 | Episode: 1297 | Score: -107.84 | Avg Score: -99.56 |\n",
"| Experiment: 0 | Episode: 1298 | Score: -33.22 | Avg Score: -99.61 |\n",
"| Experiment: 0 | Episode: 1299 | Score: -58.63 | Avg Score: -98.81 |\n",
"| Experiment: 0 | Episode: 1300 | Score: -90.18 | Avg Score: -98.06 |\n",
"| Experiment: 0 | Episode: 1301 | Score: -220.1 | Avg Score: -98.71 |\n",
"| Experiment: 0 | Episode: 1302 | Score: -210.0 | Avg Score: -100.19 |\n",
"| Experiment: 0 | Episode: 1303 | Score: -109.76 | Avg Score: -100.15 |\n",
"| Experiment: 0 | Episode: 1304 | Score: -98.28 | Avg Score: -101.14 |\n",
"| Experiment: 0 | Episode: 1305 | Score: -80.24 | Avg Score: -100.89 |\n",
"| Experiment: 0 | Episode: 1306 | Score: -84.22 | Avg Score: -101.12 |\n",
"| Experiment: 0 | Episode: 1307 | Score: -117.37 | Avg Score: -101.39 |\n",
"| Experiment: 0 | Episode: 1308 | Score: -83.92 | Avg Score: -100.77 |\n",
"| Experiment: 0 | Episode: 1309 | Score: -191.81 | Avg Score: -102.02 |\n",
"| Experiment: 0 | Episode: 1310 | Score: -6.34 | Avg Score: -101.74 |\n",
"| Experiment: 0 | Episode: 1311 | Score: -88.02 | Avg Score: -101.43 |\n",
"| Experiment: 0 | Episode: 1312 | Score: -121.26 | Avg Score: -102.07 |\n",
"| Experiment: 0 | Episode: 1313 | Score: -266.26 | Avg Score: -103.5 |\n",
"| Experiment: 0 | Episode: 1314 | Score: -41.29 | Avg Score: -102.38 |\n",
"| Experiment: 0 | Episode: 1315 | Score: -54.81 | Avg Score: -102.36 |\n",
"| Experiment: 0 | Episode: 1316 | Score: -46.36 | Avg Score: -102.03 |\n",
"| Experiment: 0 | Episode: 1317 | Score: -231.66 | Avg Score: -103.96 |\n",
"| Experiment: 0 | Episode: 1318 | Score: -100.31 | Avg Score: -102.77 |\n",
"| Experiment: 0 | Episode: 1319 | Score: -70.18 | Avg Score: -102.63 |\n",
"| Experiment: 0 | Episode: 1320 | Score: -34.35 | Avg Score: -100.97 |\n",
"| Experiment: 0 | Episode: 1321 | Score: -72.43 | Avg Score: -101.01 |\n",
"| Experiment: 0 | Episode: 1322 | Score: -357.91 | Avg Score: -103.62 |\n",
"| Experiment: 0 | Episode: 1323 | Score: -64.03 | Avg Score: -103.99 |\n",
"| Experiment: 0 | Episode: 1324 | Score: -65.1 | Avg Score: -102.76 |\n",
"| Experiment: 0 | Episode: 1325 | Score: -117.09 | Avg Score: -103.25 |\n",
"| Experiment: 0 | Episode: 1326 | Score: -101.22 | Avg Score: -102.53 |\n",
"| Experiment: 0 | Episode: 1327 | Score: -48.93 | Avg Score: -102.5 |\n",
"| Experiment: 0 | Episode: 1328 | Score: -63.43 | Avg Score: -102.48 |\n",
"| Experiment: 0 | Episode: 1329 | Score: -41.35 | Avg Score: -102.05 |\n",
"| Experiment: 0 | Episode: 1330 | Score: -70.81 | Avg Score: -102.07 |\n",
"| Experiment: 0 | Episode: 1331 | Score: -70.61 | Avg Score: -102.3 |\n",
"| Experiment: 0 | Episode: 1332 | Score: -48.37 | Avg Score: -101.06 |\n",
"| Experiment: 0 | Episode: 1333 | Score: -248.42 | Avg Score: -102.81 |\n",
"| Experiment: 0 | Episode: 1334 | Score: 9.61 | Avg Score: -101.7 |\n",
"| Experiment: 0 | Episode: 1335 | Score: -99.44 | Avg Score: -100.48 |\n",
"| Experiment: 0 | Episode: 1336 | Score: -160.37 | Avg Score: -101.04 |\n",
"| Experiment: 0 | Episode: 1337 | Score: 113.68 | Avg Score: -98.8 |\n",
"| Experiment: 0 | Episode: 1338 | Score: -54.02 | Avg Score: -98.55 |\n",
"| Experiment: 0 | Episode: 1339 | Score: -63.28 | Avg Score: -98.59 |\n",
"| Experiment: 0 | Episode: 1340 | Score: -55.94 | Avg Score: -96.2 |\n",
"| Experiment: 0 | Episode: 1341 | Score: -126.37 | Avg Score: -96.37 |\n",
"| Experiment: 0 | Episode: 1342 | Score: -18.9 | Avg Score: -96.0 |\n",
"| Experiment: 0 | Episode: 1343 | Score: -50.73 | Avg Score: -95.42 |\n",
"| Experiment: 0 | Episode: 1344 | Score: -90.97 | Avg Score: -94.8 |\n",
"| Experiment: 0 | Episode: 1345 | Score: 21.33 | Avg Score: -94.17 |\n",
"| Experiment: 0 | Episode: 1346 | Score: -236.0 | Avg Score: -95.39 |\n",
"| Experiment: 0 | Episode: 1347 | Score: -111.29 | Avg Score: -95.54 |\n",
"| Experiment: 0 | Episode: 1348 | Score: -190.5 | Avg Score: -95.4 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 1349 | Score: -109.1 | Avg Score: -95.7 |\n",
"| Experiment: 0 | Episode: 1350 | Score: -19.77 | Avg Score: -94.39 |\n",
"| Experiment: 0 | Episode: 1351 | Score: -25.41 | Avg Score: -94.18 |\n",
"| Experiment: 0 | Episode: 1352 | Score: -95.1 | Avg Score: -94.19 |\n",
"| Experiment: 0 | Episode: 1353 | Score: -132.01 | Avg Score: -94.61 |\n",
"| Experiment: 0 | Episode: 1354 | Score: -167.04 | Avg Score: -96.06 |\n",
"| Experiment: 0 | Episode: 1355 | Score: -64.91 | Avg Score: -95.94 |\n",
"| Experiment: 0 | Episode: 1356 | Score: -40.83 | Avg Score: -95.81 |\n",
"| Experiment: 0 | Episode: 1357 | Score: -70.32 | Avg Score: -95.91 |\n",
"| Experiment: 0 | Episode: 1358 | Score: -12.2 | Avg Score: -94.75 |\n",
"| Experiment: 0 | Episode: 1359 | Score: -36.68 | Avg Score: -93.85 |\n",
"| Experiment: 0 | Episode: 1360 | Score: -74.25 | Avg Score: -93.24 |\n",
"| Experiment: 0 | Episode: 1361 | Score: -2.3 | Avg Score: -92.89 |\n",
"| Experiment: 0 | Episode: 1362 | Score: -151.1 | Avg Score: -93.15 |\n",
"| Experiment: 0 | Episode: 1363 | Score: -80.41 | Avg Score: -92.85 |\n",
"| Experiment: 0 | Episode: 1364 | Score: -110.78 | Avg Score: -93.2 |\n",
"| Experiment: 0 | Episode: 1365 | Score: -32.67 | Avg Score: -91.91 |\n",
"| Experiment: 0 | Episode: 1366 | Score: -41.93 | Avg Score: -90.45 |\n",
"| Experiment: 0 | Episode: 1367 | Score: -33.16 | Avg Score: -90.12 |\n",
"| Experiment: 0 | Episode: 1368 | Score: -274.17 | Avg Score: -92.06 |\n",
"| Experiment: 0 | Episode: 1369 | Score: -46.88 | Avg Score: -92.14 |\n",
"| Experiment: 0 | Episode: 1370 | Score: -161.68 | Avg Score: -92.2 |\n",
"| Experiment: 0 | Episode: 1371 | Score: -146.21 | Avg Score: -92.89 |\n",
"| Experiment: 0 | Episode: 1372 | Score: -49.18 | Avg Score: -92.8 |\n",
"| Experiment: 0 | Episode: 1373 | Score: -53.19 | Avg Score: -92.81 |\n",
"| Experiment: 0 | Episode: 1374 | Score: -97.7 | Avg Score: -92.78 |\n",
"| Experiment: 0 | Episode: 1375 | Score: -118.17 | Avg Score: -92.46 |\n",
"| Experiment: 0 | Episode: 1376 | Score: -64.32 | Avg Score: -91.34 |\n",
"| Experiment: 0 | Episode: 1377 | Score: -66.4 | Avg Score: -91.23 |\n",
"| Experiment: 0 | Episode: 1378 | Score: -19.37 | Avg Score: -91.01 |\n",
"| Experiment: 0 | Episode: 1379 | Score: -42.61 | Avg Score: -89.89 |\n",
"| Experiment: 0 | Episode: 1380 | Score: -126.81 | Avg Score: -90.09 |\n",
"| Experiment: 0 | Episode: 1381 | Score: -110.88 | Avg Score: -89.77 |\n",
"| Experiment: 0 | Episode: 1382 | Score: -286.73 | Avg Score: -91.67 |\n",
"| Experiment: 0 | Episode: 1383 | Score: -142.03 | Avg Score: -92.55 |\n",
"| Experiment: 0 | Episode: 1384 | Score: -96.73 | Avg Score: -92.8 |\n",
"| Experiment: 0 | Episode: 1385 | Score: -129.39 | Avg Score: -93.34 |\n",
"| Experiment: 0 | Episode: 1386 | Score: -21.22 | Avg Score: -92.68 |\n",
"| Experiment: 0 | Episode: 1387 | Score: -162.65 | Avg Score: -93.43 |\n",
"| Experiment: 0 | Episode: 1388 | Score: -155.71 | Avg Score: -94.57 |\n",
"| Experiment: 0 | Episode: 1389 | Score: -235.76 | Avg Score: -96.02 |\n",
"| Experiment: 0 | Episode: 1390 | Score: -278.73 | Avg Score: -98.23 |\n",
"| Experiment: 0 | Episode: 1391 | Score: -105.35 | Avg Score: -97.84 |\n",
"| Experiment: 0 | Episode: 1392 | Score: -320.73 | Avg Score: -99.94 |\n",
"| Experiment: 0 | Episode: 1393 | Score: -35.27 | Avg Score: -99.57 |\n",
"| Experiment: 0 | Episode: 1394 | Score: -53.63 | Avg Score: -99.09 |\n",
"| Experiment: 0 | Episode: 1395 | Score: -208.85 | Avg Score: -100.0 |\n",
"| Experiment: 0 | Episode: 1396 | Score: -55.5 | Avg Score: -99.95 |\n",
"| Experiment: 0 | Episode: 1397 | Score: -27.21 | Avg Score: -99.15 |\n",
"| Experiment: 0 | Episode: 1398 | Score: -54.04 | Avg Score: -99.35 |\n",
"| Experiment: 0 | Episode: 1399 | Score: -71.9 | Avg Score: -99.49 |\n",
"| Experiment: 0 | Episode: 1400 | Score: -36.48 | Avg Score: -98.95 |\n",
"| Experiment: 0 | Episode: 1401 | Score: -63.02 | Avg Score: -97.38 |\n",
"| Experiment: 0 | Episode: 1402 | Score: -94.77 | Avg Score: -96.23 |\n",
"| Experiment: 0 | Episode: 1403 | Score: -86.98 | Avg Score: -96.0 |\n",
"| Experiment: 0 | Episode: 1404 | Score: -46.82 | Avg Score: -95.48 |\n",
"| Experiment: 0 | Episode: 1405 | Score: -42.6 | Avg Score: -95.11 |\n",
"| Experiment: 0 | Episode: 1406 | Score: -248.56 | Avg Score: -96.75 |\n",
"| Experiment: 0 | Episode: 1407 | Score: -94.59 | Avg Score: -96.52 |\n",
"| Experiment: 0 | Episode: 1408 | Score: -77.04 | Avg Score: -96.45 |\n",
"| Experiment: 0 | Episode: 1409 | Score: -77.18 | Avg Score: -95.31 |\n",
"| Experiment: 0 | Episode: 1410 | Score: -83.27 | Avg Score: -96.08 |\n",
"| Experiment: 0 | Episode: 1411 | Score: -19.81 | Avg Score: -95.39 |\n",
"| Experiment: 0 | Episode: 1412 | Score: -67.94 | Avg Score: -94.86 |\n",
"| Experiment: 0 | Episode: 1413 | Score: -89.03 | Avg Score: -93.09 |\n",
"| Experiment: 0 | Episode: 1414 | Score: -38.03 | Avg Score: -93.06 |\n",
"| Experiment: 0 | Episode: 1415 | Score: -60.34 | Avg Score: -93.11 |\n",
"| Experiment: 0 | Episode: 1416 | Score: -81.35 | Avg Score: -93.46 |\n",
"| Experiment: 0 | Episode: 1417 | Score: -35.17 | Avg Score: -91.5 |\n",
"| Experiment: 0 | Episode: 1418 | Score: -97.05 | Avg Score: -91.46 |\n",
"| Experiment: 0 | Episode: 1419 | Score: -62.33 | Avg Score: -91.39 |\n",
"| Experiment: 0 | Episode: 1420 | Score: -18.56 | Avg Score: -91.23 |\n",
"| Experiment: 0 | Episode: 1421 | Score: -76.32 | Avg Score: -91.27 |\n",
"| Experiment: 0 | Episode: 1422 | Score: -211.21 | Avg Score: -89.8 |\n",
"| Experiment: 0 | Episode: 1423 | Score: -228.05 | Avg Score: -91.44 |\n",
"| Experiment: 0 | Episode: 1424 | Score: -28.9 | Avg Score: -91.08 |\n",
"| Experiment: 0 | Episode: 1425 | Score: -54.92 | Avg Score: -90.46 |\n",
"| Experiment: 0 | Episode: 1426 | Score: -100.83 | Avg Score: -90.45 |\n",
"| Experiment: 0 | Episode: 1427 | Score: -16.25 | Avg Score: -90.13 |\n",
"| Experiment: 0 | Episode: 1428 | Score: -36.82 | Avg Score: -89.86 |\n",
"| Experiment: 0 | Episode: 1429 | Score: -85.78 | Avg Score: -90.3 |\n",
"| Experiment: 0 | Episode: 1430 | Score: -76.75 | Avg Score: -90.36 |\n",
"| Experiment: 0 | Episode: 1431 | Score: -66.24 | Avg Score: -90.32 |\n",
"| Experiment: 0 | Episode: 1432 | Score: -141.74 | Avg Score: -91.25 |\n",
"| Experiment: 0 | Episode: 1433 | Score: -61.63 | Avg Score: -89.39 |\n",
"| Experiment: 0 | Episode: 1434 | Score: -146.72 | Avg Score: -90.95 |\n",
"| Experiment: 0 | Episode: 1435 | Score: -38.31 | Avg Score: -90.34 |\n",
"| Experiment: 0 | Episode: 1436 | Score: -86.53 | Avg Score: -89.6 |\n",
"| Experiment: 0 | Episode: 1437 | Score: -60.15 | Avg Score: -91.34 |\n",
"| Experiment: 0 | Episode: 1438 | Score: -83.11 | Avg Score: -91.63 |\n",
"| Experiment: 0 | Episode: 1439 | Score: -29.68 | Avg Score: -91.29 |\n",
"| Experiment: 0 | Episode: 1440 | Score: -99.17 | Avg Score: -91.72 |\n",
"| Experiment: 0 | Episode: 1441 | Score: -51.85 | Avg Score: -90.98 |\n",
"| Experiment: 0 | Episode: 1442 | Score: -47.41 | Avg Score: -91.26 |\n",
"| Experiment: 0 | Episode: 1443 | Score: -102.17 | Avg Score: -91.78 |\n",
"| Experiment: 0 | Episode: 1444 | Score: -25.24 | Avg Score: -91.12 |\n",
"| Experiment: 0 | Episode: 1445 | Score: -64.09 | Avg Score: -91.98 |\n",
"| Experiment: 0 | Episode: 1446 | Score: -61.23 | Avg Score: -90.23 |\n",
"| Experiment: 0 | Episode: 1447 | Score: -96.45 | Avg Score: -90.08 |\n",
"| Experiment: 0 | Episode: 1448 | Score: -33.62 | Avg Score: -88.51 |\n",
"| Experiment: 0 | Episode: 1449 | Score: -63.46 | Avg Score: -88.05 |\n",
"| Experiment: 0 | Episode: 1450 | Score: -93.54 | Avg Score: -88.79 |\n",
"| Experiment: 0 | Episode: 1451 | Score: -36.74 | Avg Score: -88.91 |\n",
"| Experiment: 0 | Episode: 1452 | Score: -102.96 | Avg Score: -88.98 |\n",
"| Experiment: 0 | Episode: 1453 | Score: -87.44 | Avg Score: -88.54 |\n",
"| Experiment: 0 | Episode: 1454 | Score: -76.21 | Avg Score: -87.63 |\n",
"| Experiment: 0 | Episode: 1455 | Score: -41.56 | Avg Score: -87.4 |\n",
"| Experiment: 0 | Episode: 1456 | Score: -2.84 | Avg Score: -87.02 |\n",
"| Experiment: 0 | Episode: 1457 | Score: -198.34 | Avg Score: -88.3 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 1458 | Score: -117.22 | Avg Score: -89.35 |\n",
"| Experiment: 0 | Episode: 1459 | Score: -63.83 | Avg Score: -89.62 |\n",
"| Experiment: 0 | Episode: 1460 | Score: -74.42 | Avg Score: -89.62 |\n",
"| Experiment: 0 | Episode: 1461 | Score: -31.26 | Avg Score: -89.91 |\n",
"| Experiment: 0 | Episode: 1462 | Score: -0.7 | Avg Score: -88.41 |\n",
"| Experiment: 0 | Episode: 1463 | Score: -61.09 | Avg Score: -88.21 |\n",
"| Experiment: 0 | Episode: 1464 | Score: -65.64 | Avg Score: -87.76 |\n",
"| Experiment: 0 | Episode: 1465 | Score: -34.24 | Avg Score: -87.78 |\n",
"| Experiment: 0 | Episode: 1466 | Score: 1.38 | Avg Score: -87.34 |\n",
"| Experiment: 0 | Episode: 1467 | Score: -63.24 | Avg Score: -87.64 |\n",
"| Experiment: 0 | Episode: 1468 | Score: -20.99 | Avg Score: -85.11 |\n",
"| Experiment: 0 | Episode: 1469 | Score: -73.49 | Avg Score: -85.38 |\n",
"| Experiment: 0 | Episode: 1470 | Score: -87.41 | Avg Score: -84.64 |\n",
"| Experiment: 0 | Episode: 1471 | Score: -96.69 | Avg Score: -84.14 |\n",
"| Experiment: 0 | Episode: 1472 | Score: -52.99 | Avg Score: -84.18 |\n",
"| Experiment: 0 | Episode: 1473 | Score: -56.07 | Avg Score: -84.21 |\n",
"| Experiment: 0 | Episode: 1474 | Score: -2.12 | Avg Score: -83.25 |\n",
"| Experiment: 0 | Episode: 1475 | Score: -53.31 | Avg Score: -82.6 |\n",
"| Experiment: 0 | Episode: 1476 | Score: -3.36 | Avg Score: -81.99 |\n",
"| Experiment: 0 | Episode: 1477 | Score: -96.47 | Avg Score: -82.29 |\n",
"| Experiment: 0 | Episode: 1478 | Score: -71.15 | Avg Score: -82.81 |\n",
"| Experiment: 0 | Episode: 1479 | Score: -89.48 | Avg Score: -83.28 |\n",
"| Experiment: 0 | Episode: 1480 | Score: -2.48 | Avg Score: -82.04 |\n",
"| Experiment: 0 | Episode: 1481 | Score: -75.47 | Avg Score: -81.68 |\n",
"| Experiment: 0 | Episode: 1482 | Score: -26.16 | Avg Score: -79.08 |\n",
"| Experiment: 0 | Episode: 1483 | Score: -104.72 | Avg Score: -78.7 |\n",
"| Experiment: 0 | Episode: 1484 | Score: -94.5 | Avg Score: -78.68 |\n",
"| Experiment: 0 | Episode: 1485 | Score: -41.62 | Avg Score: -77.8 |\n",
"| Experiment: 0 | Episode: 1486 | Score: -86.05 | Avg Score: -78.45 |\n",
"| Experiment: 0 | Episode: 1487 | Score: -40.01 | Avg Score: -77.23 |\n",
"| Experiment: 0 | Episode: 1488 | Score: -60.48 | Avg Score: -76.27 |\n",
"| Experiment: 0 | Episode: 1489 | Score: -61.89 | Avg Score: -74.54 |\n",
"| Experiment: 0 | Episode: 1490 | Score: -53.14 | Avg Score: -72.28 |\n",
"| Experiment: 0 | Episode: 1491 | Score: -63.06 | Avg Score: -71.86 |\n",
"| Experiment: 0 | Episode: 1492 | Score: -76.66 | Avg Score: -69.42 |\n",
"| Experiment: 0 | Episode: 1493 | Score: -99.84 | Avg Score: -70.06 |\n",
"| Experiment: 0 | Episode: 1494 | Score: 111.81 | Avg Score: -68.41 |\n",
"| Experiment: 0 | Episode: 1495 | Score: -113.09 | Avg Score: -67.45 |\n",
"| Experiment: 0 | Episode: 1496 | Score: -145.43 | Avg Score: -68.35 |\n",
"| Experiment: 0 | Episode: 1497 | Score: -70.54 | Avg Score: -68.78 |\n",
"| Experiment: 0 | Episode: 1498 | Score: -106.2 | Avg Score: -69.3 |\n",
"| Experiment: 0 | Episode: 1499 | Score: -218.8 | Avg Score: -70.77 |\n",
"| Experiment: 0 | Episode: 1500 | Score: -50.83 | Avg Score: -70.92 |\n",
"| Experiment: 0 | Episode: 1501 | Score: 138.89 | Avg Score: -68.9 |\n",
"| Experiment: 0 | Episode: 1502 | Score: -49.15 | Avg Score: -68.44 |\n",
"| Experiment: 0 | Episode: 1503 | Score: 166.93 | Avg Score: -65.9 |\n",
"| Experiment: 0 | Episode: 1504 | Score: -56.77 | Avg Score: -66.0 |\n",
"| Experiment: 0 | Episode: 1505 | Score: -78.92 | Avg Score: -66.36 |\n",
"| Experiment: 0 | Episode: 1506 | Score: -67.18 | Avg Score: -64.55 |\n",
"| Experiment: 0 | Episode: 1507 | Score: -29.77 | Avg Score: -63.9 |\n",
"| Experiment: 0 | Episode: 1508 | Score: -226.4 | Avg Score: -65.4 |\n",
"| Experiment: 0 | Episode: 1509 | Score: -76.28 | Avg Score: -65.39 |\n",
"| Experiment: 0 | Episode: 1510 | Score: -47.71 | Avg Score: -65.03 |\n",
"| Experiment: 0 | Episode: 1511 | Score: -84.77 | Avg Score: -65.68 |\n",
"| Experiment: 0 | Episode: 1512 | Score: -36.29 | Avg Score: -65.37 |\n",
"| Experiment: 0 | Episode: 1513 | Score: -19.61 | Avg Score: -64.67 |\n",
"| Experiment: 0 | Episode: 1514 | Score: -128.01 | Avg Score: -65.57 |\n",
"| Experiment: 0 | Episode: 1515 | Score: -24.41 | Avg Score: -65.21 |\n",
"| Experiment: 0 | Episode: 1516 | Score: -137.07 | Avg Score: -65.77 |\n",
"| Experiment: 0 | Episode: 1517 | Score: -12.6 | Avg Score: -65.54 |\n",
"| Experiment: 0 | Episode: 1518 | Score: -94.03 | Avg Score: -65.51 |\n",
"| Experiment: 0 | Episode: 1519 | Score: -59.04 | Avg Score: -65.48 |\n",
"| Experiment: 0 | Episode: 1520 | Score: -62.38 | Avg Score: -65.92 |\n",
"| Experiment: 0 | Episode: 1521 | Score: -75.67 | Avg Score: -65.91 |\n",
"| Experiment: 0 | Episode: 1522 | Score: -50.78 | Avg Score: -64.31 |\n",
"| Experiment: 0 | Episode: 1523 | Score: -8.47 | Avg Score: -62.11 |\n",
"| Experiment: 0 | Episode: 1524 | Score: -38.41 | Avg Score: -62.21 |\n",
"| Experiment: 0 | Episode: 1525 | Score: -58.79 | Avg Score: -62.25 |\n",
"| Experiment: 0 | Episode: 1526 | Score: -213.51 | Avg Score: -63.37 |\n",
"| Experiment: 0 | Episode: 1527 | Score: -81.71 | Avg Score: -64.03 |\n",
"| Experiment: 0 | Episode: 1528 | Score: -21.34 | Avg Score: -63.87 |\n",
"| Experiment: 0 | Episode: 1529 | Score: -123.52 | Avg Score: -64.25 |\n",
"| Experiment: 0 | Episode: 1530 | Score: -62.16 | Avg Score: -64.1 |\n",
"| Experiment: 0 | Episode: 1531 | Score: -22.64 | Avg Score: -63.67 |\n",
"| Experiment: 0 | Episode: 1532 | Score: -79.27 | Avg Score: -63.04 |\n",
"| Experiment: 0 | Episode: 1533 | Score: -34.98 | Avg Score: -62.78 |\n",
"| Experiment: 0 | Episode: 1534 | Score: -183.22 | Avg Score: -63.14 |\n",
"| Experiment: 0 | Episode: 1535 | Score: -68.17 | Avg Score: -63.44 |\n",
"| Experiment: 0 | Episode: 1536 | Score: -124.16 | Avg Score: -63.82 |\n",
"| Experiment: 0 | Episode: 1537 | Score: -51.93 | Avg Score: -63.73 |\n",
"| Experiment: 0 | Episode: 1538 | Score: -21.89 | Avg Score: -63.12 |\n",
"| Experiment: 0 | Episode: 1539 | Score: -151.39 | Avg Score: -64.34 |\n",
"| Experiment: 0 | Episode: 1540 | Score: -23.15 | Avg Score: -63.58 |\n",
"| Experiment: 0 | Episode: 1541 | Score: -48.78 | Avg Score: -63.55 |\n",
"| Experiment: 0 | Episode: 1542 | Score: -60.42 | Avg Score: -63.68 |\n",
"| Experiment: 0 | Episode: 1543 | Score: -72.86 | Avg Score: -63.38 |\n",
"| Experiment: 0 | Episode: 1544 | Score: -29.56 | Avg Score: -63.43 |\n",
"| Experiment: 0 | Episode: 1545 | Score: -242.42 | Avg Score: -65.21 |\n",
"| Experiment: 0 | Episode: 1546 | Score: -153.74 | Avg Score: -66.14 |\n",
"| Experiment: 0 | Episode: 1547 | Score: -46.75 | Avg Score: -65.64 |\n",
"| Experiment: 0 | Episode: 1548 | Score: -98.88 | Avg Score: -66.29 |\n",
"| Experiment: 0 | Episode: 1549 | Score: -21.03 | Avg Score: -65.87 |\n",
"| Experiment: 0 | Episode: 1550 | Score: -15.79 | Avg Score: -65.09 |\n",
"| Experiment: 0 | Episode: 1551 | Score: -74.81 | Avg Score: -65.47 |\n",
"| Experiment: 0 | Episode: 1552 | Score: -95.03 | Avg Score: -65.39 |\n",
"| Experiment: 0 | Episode: 1553 | Score: -17.34 | Avg Score: -64.69 |\n",
"| Experiment: 0 | Episode: 1554 | Score: -44.7 | Avg Score: -64.38 |\n",
"| Experiment: 0 | Episode: 1555 | Score: -37.73 | Avg Score: -64.34 |\n",
"| Experiment: 0 | Episode: 1556 | Score: -75.29 | Avg Score: -65.06 |\n",
"| Experiment: 0 | Episode: 1557 | Score: -4.77 | Avg Score: -63.13 |\n",
"| Experiment: 0 | Episode: 1558 | Score: -84.15 | Avg Score: -62.8 |\n",
"| Experiment: 0 | Episode: 1559 | Score: -25.51 | Avg Score: -62.41 |\n",
"| Experiment: 0 | Episode: 1560 | Score: -61.23 | Avg Score: -62.28 |\n",
"| Experiment: 0 | Episode: 1561 | Score: -39.54 | Avg Score: -62.36 |\n",
"| Experiment: 0 | Episode: 1562 | Score: -49.65 | Avg Score: -62.85 |\n",
"| Experiment: 0 | Episode: 1563 | Score: -106.19 | Avg Score: -63.3 |\n",
"| Experiment: 0 | Episode: 1564 | Score: -78.14 | Avg Score: -63.43 |\n",
"| Experiment: 0 | Episode: 1565 | Score: -148.02 | Avg Score: -64.57 |\n",
"| Experiment: 0 | Episode: 1566 | Score: -55.81 | Avg Score: -65.14 |\n",
"| Experiment: 0 | Episode: 1567 | Score: 13.66 | Avg Score: -64.37 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 1568 | Score: -132.09 | Avg Score: -65.48 |\n",
"| Experiment: 0 | Episode: 1569 | Score: -52.36 | Avg Score: -65.27 |\n",
"| Experiment: 0 | Episode: 1570 | Score: -184.26 | Avg Score: -66.24 |\n",
"| Experiment: 0 | Episode: 1571 | Score: -163.85 | Avg Score: -66.91 |\n",
"| Experiment: 0 | Episode: 1572 | Score: -57.27 | Avg Score: -66.95 |\n",
"| Experiment: 0 | Episode: 1573 | Score: -56.53 | Avg Score: -66.96 |\n",
"| Experiment: 0 | Episode: 1574 | Score: -35.89 | Avg Score: -67.29 |\n",
"| Experiment: 0 | Episode: 1575 | Score: -37.14 | Avg Score: -67.13 |\n",
"| Experiment: 0 | Episode: 1576 | Score: -150.72 | Avg Score: -68.61 |\n",
"| Experiment: 0 | Episode: 1577 | Score: -75.48 | Avg Score: -68.4 |\n",
"| Experiment: 0 | Episode: 1578 | Score: -32.37 | Avg Score: -68.01 |\n",
"| Experiment: 0 | Episode: 1579 | Score: -151.59 | Avg Score: -68.63 |\n",
"| Experiment: 0 | Episode: 1580 | Score: -54.64 | Avg Score: -69.15 |\n",
"| Experiment: 0 | Episode: 1581 | Score: -36.09 | Avg Score: -68.76 |\n",
"| Experiment: 0 | Episode: 1582 | Score: -285.7 | Avg Score: -71.35 |\n",
"| Experiment: 0 | Episode: 1583 | Score: -11.32 | Avg Score: -70.42 |\n",
"| Experiment: 0 | Episode: 1584 | Score: -29.53 | Avg Score: -69.77 |\n",
"| Experiment: 0 | Episode: 1585 | Score: -447.89 | Avg Score: -73.83 |\n",
"| Experiment: 0 | Episode: 1586 | Score: -94.62 | Avg Score: -73.92 |\n",
"| Experiment: 0 | Episode: 1587 | Score: -93.05 | Avg Score: -74.45 |\n",
"| Experiment: 0 | Episode: 1588 | Score: -16.15 | Avg Score: -74.0 |\n",
"| Experiment: 0 | Episode: 1589 | Score: -56.64 | Avg Score: -73.95 |\n",
"| Experiment: 0 | Episode: 1590 | Score: -148.47 | Avg Score: -74.91 |\n",
"| Experiment: 0 | Episode: 1591 | Score: -34.38 | Avg Score: -74.62 |\n",
"| Experiment: 0 | Episode: 1592 | Score: -28.82 | Avg Score: -74.14 |\n",
"| Experiment: 0 | Episode: 1593 | Score: -107.24 | Avg Score: -74.21 |\n",
"| Experiment: 0 | Episode: 1594 | Score: -76.71 | Avg Score: -76.1 |\n",
"| Experiment: 0 | Episode: 1595 | Score: -96.09 | Avg Score: -75.93 |\n",
"| Experiment: 0 | Episode: 1596 | Score: 106.47 | Avg Score: -73.41 |\n",
"| Experiment: 0 | Episode: 1597 | Score: -68.05 | Avg Score: -73.39 |\n",
"| Experiment: 0 | Episode: 1598 | Score: -44.2 | Avg Score: -72.77 |\n",
"| Experiment: 0 | Episode: 1599 | Score: -114.98 | Avg Score: -71.73 |\n",
"| Experiment: 0 | Episode: 1600 | Score: -110.42 | Avg Score: -72.32 |\n",
"| Experiment: 0 | Episode: 1601 | Score: -60.48 | Avg Score: -74.32 |\n",
"| Experiment: 0 | Episode: 1602 | Score: -44.65 | Avg Score: -74.27 |\n",
"| Experiment: 0 | Episode: 1603 | Score: -63.11 | Avg Score: -76.57 |\n",
"| Experiment: 0 | Episode: 1604 | Score: -99.57 | Avg Score: -77.0 |\n",
"| Experiment: 0 | Episode: 1605 | Score: -50.09 | Avg Score: -76.71 |\n",
"| Experiment: 0 | Episode: 1606 | Score: -70.3 | Avg Score: -76.74 |\n",
"| Experiment: 0 | Episode: 1607 | Score: -173.71 | Avg Score: -78.18 |\n",
"| Experiment: 0 | Episode: 1608 | Score: -81.06 | Avg Score: -76.73 |\n",
"| Experiment: 0 | Episode: 1609 | Score: -96.92 | Avg Score: -76.94 |\n",
"| Experiment: 0 | Episode: 1610 | Score: -33.36 | Avg Score: -76.79 |\n",
"| Experiment: 0 | Episode: 1611 | Score: -67.03 | Avg Score: -76.61 |\n",
"| Experiment: 0 | Episode: 1612 | Score: -418.77 | Avg Score: -80.44 |\n",
"| Experiment: 0 | Episode: 1613 | Score: -51.43 | Avg Score: -80.76 |\n",
"| Experiment: 0 | Episode: 1614 | Score: -37.77 | Avg Score: -79.85 |\n",
"| Experiment: 0 | Episode: 1615 | Score: -68.83 | Avg Score: -80.3 |\n",
"| Experiment: 0 | Episode: 1616 | Score: -151.91 | Avg Score: -80.45 |\n",
"| Experiment: 0 | Episode: 1617 | Score: -109.78 | Avg Score: -81.42 |\n",
"| Experiment: 0 | Episode: 1618 | Score: -84.52 | Avg Score: -81.32 |\n",
"| Experiment: 0 | Episode: 1619 | Score: -18.78 | Avg Score: -80.92 |\n",
"| Experiment: 0 | Episode: 1620 | Score: -21.85 | Avg Score: -80.52 |\n",
"| Experiment: 0 | Episode: 1621 | Score: -124.43 | Avg Score: -81.0 |\n",
"| Experiment: 0 | Episode: 1622 | Score: -95.13 | Avg Score: -81.45 |\n",
"| Experiment: 0 | Episode: 1623 | Score: -93.01 | Avg Score: -82.29 |\n",
"| Experiment: 0 | Episode: 1624 | Score: -78.17 | Avg Score: -82.69 |\n",
"| Experiment: 0 | Episode: 1625 | Score: -110.56 | Avg Score: -83.21 |\n",
"| Experiment: 0 | Episode: 1626 | Score: -39.51 | Avg Score: -81.47 |\n",
"| Experiment: 0 | Episode: 1627 | Score: 34.47 | Avg Score: -80.31 |\n",
"| Experiment: 0 | Episode: 1628 | Score: -49.22 | Avg Score: -80.59 |\n",
"| Experiment: 0 | Episode: 1629 | Score: -9.93 | Avg Score: -79.45 |\n",
"| Experiment: 0 | Episode: 1630 | Score: -75.71 | Avg Score: -79.58 |\n",
"| Experiment: 0 | Episode: 1631 | Score: -23.41 | Avg Score: -79.59 |\n",
"| Experiment: 0 | Episode: 1632 | Score: -46.67 | Avg Score: -79.27 |\n",
"| Experiment: 0 | Episode: 1633 | Score: -223.44 | Avg Score: -81.15 |\n",
"| Experiment: 0 | Episode: 1634 | Score: -35.2 | Avg Score: -79.67 |\n",
"| Experiment: 0 | Episode: 1635 | Score: -53.03 | Avg Score: -79.52 |\n",
"| Experiment: 0 | Episode: 1636 | Score: -131.86 | Avg Score: -79.6 |\n",
"| Experiment: 0 | Episode: 1637 | Score: -47.55 | Avg Score: -79.55 |\n",
"| Experiment: 0 | Episode: 1638 | Score: -73.53 | Avg Score: -80.07 |\n",
"| Experiment: 0 | Episode: 1639 | Score: -135.32 | Avg Score: -79.91 |\n",
"| Experiment: 0 | Episode: 1640 | Score: -142.63 | Avg Score: -81.1 |\n",
"| Experiment: 0 | Episode: 1641 | Score: -162.76 | Avg Score: -82.24 |\n",
"| Experiment: 0 | Episode: 1642 | Score: -112.3 | Avg Score: -82.76 |\n",
"| Experiment: 0 | Episode: 1643 | Score: -246.61 | Avg Score: -84.5 |\n",
"| Experiment: 0 | Episode: 1644 | Score: -7.14 | Avg Score: -84.28 |\n",
"| Experiment: 0 | Episode: 1645 | Score: 9.33 | Avg Score: -81.76 |\n",
"| Experiment: 0 | Episode: 1646 | Score: -59.74 | Avg Score: -80.82 |\n",
"| Experiment: 0 | Episode: 1647 | Score: 20.42 | Avg Score: -80.15 |\n",
"| Experiment: 0 | Episode: 1648 | Score: -53.89 | Avg Score: -79.7 |\n",
"| Experiment: 0 | Episode: 1649 | Score: -194.89 | Avg Score: -81.43 |\n",
"| Experiment: 0 | Episode: 1650 | Score: -98.09 | Avg Score: -82.26 |\n",
"| Experiment: 0 | Episode: 1651 | Score: -29.58 | Avg Score: -81.81 |\n",
"| Experiment: 0 | Episode: 1652 | Score: -15.26 | Avg Score: -81.01 |\n",
"| Experiment: 0 | Episode: 1653 | Score: -124.18 | Avg Score: -82.08 |\n",
"| Experiment: 0 | Episode: 1654 | Score: 91.95 | Avg Score: -80.71 |\n",
"| Experiment: 0 | Episode: 1655 | Score: -37.64 | Avg Score: -80.71 |\n",
"| Experiment: 0 | Episode: 1656 | Score: -110.82 | Avg Score: -81.06 |\n",
"| Experiment: 0 | Episode: 1657 | Score: -79.8 | Avg Score: -81.81 |\n",
"| Experiment: 0 | Episode: 1658 | Score: -40.6 | Avg Score: -81.38 |\n",
"| Experiment: 0 | Episode: 1659 | Score: -195.52 | Avg Score: -83.08 |\n",
"| Experiment: 0 | Episode: 1660 | Score: -39.92 | Avg Score: -82.87 |\n",
"| Experiment: 0 | Episode: 1661 | Score: -240.12 | Avg Score: -84.87 |\n",
"| Experiment: 0 | Episode: 1662 | Score: -4.4 | Avg Score: -84.42 |\n",
"| Experiment: 0 | Episode: 1663 | Score: -9.61 | Avg Score: -83.45 |\n",
"| Experiment: 0 | Episode: 1664 | Score: -28.34 | Avg Score: -82.96 |\n",
"| Experiment: 0 | Episode: 1665 | Score: -46.99 | Avg Score: -81.94 |\n",
"| Experiment: 0 | Episode: 1666 | Score: -133.81 | Avg Score: -82.72 |\n",
"| Experiment: 0 | Episode: 1667 | Score: -109.91 | Avg Score: -83.96 |\n",
"| Experiment: 0 | Episode: 1668 | Score: -34.49 | Avg Score: -82.98 |\n",
"| Experiment: 0 | Episode: 1669 | Score: 12.45 | Avg Score: -82.34 |\n",
"| Experiment: 0 | Episode: 1670 | Score: -121.19 | Avg Score: -81.71 |\n",
"| Experiment: 0 | Episode: 1671 | Score: -18.6 | Avg Score: -80.25 |\n",
"| Experiment: 0 | Episode: 1672 | Score: -64.12 | Avg Score: -80.32 |\n",
"| Experiment: 0 | Episode: 1673 | Score: -106.92 | Avg Score: -80.83 |\n",
"| Experiment: 0 | Episode: 1674 | Score: -96.71 | Avg Score: -81.43 |\n",
"| Experiment: 0 | Episode: 1675 | Score: -62.59 | Avg Score: -81.69 |\n",
"| Experiment: 0 | Episode: 1676 | Score: -57.86 | Avg Score: -80.76 |\n",
"| Experiment: 0 | Episode: 1677 | Score: -69.9 | Avg Score: -80.7 |\n",
"| Experiment: 0 | Episode: 1678 | Score: -29.68 | Avg Score: -80.68 |\n",
"| Experiment: 0 | Episode: 1679 | Score: -44.7 | Avg Score: -79.61 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 1680 | Score: -89.25 | Avg Score: -79.95 |\n",
"| Experiment: 0 | Episode: 1681 | Score: -42.23 | Avg Score: -80.02 |\n",
"| Experiment: 0 | Episode: 1682 | Score: -89.02 | Avg Score: -78.05 |\n",
"| Experiment: 0 | Episode: 1683 | Score: -85.45 | Avg Score: -78.79 |\n",
"| Experiment: 0 | Episode: 1684 | Score: -64.28 | Avg Score: -79.14 |\n",
"| Experiment: 0 | Episode: 1685 | Score: -146.27 | Avg Score: -76.12 |\n",
"| Experiment: 0 | Episode: 1686 | Score: -68.73 | Avg Score: -75.86 |\n",
"| Experiment: 0 | Episode: 1687 | Score: -63.65 | Avg Score: -75.57 |\n",
"| Experiment: 0 | Episode: 1688 | Score: 18.57 | Avg Score: -75.22 |\n",
"| Experiment: 0 | Episode: 1689 | Score: 28.18 | Avg Score: -74.37 |\n",
"| Experiment: 0 | Episode: 1690 | Score: -32.61 | Avg Score: -73.21 |\n",
"| Experiment: 0 | Episode: 1691 | Score: -68.4 | Avg Score: -73.55 |\n",
"| Experiment: 0 | Episode: 1692 | Score: 13.91 | Avg Score: -73.13 |\n",
"| Experiment: 0 | Episode: 1693 | Score: -12.38 | Avg Score: -72.18 |\n",
"| Experiment: 0 | Episode: 1694 | Score: -68.16 | Avg Score: -72.09 |\n",
"| Experiment: 0 | Episode: 1695 | Score: -19.39 | Avg Score: -71.33 |\n",
"| Experiment: 0 | Episode: 1696 | Score: -19.14 | Avg Score: -72.58 |\n",
"| Experiment: 0 | Episode: 1697 | Score: -70.02 | Avg Score: -72.6 |\n",
"| Experiment: 0 | Episode: 1698 | Score: -136.77 | Avg Score: -73.53 |\n",
"| Experiment: 0 | Episode: 1699 | Score: 9.5 | Avg Score: -72.28 |\n",
"| Experiment: 0 | Episode: 1700 | Score: -67.5 | Avg Score: -71.85 |\n",
"| Experiment: 0 | Episode: 1701 | Score: -38.67 | Avg Score: -71.64 |\n",
"| Experiment: 0 | Episode: 1702 | Score: -97.65 | Avg Score: -72.17 |\n",
"| Experiment: 0 | Episode: 1703 | Score: -122.81 | Avg Score: -72.76 |\n",
"| Experiment: 0 | Episode: 1704 | Score: -136.08 | Avg Score: -73.13 |\n",
"| Experiment: 0 | Episode: 1705 | Score: -48.55 | Avg Score: -73.11 |\n",
"| Experiment: 0 | Episode: 1706 | Score: -27.24 | Avg Score: -72.68 |\n",
"| Experiment: 0 | Episode: 1707 | Score: -191.0 | Avg Score: -72.85 |\n",
"| Experiment: 0 | Episode: 1708 | Score: -64.64 | Avg Score: -72.69 |\n",
"| Experiment: 0 | Episode: 1709 | Score: -50.36 | Avg Score: -72.22 |\n",
"| Experiment: 0 | Episode: 1710 | Score: -23.62 | Avg Score: -72.13 |\n",
"| Experiment: 0 | Episode: 1711 | Score: -0.6 | Avg Score: -71.46 |\n",
"| Experiment: 0 | Episode: 1712 | Score: -96.8 | Avg Score: -68.24 |\n",
"| Experiment: 0 | Episode: 1713 | Score: -13.59 | Avg Score: -67.87 |\n",
"| Experiment: 0 | Episode: 1714 | Score: 12.77 | Avg Score: -67.36 |\n",
"| Experiment: 0 | Episode: 1715 | Score: -68.06 | Avg Score: -67.35 |\n",
"| Experiment: 0 | Episode: 1716 | Score: -127.47 | Avg Score: -67.11 |\n",
"| Experiment: 0 | Episode: 1717 | Score: -283.74 | Avg Score: -68.85 |\n",
"| Experiment: 0 | Episode: 1718 | Score: -54.51 | Avg Score: -68.55 |\n",
"| Experiment: 0 | Episode: 1719 | Score: -61.4 | Avg Score: -68.97 |\n",
"| Experiment: 0 | Episode: 1720 | Score: -66.61 | Avg Score: -69.42 |\n",
"| Experiment: 0 | Episode: 1721 | Score: -122.04 | Avg Score: -69.4 |\n",
"| Experiment: 0 | Episode: 1722 | Score: -30.52 | Avg Score: -68.75 |\n",
"| Experiment: 0 | Episode: 1723 | Score: -94.95 | Avg Score: -68.77 |\n",
"| Experiment: 0 | Episode: 1724 | Score: 43.15 | Avg Score: -67.56 |\n",
"| Experiment: 0 | Episode: 1725 | Score: 18.93 | Avg Score: -66.26 |\n",
"| Experiment: 0 | Episode: 1726 | Score: -16.02 | Avg Score: -66.03 |\n",
"| Experiment: 0 | Episode: 1727 | Score: -62.74 | Avg Score: -67.0 |\n",
"| Experiment: 0 | Episode: 1728 | Score: -97.9 | Avg Score: -67.49 |\n",
"| Experiment: 0 | Episode: 1729 | Score: -41.7 | Avg Score: -67.8 |\n",
"| Experiment: 0 | Episode: 1730 | Score: -186.7 | Avg Score: -68.91 |\n",
"| Experiment: 0 | Episode: 1731 | Score: -49.75 | Avg Score: -69.18 |\n",
"| Experiment: 0 | Episode: 1732 | Score: -31.77 | Avg Score: -69.03 |\n",
"| Experiment: 0 | Episode: 1733 | Score: -156.01 | Avg Score: -68.35 |\n",
"| Experiment: 0 | Episode: 1734 | Score: -43.29 | Avg Score: -68.43 |\n",
"| Experiment: 0 | Episode: 1735 | Score: -59.49 | Avg Score: -68.5 |\n",
"| Experiment: 0 | Episode: 1736 | Score: -4.89 | Avg Score: -67.23 |\n",
"| Experiment: 0 | Episode: 1737 | Score: -44.11 | Avg Score: -67.19 |\n",
"| Experiment: 0 | Episode: 1738 | Score: -38.39 | Avg Score: -66.84 |\n",
"| Experiment: 0 | Episode: 1739 | Score: 1.41 | Avg Score: -65.48 |\n",
"| Experiment: 0 | Episode: 1740 | Score: -126.91 | Avg Score: -65.32 |\n",
"| Experiment: 0 | Episode: 1741 | Score: -7.23 | Avg Score: -63.76 |\n",
"| Experiment: 0 | Episode: 1742 | Score: -54.7 | Avg Score: -63.19 |\n",
"| Experiment: 0 | Episode: 1743 | Score: -16.12 | Avg Score: -60.88 |\n",
"| Experiment: 0 | Episode: 1744 | Score: -268.58 | Avg Score: -63.5 |\n",
"| Experiment: 0 | Episode: 1745 | Score: -113.8 | Avg Score: -64.73 |\n",
"| Experiment: 0 | Episode: 1746 | Score: 22.3 | Avg Score: -63.91 |\n",
"| Experiment: 0 | Episode: 1747 | Score: -38.63 | Avg Score: -64.5 |\n",
"| Experiment: 0 | Episode: 1748 | Score: -72.08 | Avg Score: -64.68 |\n",
"| Experiment: 0 | Episode: 1749 | Score: -379.19 | Avg Score: -66.52 |\n",
"| Experiment: 0 | Episode: 1750 | Score: 37.74 | Avg Score: -65.17 |\n",
"| Experiment: 0 | Episode: 1751 | Score: -69.25 | Avg Score: -65.56 |\n",
"| Experiment: 0 | Episode: 1752 | Score: -45.54 | Avg Score: -65.86 |\n",
"| Experiment: 0 | Episode: 1753 | Score: -153.01 | Avg Score: -66.15 |\n",
"| Experiment: 0 | Episode: 1754 | Score: -62.08 | Avg Score: -67.69 |\n",
"| Experiment: 0 | Episode: 1755 | Score: 15.95 | Avg Score: -67.16 |\n",
"| Experiment: 0 | Episode: 1756 | Score: -139.17 | Avg Score: -67.44 |\n",
"| Experiment: 0 | Episode: 1757 | Score: -195.73 | Avg Score: -68.6 |\n",
"| Experiment: 0 | Episode: 1758 | Score: -101.0 | Avg Score: -69.2 |\n",
"| Experiment: 0 | Episode: 1759 | Score: -34.86 | Avg Score: -67.6 |\n",
"| Experiment: 0 | Episode: 1760 | Score: -107.32 | Avg Score: -68.27 |\n",
"| Experiment: 0 | Episode: 1761 | Score: -17.85 | Avg Score: -66.05 |\n",
"| Experiment: 0 | Episode: 1762 | Score: -71.76 | Avg Score: -66.72 |\n",
"| Experiment: 0 | Episode: 1763 | Score: -195.3 | Avg Score: -68.58 |\n",
"| Experiment: 0 | Episode: 1764 | Score: 15.52 | Avg Score: -68.14 |\n",
"| Experiment: 0 | Episode: 1765 | Score: -173.8 | Avg Score: -69.41 |\n",
"| Experiment: 0 | Episode: 1766 | Score: -14.32 | Avg Score: -68.21 |\n",
"| Experiment: 0 | Episode: 1767 | Score: 24.16 | Avg Score: -66.87 |\n",
"| Experiment: 0 | Episode: 1768 | Score: -30.48 | Avg Score: -66.83 |\n",
"| Experiment: 0 | Episode: 1769 | Score: 27.01 | Avg Score: -66.69 |\n",
"| Experiment: 0 | Episode: 1770 | Score: -17.52 | Avg Score: -65.65 |\n",
"| Experiment: 0 | Episode: 1771 | Score: -60.02 | Avg Score: -66.07 |\n",
"| Experiment: 0 | Episode: 1772 | Score: -0.05 | Avg Score: -65.42 |\n",
"| Experiment: 0 | Episode: 1773 | Score: -16.8 | Avg Score: -64.52 |\n",
"| Experiment: 0 | Episode: 1774 | Score: -61.3 | Avg Score: -64.17 |\n",
"| Experiment: 0 | Episode: 1775 | Score: -77.78 | Avg Score: -64.32 |\n",
"| Experiment: 0 | Episode: 1776 | Score: -52.3 | Avg Score: -64.27 |\n",
"| Experiment: 0 | Episode: 1777 | Score: -199.45 | Avg Score: -65.56 |\n",
"| Experiment: 0 | Episode: 1778 | Score: -156.59 | Avg Score: -66.83 |\n",
"| Experiment: 0 | Episode: 1779 | Score: -67.9 | Avg Score: -67.06 |\n",
"| Experiment: 0 | Episode: 1780 | Score: -180.72 | Avg Score: -67.98 |\n",
"| Experiment: 0 | Episode: 1781 | Score: -78.8 | Avg Score: -68.34 |\n",
"| Experiment: 0 | Episode: 1782 | Score: -75.07 | Avg Score: -68.2 |\n",
"| Experiment: 0 | Episode: 1783 | Score: -44.61 | Avg Score: -67.8 |\n",
"| Experiment: 0 | Episode: 1784 | Score: -43.53 | Avg Score: -67.59 |\n",
"| Experiment: 0 | Episode: 1785 | Score: -62.28 | Avg Score: -66.75 |\n",
"| Experiment: 0 | Episode: 1786 | Score: 14.11 | Avg Score: -65.92 |\n",
"| Experiment: 0 | Episode: 1787 | Score: -8.71 | Avg Score: -65.37 |\n",
"| Experiment: 0 | Episode: 1788 | Score: -64.85 | Avg Score: -66.2 |\n",
"| Experiment: 0 | Episode: 1789 | Score: -18.97 | Avg Score: -66.68 |\n",
"| Experiment: 0 | Episode: 1790 | Score: -29.09 | Avg Score: -66.64 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 1791 | Score: -59.22 | Avg Score: -66.55 |\n",
"| Experiment: 0 | Episode: 1792 | Score: -228.03 | Avg Score: -68.97 |\n",
"| Experiment: 0 | Episode: 1793 | Score: 13.05 | Avg Score: -68.71 |\n",
"| Experiment: 0 | Episode: 1794 | Score: 79.16 | Avg Score: -67.24 |\n",
"| Experiment: 0 | Episode: 1795 | Score: 33.0 | Avg Score: -66.72 |\n",
"| Experiment: 0 | Episode: 1796 | Score: -18.86 | Avg Score: -66.71 |\n",
"| Experiment: 0 | Episode: 1797 | Score: -32.95 | Avg Score: -66.34 |\n",
"| Experiment: 0 | Episode: 1798 | Score: -153.4 | Avg Score: -66.51 |\n",
"| Experiment: 0 | Episode: 1799 | Score: 4.76 | Avg Score: -66.56 |\n",
"| Experiment: 0 | Episode: 1800 | Score: -93.84 | Avg Score: -66.82 |\n",
"| Experiment: 0 | Episode: 1801 | Score: -62.35 | Avg Score: -67.06 |\n",
"| Experiment: 0 | Episode: 1802 | Score: -0.84 | Avg Score: -66.09 |\n",
"| Experiment: 0 | Episode: 1803 | Score: -10.51 | Avg Score: -64.97 |\n",
"| Experiment: 0 | Episode: 1804 | Score: -240.06 | Avg Score: -66.01 |\n",
"| Experiment: 0 | Episode: 1805 | Score: -142.58 | Avg Score: -66.95 |\n",
"| Experiment: 0 | Episode: 1806 | Score: -200.7 | Avg Score: -68.68 |\n",
"| Experiment: 0 | Episode: 1807 | Score: -19.32 | Avg Score: -66.96 |\n",
"| Experiment: 0 | Episode: 1808 | Score: -62.5 | Avg Score: -66.94 |\n",
"| Experiment: 0 | Episode: 1809 | Score: -247.19 | Avg Score: -68.91 |\n",
"| Experiment: 0 | Episode: 1810 | Score: -454.05 | Avg Score: -73.22 |\n",
"| Experiment: 0 | Episode: 1811 | Score: -52.77 | Avg Score: -73.74 |\n",
"| Experiment: 0 | Episode: 1812 | Score: -7.5 | Avg Score: -72.84 |\n",
"| Experiment: 0 | Episode: 1813 | Score: -35.43 | Avg Score: -73.06 |\n",
"| Experiment: 0 | Episode: 1814 | Score: -61.58 | Avg Score: -73.81 |\n",
"| Experiment: 0 | Episode: 1815 | Score: -18.71 | Avg Score: -73.31 |\n",
"| Experiment: 0 | Episode: 1816 | Score: -37.62 | Avg Score: -72.41 |\n",
"| Experiment: 0 | Episode: 1817 | Score: -225.74 | Avg Score: -71.83 |\n",
"| Experiment: 0 | Episode: 1818 | Score: -81.02 | Avg Score: -72.1 |\n",
"| Experiment: 0 | Episode: 1819 | Score: -79.84 | Avg Score: -72.28 |\n",
"| Experiment: 0 | Episode: 1820 | Score: -0.72 | Avg Score: -71.62 |\n",
"| Experiment: 0 | Episode: 1821 | Score: -22.36 | Avg Score: -70.63 |\n",
"| Experiment: 0 | Episode: 1822 | Score: -50.43 | Avg Score: -70.83 |\n",
"| Experiment: 0 | Episode: 1823 | Score: -54.06 | Avg Score: -70.42 |\n",
"| Experiment: 0 | Episode: 1824 | Score: -57.37 | Avg Score: -71.42 |\n",
"| Experiment: 0 | Episode: 1825 | Score: -61.1 | Avg Score: -72.22 |\n",
"| Experiment: 0 | Episode: 1826 | Score: -28.17 | Avg Score: -72.34 |\n",
"| Experiment: 0 | Episode: 1827 | Score: 4.26 | Avg Score: -71.67 |\n",
"| Experiment: 0 | Episode: 1828 | Score: -20.36 | Avg Score: -70.9 |\n",
"| Experiment: 0 | Episode: 1829 | Score: -48.46 | Avg Score: -70.97 |\n",
"| Experiment: 0 | Episode: 1830 | Score: -30.8 | Avg Score: -69.41 |\n",
"| Experiment: 0 | Episode: 1831 | Score: -27.08 | Avg Score: -69.18 |\n",
"| Experiment: 0 | Episode: 1832 | Score: -270.47 | Avg Score: -71.57 |\n",
"| Experiment: 0 | Episode: 1833 | Score: -29.7 | Avg Score: -70.31 |\n",
"| Experiment: 0 | Episode: 1834 | Score: -69.89 | Avg Score: -70.57 |\n",
"| Experiment: 0 | Episode: 1835 | Score: -76.98 | Avg Score: -70.75 |\n",
"| Experiment: 0 | Episode: 1836 | Score: -64.33 | Avg Score: -71.34 |\n",
"| Experiment: 0 | Episode: 1837 | Score: -38.69 | Avg Score: -71.29 |\n",
"| Experiment: 0 | Episode: 1838 | Score: -105.19 | Avg Score: -71.95 |\n",
"| Experiment: 0 | Episode: 1839 | Score: -23.41 | Avg Score: -72.2 |\n",
"| Experiment: 0 | Episode: 1840 | Score: -138.56 | Avg Score: -72.32 |\n",
"| Experiment: 0 | Episode: 1841 | Score: -91.18 | Avg Score: -73.16 |\n",
"| Experiment: 0 | Episode: 1842 | Score: -78.59 | Avg Score: -73.4 |\n",
"| Experiment: 0 | Episode: 1843 | Score: -19.31 | Avg Score: -73.43 |\n",
"| Experiment: 0 | Episode: 1844 | Score: -29.03 | Avg Score: -71.03 |\n",
"| Experiment: 0 | Episode: 1845 | Score: -68.14 | Avg Score: -70.58 |\n",
"| Experiment: 0 | Episode: 1846 | Score: -38.89 | Avg Score: -71.19 |\n",
"| Experiment: 0 | Episode: 1847 | Score: -24.71 | Avg Score: -71.05 |\n",
"| Experiment: 0 | Episode: 1848 | Score: -105.25 | Avg Score: -71.38 |\n",
"| Experiment: 0 | Episode: 1849 | Score: -130.22 | Avg Score: -68.89 |\n",
"| Experiment: 0 | Episode: 1850 | Score: -52.79 | Avg Score: -69.8 |\n",
"| Experiment: 0 | Episode: 1851 | Score: -66.54 | Avg Score: -69.77 |\n",
"| Experiment: 0 | Episode: 1852 | Score: -95.72 | Avg Score: -70.27 |\n",
"| Experiment: 0 | Episode: 1853 | Score: -40.4 | Avg Score: -69.15 |\n",
"| Experiment: 0 | Episode: 1854 | Score: -47.66 | Avg Score: -69.0 |\n",
"| Experiment: 0 | Episode: 1855 | Score: -50.73 | Avg Score: -69.67 |\n",
"| Experiment: 0 | Episode: 1856 | Score: -82.88 | Avg Score: -69.11 |\n",
"| Experiment: 0 | Episode: 1857 | Score: -34.1 | Avg Score: -67.49 |\n",
"| Experiment: 0 | Episode: 1858 | Score: -116.3 | Avg Score: -67.64 |\n",
"| Experiment: 0 | Episode: 1859 | Score: -50.89 | Avg Score: -67.8 |\n",
"| Experiment: 0 | Episode: 1860 | Score: -54.45 | Avg Score: -67.27 |\n",
"| Experiment: 0 | Episode: 1861 | Score: -96.02 | Avg Score: -68.06 |\n",
"| Experiment: 0 | Episode: 1862 | Score: -61.77 | Avg Score: -67.96 |\n",
"| Experiment: 0 | Episode: 1863 | Score: -117.73 | Avg Score: -67.18 |\n",
"| Experiment: 0 | Episode: 1864 | Score: -85.53 | Avg Score: -68.19 |\n",
"| Experiment: 0 | Episode: 1865 | Score: 3.52 | Avg Score: -66.42 |\n",
"| Experiment: 0 | Episode: 1866 | Score: -77.69 | Avg Score: -67.05 |\n",
"| Experiment: 0 | Episode: 1867 | Score: -127.4 | Avg Score: -68.57 |\n",
"| Experiment: 0 | Episode: 1868 | Score: 148.29 | Avg Score: -66.78 |\n",
"| Experiment: 0 | Episode: 1869 | Score: -71.44 | Avg Score: -67.76 |\n",
"| Experiment: 0 | Episode: 1870 | Score: -32.06 | Avg Score: -67.91 |\n",
"| Experiment: 0 | Episode: 1871 | Score: -80.46 | Avg Score: -68.11 |\n",
"| Experiment: 0 | Episode: 1872 | Score: -62.2 | Avg Score: -68.73 |\n",
"| Experiment: 0 | Episode: 1873 | Score: -52.79 | Avg Score: -69.09 |\n",
"| Experiment: 0 | Episode: 1874 | Score: -100.36 | Avg Score: -69.49 |\n",
"| Experiment: 0 | Episode: 1875 | Score: -65.28 | Avg Score: -69.36 |\n",
"| Experiment: 0 | Episode: 1876 | Score: -71.64 | Avg Score: -69.55 |\n",
"| Experiment: 0 | Episode: 1877 | Score: -82.79 | Avg Score: -68.39 |\n",
"| Experiment: 0 | Episode: 1878 | Score: -70.77 | Avg Score: -67.53 |\n",
"| Experiment: 0 | Episode: 1879 | Score: -83.54 | Avg Score: -67.69 |\n",
"| Experiment: 0 | Episode: 1880 | Score: -95.25 | Avg Score: -66.83 |\n",
"| Experiment: 0 | Episode: 1881 | Score: -152.82 | Avg Score: -67.57 |\n",
"| Experiment: 0 | Episode: 1882 | Score: -65.6 | Avg Score: -67.48 |\n",
"| Experiment: 0 | Episode: 1883 | Score: -251.46 | Avg Score: -69.54 |\n",
"| Experiment: 0 | Episode: 1884 | Score: -25.42 | Avg Score: -69.36 |\n",
"| Experiment: 0 | Episode: 1885 | Score: -33.36 | Avg Score: -69.07 |\n",
"| Experiment: 0 | Episode: 1886 | Score: 4.44 | Avg Score: -69.17 |\n",
"| Experiment: 0 | Episode: 1887 | Score: -68.35 | Avg Score: -69.77 |\n",
"| Experiment: 0 | Episode: 1888 | Score: -36.61 | Avg Score: -69.48 |\n",
"| Experiment: 0 | Episode: 1889 | Score: -86.37 | Avg Score: -70.16 |\n",
"| Experiment: 0 | Episode: 1890 | Score: -78.92 | Avg Score: -70.66 |\n",
"| Experiment: 0 | Episode: 1891 | Score: -37.88 | Avg Score: -70.44 |\n",
"| Experiment: 0 | Episode: 1892 | Score: -57.46 | Avg Score: -68.74 |\n",
"| Experiment: 0 | Episode: 1893 | Score: -107.58 | Avg Score: -69.94 |\n",
"| Experiment: 0 | Episode: 1894 | Score: -86.33 | Avg Score: -71.6 |\n",
"| Experiment: 0 | Episode: 1895 | Score: -65.94 | Avg Score: -72.59 |\n",
"| Experiment: 0 | Episode: 1896 | Score: -60.91 | Avg Score: -73.01 |\n",
"| Experiment: 0 | Episode: 1897 | Score: -27.61 | Avg Score: -72.96 |\n",
"| Experiment: 0 | Episode: 1898 | Score: -34.85 | Avg Score: -71.77 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 1899 | Score: -50.12 | Avg Score: -72.32 |\n",
"| Experiment: 0 | Episode: 1900 | Score: -89.41 | Avg Score: -72.27 |\n",
"| Experiment: 0 | Episode: 1901 | Score: -61.85 | Avg Score: -72.27 |\n",
"| Experiment: 0 | Episode: 1902 | Score: -56.75 | Avg Score: -72.83 |\n",
"| Experiment: 0 | Episode: 1903 | Score: -38.49 | Avg Score: -73.11 |\n",
"| Experiment: 0 | Episode: 1904 | Score: -7.0 | Avg Score: -70.78 |\n",
"| Experiment: 0 | Episode: 1905 | Score: -55.69 | Avg Score: -69.91 |\n",
"| Experiment: 0 | Episode: 1906 | Score: -51.54 | Avg Score: -68.42 |\n",
"| Experiment: 0 | Episode: 1907 | Score: -70.44 | Avg Score: -68.93 |\n",
"| Experiment: 0 | Episode: 1908 | Score: -69.7 | Avg Score: -69.0 |\n",
"| Experiment: 0 | Episode: 1909 | Score: -65.15 | Avg Score: -67.18 |\n",
"| Experiment: 0 | Episode: 1910 | Score: -47.4 | Avg Score: -63.11 |\n",
"| Experiment: 0 | Episode: 1911 | Score: -62.33 | Avg Score: -63.21 |\n",
"| Experiment: 0 | Episode: 1912 | Score: -32.31 | Avg Score: -63.46 |\n",
"| Experiment: 0 | Episode: 1913 | Score: -31.66 | Avg Score: -63.42 |\n",
"| Experiment: 0 | Episode: 1914 | Score: -39.69 | Avg Score: -63.2 |\n",
"| Experiment: 0 | Episode: 1915 | Score: -64.74 | Avg Score: -63.66 |\n",
"| Experiment: 0 | Episode: 1916 | Score: -39.81 | Avg Score: -63.68 |\n",
"| Experiment: 0 | Episode: 1917 | Score: -20.47 | Avg Score: -61.63 |\n",
"| Experiment: 0 | Episode: 1918 | Score: -20.37 | Avg Score: -61.02 |\n",
"| Experiment: 0 | Episode: 1919 | Score: -17.69 | Avg Score: -60.4 |\n",
"| Experiment: 0 | Episode: 1920 | Score: -74.56 | Avg Score: -61.14 |\n",
"| Experiment: 0 | Episode: 1921 | Score: -52.85 | Avg Score: -61.45 |\n",
"| Experiment: 0 | Episode: 1922 | Score: -37.79 | Avg Score: -61.32 |\n",
"| Experiment: 0 | Episode: 1923 | Score: -10.44 | Avg Score: -60.88 |\n",
"| Experiment: 0 | Episode: 1924 | Score: -25.84 | Avg Score: -60.57 |\n",
"| Experiment: 0 | Episode: 1925 | Score: -195.39 | Avg Score: -61.91 |\n",
"| Experiment: 0 | Episode: 1926 | Score: -9.87 | Avg Score: -61.73 |\n",
"| Experiment: 0 | Episode: 1927 | Score: -53.27 | Avg Score: -62.3 |\n",
"| Experiment: 0 | Episode: 1928 | Score: -48.51 | Avg Score: -62.58 |\n",
"| Experiment: 0 | Episode: 1929 | Score: -65.79 | Avg Score: -62.76 |\n",
"| Experiment: 0 | Episode: 1930 | Score: -77.58 | Avg Score: -63.23 |\n",
"| Experiment: 0 | Episode: 1931 | Score: -31.15 | Avg Score: -63.27 |\n",
"| Experiment: 0 | Episode: 1932 | Score: -33.42 | Avg Score: -60.9 |\n",
"| Experiment: 0 | Episode: 1933 | Score: -73.53 | Avg Score: -61.33 |\n",
"| Experiment: 0 | Episode: 1934 | Score: -14.29 | Avg Score: -60.78 |\n",
"| Experiment: 0 | Episode: 1935 | Score: -113.28 | Avg Score: -61.14 |\n",
"| Experiment: 0 | Episode: 1936 | Score: -90.78 | Avg Score: -61.4 |\n",
"| Experiment: 0 | Episode: 1937 | Score: 17.85 | Avg Score: -60.84 |\n",
"| Experiment: 0 | Episode: 1938 | Score: -39.21 | Avg Score: -60.18 |\n",
"| Experiment: 0 | Episode: 1939 | Score: 12.54 | Avg Score: -59.82 |\n",
"| Experiment: 0 | Episode: 1940 | Score: -41.39 | Avg Score: -58.85 |\n",
"| Experiment: 0 | Episode: 1941 | Score: -66.93 | Avg Score: -58.61 |\n",
"| Experiment: 0 | Episode: 1942 | Score: -45.56 | Avg Score: -58.28 |\n",
"| Experiment: 0 | Episode: 1943 | Score: -52.46 | Avg Score: -58.61 |\n",
"| Experiment: 0 | Episode: 1944 | Score: -41.79 | Avg Score: -58.73 |\n",
"| Experiment: 0 | Episode: 1945 | Score: -20.08 | Avg Score: -58.25 |\n",
"| Experiment: 0 | Episode: 1946 | Score: -3.97 | Avg Score: -57.91 |\n",
"| Experiment: 0 | Episode: 1947 | Score: -30.23 | Avg Score: -57.96 |\n",
"| Experiment: 0 | Episode: 1948 | Score: -42.63 | Avg Score: -57.33 |\n",
"| Experiment: 0 | Episode: 1949 | Score: -50.45 | Avg Score: -56.54 |\n",
"| Experiment: 0 | Episode: 1950 | Score: -212.46 | Avg Score: -58.13 |\n",
"| Experiment: 0 | Episode: 1951 | Score: -11.16 | Avg Score: -57.58 |\n",
"| Experiment: 0 | Episode: 1952 | Score: -37.91 | Avg Score: -57.0 |\n",
"| Experiment: 0 | Episode: 1953 | Score: -45.84 | Avg Score: -57.06 |\n",
"| Experiment: 0 | Episode: 1954 | Score: 28.84 | Avg Score: -56.29 |\n",
"| Experiment: 0 | Episode: 1955 | Score: -88.17 | Avg Score: -56.66 |\n",
"| Experiment: 0 | Episode: 1956 | Score: -59.91 | Avg Score: -56.44 |\n",
"| Experiment: 0 | Episode: 1957 | Score: -27.56 | Avg Score: -56.37 |\n",
"| Experiment: 0 | Episode: 1958 | Score: -5.94 | Avg Score: -55.27 |\n",
"| Experiment: 0 | Episode: 1959 | Score: -21.17 | Avg Score: -54.97 |\n",
"| Experiment: 0 | Episode: 1960 | Score: -38.3 | Avg Score: -54.81 |\n",
"| Experiment: 0 | Episode: 1961 | Score: -22.25 | Avg Score: -54.07 |\n",
"| Experiment: 0 | Episode: 1962 | Score: 22.26 | Avg Score: -53.23 |\n",
"| Experiment: 0 | Episode: 1963 | Score: 8.91 | Avg Score: -51.96 |\n",
"| Experiment: 0 | Episode: 1964 | Score: -55.43 | Avg Score: -51.66 |\n",
"| Experiment: 0 | Episode: 1965 | Score: -49.08 | Avg Score: -52.19 |\n",
"| Experiment: 0 | Episode: 1966 | Score: 15.44 | Avg Score: -51.26 |\n",
"| Experiment: 0 | Episode: 1967 | Score: -10.36 | Avg Score: -50.09 |\n",
"| Experiment: 0 | Episode: 1968 | Score: -46.63 | Avg Score: -52.04 |\n",
"| Experiment: 0 | Episode: 1969 | Score: -101.59 | Avg Score: -52.34 |\n",
"| Experiment: 0 | Episode: 1970 | Score: -21.8 | Avg Score: -52.23 |\n",
"| Experiment: 0 | Episode: 1971 | Score: -24.02 | Avg Score: -51.67 |\n",
"| Experiment: 0 | Episode: 1972 | Score: -68.45 | Avg Score: -51.73 |\n",
"| Experiment: 0 | Episode: 1973 | Score: -45.8 | Avg Score: -51.66 |\n",
"| Experiment: 0 | Episode: 1974 | Score: -50.12 | Avg Score: -51.16 |\n",
"| Experiment: 0 | Episode: 1975 | Score: -13.56 | Avg Score: -50.64 |\n",
"| Experiment: 0 | Episode: 1976 | Score: 2.83 | Avg Score: -49.9 |\n",
"| Experiment: 0 | Episode: 1977 | Score: 41.79 | Avg Score: -48.65 |\n",
"| Experiment: 0 | Episode: 1978 | Score: -59.2 | Avg Score: -48.54 |\n",
"| Experiment: 0 | Episode: 1979 | Score: -41.83 | Avg Score: -48.12 |\n",
"| Experiment: 0 | Episode: 1980 | Score: -28.27 | Avg Score: -47.45 |\n",
"| Experiment: 0 | Episode: 1981 | Score: -47.24 | Avg Score: -46.39 |\n",
"| Experiment: 0 | Episode: 1982 | Score: -5.14 | Avg Score: -45.79 |\n",
"| Experiment: 0 | Episode: 1983 | Score: -59.0 | Avg Score: -43.87 |\n",
"| Experiment: 0 | Episode: 1984 | Score: -61.14 | Avg Score: -44.22 |\n",
"| Experiment: 0 | Episode: 1985 | Score: -8.75 | Avg Score: -43.98 |\n",
"| Experiment: 0 | Episode: 1986 | Score: -7.45 | Avg Score: -44.1 |\n",
"| Experiment: 0 | Episode: 1987 | Score: -20.72 | Avg Score: -43.62 |\n",
"| Experiment: 0 | Episode: 1988 | Score: -3.34 | Avg Score: -43.29 |\n",
"| Experiment: 0 | Episode: 1989 | Score: -45.93 | Avg Score: -42.88 |\n",
"| Experiment: 0 | Episode: 1990 | Score: -15.51 | Avg Score: -42.25 |\n",
"| Experiment: 0 | Episode: 1991 | Score: -2.54 | Avg Score: -41.89 |\n",
"| Experiment: 0 | Episode: 1992 | Score: -48.96 | Avg Score: -41.81 |\n",
"| Experiment: 0 | Episode: 1993 | Score: -149.81 | Avg Score: -42.23 |\n",
"| Experiment: 0 | Episode: 1994 | Score: -50.82 | Avg Score: -41.88 |\n",
"| Experiment: 0 | Episode: 1995 | Score: -16.84 | Avg Score: -41.39 |\n",
"| Experiment: 0 | Episode: 1996 | Score: 40.14 | Avg Score: -40.37 |\n",
"| Experiment: 0 | Episode: 1997 | Score: -50.14 | Avg Score: -40.6 |\n",
"| Experiment: 0 | Episode: 1998 | Score: -22.64 | Avg Score: -40.48 |\n",
"| Experiment: 0 | Episode: 1999 | Score: -13.91 | Avg Score: -40.12 |\n",
"| Experiment: 1 | Episode: 0 | Score: -99.61 | Avg Score: -99.61 |\n",
"| Experiment: 1 | Episode: 1 | Score: -123.27 | Avg Score: -111.44 |\n",
"| Experiment: 1 | Episode: 2 | Score: -380.62 | Avg Score: -201.17 |\n",
"| Experiment: 1 | Episode: 3 | Score: -293.52 | Avg Score: -224.25 |\n",
"| Experiment: 1 | Episode: 4 | Score: -302.54 | Avg Score: -239.91 |\n",
"| Experiment: 1 | Episode: 5 | Score: -81.22 | Avg Score: -213.46 |\n",
"| Experiment: 1 | Episode: 6 | Score: -99.04 | Avg Score: -197.12 |\n",
"| Experiment: 1 | Episode: 7 | Score: -118.97 | Avg Score: -187.35 |\n",
"| Experiment: 1 | Episode: 8 | Score: -128.3 | Avg Score: -180.79 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 | Episode: 9 | Score: -124.67 | Avg Score: -175.18 |\n",
"| Experiment: 1 | Episode: 10 | Score: -42.62 | Avg Score: -163.12 |\n",
"| Experiment: 1 | Episode: 11 | Score: -75.41 | Avg Score: -155.82 |\n",
"| Experiment: 1 | Episode: 12 | Score: -183.67 | Avg Score: -157.96 |\n",
"| Experiment: 1 | Episode: 13 | Score: -408.08 | Avg Score: -175.82 |\n",
"| Experiment: 1 | Episode: 14 | Score: -395.95 | Avg Score: -190.5 |\n",
"| Experiment: 1 | Episode: 15 | Score: -474.26 | Avg Score: -208.23 |\n",
"| Experiment: 1 | Episode: 16 | Score: -315.23 | Avg Score: -214.53 |\n",
"| Experiment: 1 | Episode: 17 | Score: -388.2 | Avg Score: -224.18 |\n",
"| Experiment: 1 | Episode: 18 | Score: -301.59 | Avg Score: -228.25 |\n",
"| Experiment: 1 | Episode: 19 | Score: -294.33 | Avg Score: -231.55 |\n",
"| Experiment: 1 | Episode: 20 | Score: -333.91 | Avg Score: -236.43 |\n",
"| Experiment: 1 | Episode: 21 | Score: -180.34 | Avg Score: -233.88 |\n",
"| Experiment: 1 | Episode: 22 | Score: -440.08 | Avg Score: -242.84 |\n",
"| Experiment: 1 | Episode: 23 | Score: -282.23 | Avg Score: -244.49 |\n",
"| Experiment: 1 | Episode: 24 | Score: -430.89 | Avg Score: -251.94 |\n",
"| Experiment: 1 | Episode: 25 | Score: -504.54 | Avg Score: -261.66 |\n",
"| Experiment: 1 | Episode: 26 | Score: -382.11 | Avg Score: -266.12 |\n",
"| Experiment: 1 | Episode: 27 | Score: -379.88 | Avg Score: -270.18 |\n",
"| Experiment: 1 | Episode: 28 | Score: -113.18 | Avg Score: -264.77 |\n",
"| Experiment: 1 | Episode: 29 | Score: -266.34 | Avg Score: -264.82 |\n",
"| Experiment: 1 | Episode: 30 | Score: -136.42 | Avg Score: -260.68 |\n",
"| Experiment: 1 | Episode: 31 | Score: -588.51 | Avg Score: -270.92 |\n",
"| Experiment: 1 | Episode: 32 | Score: -294.48 | Avg Score: -271.64 |\n",
"| Experiment: 1 | Episode: 33 | Score: -456.83 | Avg Score: -277.08 |\n",
"| Experiment: 1 | Episode: 34 | Score: -96.21 | Avg Score: -271.92 |\n",
"| Experiment: 1 | Episode: 35 | Score: -154.96 | Avg Score: -268.67 |\n",
"| Experiment: 1 | Episode: 36 | Score: -345.01 | Avg Score: -270.73 |\n",
"| Experiment: 1 | Episode: 37 | Score: -308.3 | Avg Score: -271.72 |\n",
"| Experiment: 1 | Episode: 38 | Score: -303.58 | Avg Score: -272.54 |\n",
"| Experiment: 1 | Episode: 39 | Score: -355.53 | Avg Score: -274.61 |\n",
"| Experiment: 1 | Episode: 40 | Score: -254.18 | Avg Score: -274.11 |\n",
"| Experiment: 1 | Episode: 41 | Score: -265.33 | Avg Score: -273.9 |\n",
"| Experiment: 1 | Episode: 42 | Score: -57.96 | Avg Score: -268.88 |\n",
"| Experiment: 1 | Episode: 43 | Score: -194.21 | Avg Score: -267.18 |\n",
"| Experiment: 1 | Episode: 44 | Score: -127.21 | Avg Score: -264.07 |\n",
"| Experiment: 1 | Episode: 45 | Score: -45.47 | Avg Score: -259.32 |\n",
"| Experiment: 1 | Episode: 46 | Score: -391.87 | Avg Score: -262.14 |\n",
"| Experiment: 1 | Episode: 47 | Score: -166.44 | Avg Score: -260.15 |\n",
"| Experiment: 1 | Episode: 48 | Score: -90.77 | Avg Score: -256.69 |\n",
"| Experiment: 1 | Episode: 49 | Score: -4.68 | Avg Score: -251.65 |\n",
"| Experiment: 1 | Episode: 50 | Score: -96.94 | Avg Score: -248.62 |\n",
"| Experiment: 1 | Episode: 51 | Score: -88.5 | Avg Score: -245.54 |\n",
"| Experiment: 1 | Episode: 52 | Score: -134.97 | Avg Score: -243.45 |\n",
"| Experiment: 1 | Episode: 53 | Score: -380.04 | Avg Score: -245.98 |\n",
"| Experiment: 1 | Episode: 54 | Score: -296.5 | Avg Score: -246.9 |\n",
"| Experiment: 1 | Episode: 55 | Score: -105.78 | Avg Score: -244.38 |\n",
"| Experiment: 1 | Episode: 56 | Score: -121.38 | Avg Score: -242.22 |\n",
"| Experiment: 1 | Episode: 57 | Score: -75.88 | Avg Score: -239.35 |\n",
"| Experiment: 1 | Episode: 58 | Score: -94.86 | Avg Score: -236.91 |\n",
"| Experiment: 1 | Episode: 59 | Score: -108.78 | Avg Score: -234.77 |\n",
"| Experiment: 1 | Episode: 60 | Score: -385.09 | Avg Score: -237.23 |\n",
"| Experiment: 1 | Episode: 61 | Score: -26.92 | Avg Score: -233.84 |\n",
"| Experiment: 1 | Episode: 62 | Score: -171.3 | Avg Score: -232.85 |\n",
"| Experiment: 1 | Episode: 63 | Score: -378.21 | Avg Score: -235.12 |\n",
"| Experiment: 1 | Episode: 64 | Score: -154.27 | Avg Score: -233.88 |\n",
"| Experiment: 1 | Episode: 65 | Score: -145.52 | Avg Score: -232.54 |\n",
"| Experiment: 1 | Episode: 66 | Score: -125.3 | Avg Score: -230.94 |\n",
"| Experiment: 1 | Episode: 67 | Score: -145.94 | Avg Score: -229.69 |\n",
"| Experiment: 1 | Episode: 68 | Score: -252.96 | Avg Score: -230.02 |\n",
"| Experiment: 1 | Episode: 69 | Score: -44.54 | Avg Score: -227.37 |\n",
"| Experiment: 1 | Episode: 70 | Score: -212.24 | Avg Score: -227.16 |\n",
"| Experiment: 1 | Episode: 71 | Score: -113.85 | Avg Score: -225.59 |\n",
"| Experiment: 1 | Episode: 72 | Score: -52.05 | Avg Score: -223.21 |\n",
"| Experiment: 1 | Episode: 73 | Score: -107.72 | Avg Score: -221.65 |\n",
"| Experiment: 1 | Episode: 74 | Score: -121.47 | Avg Score: -220.31 |\n",
"| Experiment: 1 | Episode: 75 | Score: -337.9 | Avg Score: -221.86 |\n",
"| Experiment: 1 | Episode: 76 | Score: -103.38 | Avg Score: -220.32 |\n",
"| Experiment: 1 | Episode: 77 | Score: -158.38 | Avg Score: -219.53 |\n",
"| Experiment: 1 | Episode: 78 | Score: -243.17 | Avg Score: -219.83 |\n",
"| Experiment: 1 | Episode: 79 | Score: -92.07 | Avg Score: -218.23 |\n",
"| Experiment: 1 | Episode: 80 | Score: -43.1 | Avg Score: -216.07 |\n",
"| Experiment: 1 | Episode: 81 | Score: -210.99 | Avg Score: -216.01 |\n",
"| Experiment: 1 | Episode: 82 | Score: -141.9 | Avg Score: -215.11 |\n",
"| Experiment: 1 | Episode: 83 | Score: -139.57 | Avg Score: -214.21 |\n",
"| Experiment: 1 | Episode: 84 | Score: -174.4 | Avg Score: -213.75 |\n",
"| Experiment: 1 | Episode: 85 | Score: -1.11 | Avg Score: -211.27 |\n",
"| Experiment: 1 | Episode: 86 | Score: -80.97 | Avg Score: -209.78 |\n",
"| Experiment: 1 | Episode: 87 | Score: -99.11 | Avg Score: -208.52 |\n",
"| Experiment: 1 | Episode: 88 | Score: -169.94 | Avg Score: -208.08 |\n",
"| Experiment: 1 | Episode: 89 | Score: -266.62 | Avg Score: -208.74 |\n",
"| Experiment: 1 | Episode: 90 | Score: -48.41 | Avg Score: -206.97 |\n",
"| Experiment: 1 | Episode: 91 | Score: -307.02 | Avg Score: -208.06 |\n",
"| Experiment: 1 | Episode: 92 | Score: -231.07 | Avg Score: -208.31 |\n",
"| Experiment: 1 | Episode: 93 | Score: -293.79 | Avg Score: -209.22 |\n",
"| Experiment: 1 | Episode: 94 | Score: -117.3 | Avg Score: -208.25 |\n",
"| Experiment: 1 | Episode: 95 | Score: -317.82 | Avg Score: -209.39 |\n",
"| Experiment: 1 | Episode: 96 | Score: -194.04 | Avg Score: -209.23 |\n",
"| Experiment: 1 | Episode: 97 | Score: -101.02 | Avg Score: -208.13 |\n",
"| Experiment: 1 | Episode: 98 | Score: -29.4 | Avg Score: -206.32 |\n",
"| Experiment: 1 | Episode: 99 | Score: -309.97 | Avg Score: -207.36 |\n",
"| Experiment: 1 | Episode: 100 | Score: -77.66 | Avg Score: -207.14 |\n",
"| Experiment: 1 | Episode: 101 | Score: -47.5 | Avg Score: -206.38 |\n",
"| Experiment: 1 | Episode: 102 | Score: -149.49 | Avg Score: -204.07 |\n",
"| Experiment: 1 | Episode: 103 | Score: -120.89 | Avg Score: -202.35 |\n",
"| Experiment: 1 | Episode: 104 | Score: -195.12 | Avg Score: -201.27 |\n",
"| Experiment: 1 | Episode: 105 | Score: -8.03 | Avg Score: -200.54 |\n",
"| Experiment: 1 | Episode: 106 | Score: -108.58 | Avg Score: -200.63 |\n",
"| Experiment: 1 | Episode: 107 | Score: -176.98 | Avg Score: -201.21 |\n",
"| Experiment: 1 | Episode: 108 | Score: -158.45 | Avg Score: -201.52 |\n",
"| Experiment: 1 | Episode: 109 | Score: -350.95 | Avg Score: -203.78 |\n",
"| Experiment: 1 | Episode: 110 | Score: -100.07 | Avg Score: -204.35 |\n",
"| Experiment: 1 | Episode: 111 | Score: -71.65 | Avg Score: -204.32 |\n",
"| Experiment: 1 | Episode: 112 | Score: -120.52 | Avg Score: -203.68 |\n",
"| Experiment: 1 | Episode: 113 | Score: -152.49 | Avg Score: -201.13 |\n",
"| Experiment: 1 | Episode: 114 | Score: -143.42 | Avg Score: -198.6 |\n",
"| Experiment: 1 | Episode: 115 | Score: -231.94 | Avg Score: -196.18 |\n",
"| Experiment: 1 | Episode: 116 | Score: -133.4 | Avg Score: -194.36 |\n",
"| Experiment: 1 | Episode: 117 | Score: -247.55 | Avg Score: -192.96 |\n",
"| Experiment: 1 | Episode: 118 | Score: -74.27 | Avg Score: -190.68 |\n",
"| Experiment: 1 | Episode: 119 | Score: -407.43 | Avg Score: -191.81 |\n",
"| Experiment: 1 | Episode: 120 | Score: -304.25 | Avg Score: -191.52 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 | Episode: 121 | Score: -204.23 | Avg Score: -191.76 |\n",
"| Experiment: 1 | Episode: 122 | Score: -169.81 | Avg Score: -189.05 |\n",
"| Experiment: 1 | Episode: 123 | Score: -237.57 | Avg Score: -188.61 |\n",
"| Experiment: 1 | Episode: 124 | Score: -113.24 | Avg Score: -185.43 |\n",
"| Experiment: 1 | Episode: 125 | Score: -289.67 | Avg Score: -183.28 |\n",
"| Experiment: 1 | Episode: 126 | Score: -164.22 | Avg Score: -181.1 |\n",
"| Experiment: 1 | Episode: 127 | Score: -99.88 | Avg Score: -178.3 |\n",
"| Experiment: 1 | Episode: 128 | Score: -335.09 | Avg Score: -180.52 |\n",
"| Experiment: 1 | Episode: 129 | Score: -35.72 | Avg Score: -178.21 |\n",
"| Experiment: 1 | Episode: 130 | Score: -93.71 | Avg Score: -177.79 |\n",
"| Experiment: 1 | Episode: 131 | Score: -135.63 | Avg Score: -173.26 |\n",
"| Experiment: 1 | Episode: 132 | Score: -140.62 | Avg Score: -171.72 |\n",
"| Experiment: 1 | Episode: 133 | Score: -291.25 | Avg Score: -170.06 |\n",
"| Experiment: 1 | Episode: 134 | Score: -100.33 | Avg Score: -170.11 |\n",
"| Experiment: 1 | Episode: 135 | Score: -92.79 | Avg Score: -169.48 |\n",
"| Experiment: 1 | Episode: 136 | Score: -335.36 | Avg Score: -169.39 |\n",
"| Experiment: 1 | Episode: 137 | Score: -125.02 | Avg Score: -167.55 |\n",
"| Experiment: 1 | Episode: 138 | Score: -126.78 | Avg Score: -165.79 |\n",
"| Experiment: 1 | Episode: 139 | Score: -332.97 | Avg Score: -165.56 |\n",
"| Experiment: 1 | Episode: 140 | Score: -145.41 | Avg Score: -164.47 |\n",
"| Experiment: 1 | Episode: 141 | Score: -34.7 | Avg Score: -162.17 |\n",
"| Experiment: 1 | Episode: 142 | Score: -252.82 | Avg Score: -164.12 |\n",
"| Experiment: 1 | Episode: 143 | Score: -101.03 | Avg Score: -163.18 |\n",
"| Experiment: 1 | Episode: 144 | Score: -134.48 | Avg Score: -163.26 |\n",
"| Experiment: 1 | Episode: 145 | Score: -266.74 | Avg Score: -165.47 |\n",
"| Experiment: 1 | Episode: 146 | Score: -87.69 | Avg Score: -162.43 |\n",
"| Experiment: 1 | Episode: 147 | Score: -61.19 | Avg Score: -161.38 |\n",
"| Experiment: 1 | Episode: 148 | Score: -96.95 | Avg Score: -161.44 |\n",
"| Experiment: 1 | Episode: 149 | Score: -194.57 | Avg Score: -163.34 |\n",
"| Experiment: 1 | Episode: 150 | Score: -166.53 | Avg Score: -164.03 |\n",
"| Experiment: 1 | Episode: 151 | Score: -414.07 | Avg Score: -167.29 |\n",
"| Experiment: 1 | Episode: 152 | Score: -307.19 | Avg Score: -169.01 |\n",
"| Experiment: 1 | Episode: 153 | Score: -78.18 | Avg Score: -165.99 |\n",
"| Experiment: 1 | Episode: 154 | Score: -260.94 | Avg Score: -165.64 |\n",
"| Experiment: 1 | Episode: 155 | Score: -78.27 | Avg Score: -165.36 |\n",
"| Experiment: 1 | Episode: 156 | Score: -254.56 | Avg Score: -166.69 |\n",
"| Experiment: 1 | Episode: 157 | Score: -396.8 | Avg Score: -169.9 |\n",
"| Experiment: 1 | Episode: 158 | Score: -339.37 | Avg Score: -172.35 |\n",
"| Experiment: 1 | Episode: 159 | Score: -113.36 | Avg Score: -172.39 |\n",
"| Experiment: 1 | Episode: 160 | Score: -345.19 | Avg Score: -171.99 |\n",
"| Experiment: 1 | Episode: 161 | Score: -298.0 | Avg Score: -174.7 |\n",
"| Experiment: 1 | Episode: 162 | Score: -52.58 | Avg Score: -173.52 |\n",
"| Experiment: 1 | Episode: 163 | Score: -184.16 | Avg Score: -171.58 |\n",
"| Experiment: 1 | Episode: 164 | Score: -122.23 | Avg Score: -171.26 |\n",
"| Experiment: 1 | Episode: 165 | Score: -127.83 | Avg Score: -171.08 |\n",
"| Experiment: 1 | Episode: 166 | Score: -231.31 | Avg Score: -172.14 |\n",
"| Experiment: 1 | Episode: 167 | Score: -195.4 | Avg Score: -172.63 |\n",
"| Experiment: 1 | Episode: 168 | Score: -341.42 | Avg Score: -173.52 |\n",
"| Experiment: 1 | Episode: 169 | Score: -299.06 | Avg Score: -176.06 |\n",
"| Experiment: 1 | Episode: 170 | Score: -419.53 | Avg Score: -178.14 |\n",
"| Experiment: 1 | Episode: 171 | Score: -358.63 | Avg Score: -180.58 |\n",
"| Experiment: 1 | Episode: 172 | Score: -296.94 | Avg Score: -183.03 |\n",
"| Experiment: 1 | Episode: 173 | Score: -105.35 | Avg Score: -183.01 |\n",
"| Experiment: 1 | Episode: 174 | Score: -66.59 | Avg Score: -182.46 |\n",
"| Experiment: 1 | Episode: 175 | Score: 136.57 | Avg Score: -177.72 |\n",
"| Experiment: 1 | Episode: 176 | Score: -230.68 | Avg Score: -178.99 |\n",
"| Experiment: 1 | Episode: 177 | Score: -82.98 | Avg Score: -178.24 |\n",
"| Experiment: 1 | Episode: 178 | Score: -106.65 | Avg Score: -176.87 |\n",
"| Experiment: 1 | Episode: 179 | Score: -169.53 | Avg Score: -177.64 |\n",
"| Experiment: 1 | Episode: 180 | Score: -99.01 | Avg Score: -178.2 |\n",
"| Experiment: 1 | Episode: 181 | Score: -257.43 | Avg Score: -178.67 |\n",
"| Experiment: 1 | Episode: 182 | Score: -124.62 | Avg Score: -178.5 |\n",
"| Experiment: 1 | Episode: 183 | Score: -323.98 | Avg Score: -180.34 |\n",
"| Experiment: 1 | Episode: 184 | Score: -233.06 | Avg Score: -180.93 |\n",
"| Experiment: 1 | Episode: 185 | Score: -204.47 | Avg Score: -182.96 |\n",
"| Experiment: 1 | Episode: 186 | Score: -303.29 | Avg Score: -185.18 |\n",
"| Experiment: 1 | Episode: 187 | Score: -316.29 | Avg Score: -187.35 |\n",
"| Experiment: 1 | Episode: 188 | Score: 17.98 | Avg Score: -185.48 |\n",
"| Experiment: 1 | Episode: 189 | Score: -323.91 | Avg Score: -186.05 |\n",
"| Experiment: 1 | Episode: 190 | Score: -100.38 | Avg Score: -186.57 |\n",
"| Experiment: 1 | Episode: 191 | Score: -308.36 | Avg Score: -186.58 |\n",
"| Experiment: 1 | Episode: 192 | Score: -91.42 | Avg Score: -185.18 |\n",
"| Experiment: 1 | Episode: 193 | Score: -439.82 | Avg Score: -186.64 |\n",
"| Experiment: 1 | Episode: 194 | Score: -149.44 | Avg Score: -186.97 |\n",
"| Experiment: 1 | Episode: 195 | Score: -103.74 | Avg Score: -184.83 |\n",
"| Experiment: 1 | Episode: 196 | Score: -67.21 | Avg Score: -183.56 |\n",
"| Experiment: 1 | Episode: 197 | Score: -184.55 | Avg Score: -184.39 |\n",
"| Experiment: 1 | Episode: 198 | Score: -129.75 | Avg Score: -185.4 |\n",
"| Experiment: 1 | Episode: 199 | Score: -114.17 | Avg Score: -183.44 |\n",
"| Experiment: 1 | Episode: 200 | Score: -103.21 | Avg Score: -183.69 |\n",
"| Experiment: 1 | Episode: 201 | Score: -117.31 | Avg Score: -184.39 |\n",
"| Experiment: 1 | Episode: 202 | Score: -236.98 | Avg Score: -185.27 |\n",
"| Experiment: 1 | Episode: 203 | Score: -229.52 | Avg Score: -186.35 |\n",
"| Experiment: 1 | Episode: 204 | Score: -293.81 | Avg Score: -187.34 |\n",
"| Experiment: 1 | Episode: 205 | Score: -230.0 | Avg Score: -189.56 |\n",
"| Experiment: 1 | Episode: 206 | Score: -261.17 | Avg Score: -191.09 |\n",
"| Experiment: 1 | Episode: 207 | Score: -81.33 | Avg Score: -190.13 |\n",
"| Experiment: 1 | Episode: 208 | Score: -342.36 | Avg Score: -191.97 |\n",
"| Experiment: 1 | Episode: 209 | Score: -180.72 | Avg Score: -190.27 |\n",
"| Experiment: 1 | Episode: 210 | Score: -234.98 | Avg Score: -191.61 |\n",
"| Experiment: 1 | Episode: 211 | Score: -267.75 | Avg Score: -193.58 |\n",
"| Experiment: 1 | Episode: 212 | Score: -482.83 | Avg Score: -197.2 |\n",
"| Experiment: 1 | Episode: 213 | Score: -57.68 | Avg Score: -196.25 |\n",
"| Experiment: 1 | Episode: 214 | Score: -109.91 | Avg Score: -195.92 |\n",
"| Experiment: 1 | Episode: 215 | Score: -90.55 | Avg Score: -194.5 |\n",
"| Experiment: 1 | Episode: 216 | Score: -378.2 | Avg Score: -196.95 |\n",
"| Experiment: 1 | Episode: 217 | Score: -319.3 | Avg Score: -197.67 |\n",
"| Experiment: 1 | Episode: 218 | Score: -266.49 | Avg Score: -199.59 |\n",
"| Experiment: 1 | Episode: 219 | Score: -288.88 | Avg Score: -198.4 |\n",
"| Experiment: 1 | Episode: 220 | Score: -311.42 | Avg Score: -198.48 |\n",
"| Experiment: 1 | Episode: 221 | Score: -150.34 | Avg Score: -197.94 |\n",
"| Experiment: 1 | Episode: 222 | Score: -47.25 | Avg Score: -196.71 |\n",
"| Experiment: 1 | Episode: 223 | Score: -114.47 | Avg Score: -195.48 |\n",
"| Experiment: 1 | Episode: 224 | Score: -30.98 | Avg Score: -194.66 |\n",
"| Experiment: 1 | Episode: 225 | Score: -86.34 | Avg Score: -192.62 |\n",
"| Experiment: 1 | Episode: 226 | Score: -84.75 | Avg Score: -191.83 |\n",
"| Experiment: 1 | Episode: 227 | Score: -110.01 | Avg Score: -191.93 |\n",
"| Experiment: 1 | Episode: 228 | Score: -49.75 | Avg Score: -189.08 |\n",
"| Experiment: 1 | Episode: 229 | Score: -197.58 | Avg Score: -190.7 |\n",
"| Experiment: 1 | Episode: 230 | Score: -162.02 | Avg Score: -191.38 |\n",
"| Experiment: 1 | Episode: 231 | Score: -208.62 | Avg Score: -192.11 |\n",
"| Experiment: 1 | Episode: 232 | Score: -234.3 | Avg Score: -193.05 |\n",
"| Experiment: 1 | Episode: 233 | Score: -179.92 | Avg Score: -191.93 |\n",
"| Experiment: 1 | Episode: 234 | Score: -90.44 | Avg Score: -191.83 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 | Episode: 235 | Score: -118.09 | Avg Score: -192.09 |\n",
"| Experiment: 1 | Episode: 236 | Score: -121.96 | Avg Score: -189.95 |\n",
"| Experiment: 1 | Episode: 237 | Score: -199.25 | Avg Score: -190.7 |\n",
"| Experiment: 1 | Episode: 238 | Score: -179.24 | Avg Score: -191.22 |\n",
"| Experiment: 1 | Episode: 239 | Score: -113.34 | Avg Score: -189.02 |\n",
"| Experiment: 1 | Episode: 240 | Score: -174.87 | Avg Score: -189.32 |\n",
"| Experiment: 1 | Episode: 241 | Score: -89.65 | Avg Score: -189.87 |\n",
"| Experiment: 1 | Episode: 242 | Score: -130.84 | Avg Score: -188.65 |\n",
"| Experiment: 1 | Episode: 243 | Score: -92.35 | Avg Score: -188.56 |\n",
"| Experiment: 1 | Episode: 244 | Score: -37.27 | Avg Score: -187.59 |\n",
"| Experiment: 1 | Episode: 245 | Score: -317.29 | Avg Score: -188.09 |\n",
"| Experiment: 1 | Episode: 246 | Score: -84.53 | Avg Score: -188.06 |\n",
"| Experiment: 1 | Episode: 247 | Score: -135.07 | Avg Score: -188.8 |\n",
"| Experiment: 1 | Episode: 248 | Score: -85.63 | Avg Score: -188.69 |\n",
"| Experiment: 1 | Episode: 249 | Score: -166.83 | Avg Score: -188.41 |\n",
"| Experiment: 1 | Episode: 250 | Score: -154.75 | Avg Score: -188.29 |\n",
"| Experiment: 1 | Episode: 251 | Score: -252.16 | Avg Score: -186.67 |\n",
"| Experiment: 1 | Episode: 252 | Score: -379.99 | Avg Score: -187.4 |\n",
"| Experiment: 1 | Episode: 253 | Score: -329.06 | Avg Score: -189.91 |\n",
"| Experiment: 1 | Episode: 254 | Score: -261.99 | Avg Score: -189.92 |\n",
"| Experiment: 1 | Episode: 255 | Score: -444.53 | Avg Score: -193.58 |\n",
"| Experiment: 1 | Episode: 256 | Score: -99.71 | Avg Score: -192.04 |\n",
"| Experiment: 1 | Episode: 257 | Score: -95.44 | Avg Score: -189.02 |\n",
"| Experiment: 1 | Episode: 258 | Score: -197.28 | Avg Score: -187.6 |\n",
"| Experiment: 1 | Episode: 259 | Score: -153.81 | Avg Score: -188.01 |\n",
"| Experiment: 1 | Episode: 260 | Score: -184.71 | Avg Score: -186.4 |\n",
"| Experiment: 1 | Episode: 261 | Score: -204.13 | Avg Score: -185.46 |\n",
"| Experiment: 1 | Episode: 262 | Score: -278.31 | Avg Score: -187.72 |\n",
"| Experiment: 1 | Episode: 263 | Score: -206.18 | Avg Score: -187.94 |\n",
"| Experiment: 1 | Episode: 264 | Score: -141.52 | Avg Score: -188.13 |\n",
"| Experiment: 1 | Episode: 265 | Score: -137.32 | Avg Score: -188.23 |\n",
"| Experiment: 1 | Episode: 266 | Score: -225.75 | Avg Score: -188.17 |\n",
"| Experiment: 1 | Episode: 267 | Score: -112.0 | Avg Score: -187.34 |\n",
"| Experiment: 1 | Episode: 268 | Score: -58.31 | Avg Score: -184.51 |\n",
"| Experiment: 1 | Episode: 269 | Score: -79.65 | Avg Score: -182.31 |\n",
"| Experiment: 1 | Episode: 270 | Score: -288.1 | Avg Score: -181.0 |\n",
"| Experiment: 1 | Episode: 271 | Score: -368.08 | Avg Score: -181.09 |\n",
"| Experiment: 1 | Episode: 272 | Score: -69.77 | Avg Score: -178.82 |\n",
"| Experiment: 1 | Episode: 273 | Score: -128.85 | Avg Score: -179.06 |\n",
"| Experiment: 1 | Episode: 274 | Score: -91.51 | Avg Score: -179.3 |\n",
"| Experiment: 1 | Episode: 275 | Score: -91.17 | Avg Score: -181.58 |\n",
"| Experiment: 1 | Episode: 276 | Score: -30.8 | Avg Score: -179.58 |\n",
"| Experiment: 1 | Episode: 277 | Score: -56.13 | Avg Score: -179.31 |\n",
"| Experiment: 1 | Episode: 278 | Score: -119.05 | Avg Score: -179.44 |\n",
"| Experiment: 1 | Episode: 279 | Score: -63.46 | Avg Score: -178.38 |\n",
"| Experiment: 1 | Episode: 280 | Score: -170.99 | Avg Score: -179.1 |\n",
"| Experiment: 1 | Episode: 281 | Score: -78.96 | Avg Score: -177.31 |\n",
"| Experiment: 1 | Episode: 282 | Score: -55.22 | Avg Score: -176.62 |\n",
"| Experiment: 1 | Episode: 283 | Score: -107.36 | Avg Score: -174.45 |\n",
"| Experiment: 1 | Episode: 284 | Score: -191.21 | Avg Score: -174.03 |\n",
"| Experiment: 1 | Episode: 285 | Score: -80.52 | Avg Score: -172.8 |\n",
"| Experiment: 1 | Episode: 286 | Score: -140.8 | Avg Score: -171.17 |\n",
"| Experiment: 1 | Episode: 287 | Score: -302.75 | Avg Score: -171.03 |\n",
"| Experiment: 1 | Episode: 288 | Score: -95.99 | Avg Score: -172.17 |\n",
"| Experiment: 1 | Episode: 289 | Score: -177.49 | Avg Score: -170.71 |\n",
"| Experiment: 1 | Episode: 290 | Score: -151.48 | Avg Score: -171.22 |\n",
"| Experiment: 1 | Episode: 291 | Score: -55.86 | Avg Score: -168.7 |\n",
"| Experiment: 1 | Episode: 292 | Score: -97.82 | Avg Score: -168.76 |\n",
"| Experiment: 1 | Episode: 293 | Score: -55.05 | Avg Score: -164.91 |\n",
"| Experiment: 1 | Episode: 294 | Score: -334.6 | Avg Score: -166.76 |\n",
"| Experiment: 1 | Episode: 295 | Score: -63.68 | Avg Score: -166.36 |\n",
"| Experiment: 1 | Episode: 296 | Score: -81.18 | Avg Score: -166.5 |\n",
"| Experiment: 1 | Episode: 297 | Score: -79.81 | Avg Score: -165.46 |\n",
"| Experiment: 1 | Episode: 298 | Score: -67.65 | Avg Score: -164.83 |\n",
"| Experiment: 1 | Episode: 299 | Score: -173.52 | Avg Score: -165.43 |\n",
"| Experiment: 1 | Episode: 300 | Score: -171.96 | Avg Score: -166.12 |\n",
"| Experiment: 1 | Episode: 301 | Score: -73.66 | Avg Score: -165.68 |\n",
"| Experiment: 1 | Episode: 302 | Score: -198.74 | Avg Score: -165.3 |\n",
"| Experiment: 1 | Episode: 303 | Score: -130.21 | Avg Score: -164.3 |\n",
"| Experiment: 1 | Episode: 304 | Score: -105.87 | Avg Score: -162.42 |\n",
"| Experiment: 1 | Episode: 305 | Score: -96.24 | Avg Score: -161.09 |\n",
"| Experiment: 1 | Episode: 306 | Score: 2.1 | Avg Score: -158.45 |\n",
"| Experiment: 1 | Episode: 307 | Score: -155.83 | Avg Score: -159.2 |\n",
"| Experiment: 1 | Episode: 308 | Score: -100.35 | Avg Score: -156.78 |\n",
"| Experiment: 1 | Episode: 309 | Score: -323.76 | Avg Score: -158.21 |\n",
"| Experiment: 1 | Episode: 310 | Score: -136.18 | Avg Score: -157.22 |\n",
"| Experiment: 1 | Episode: 311 | Score: -164.26 | Avg Score: -156.19 |\n",
"| Experiment: 1 | Episode: 312 | Score: -252.84 | Avg Score: -153.89 |\n",
"| Experiment: 1 | Episode: 313 | Score: -402.88 | Avg Score: -157.34 |\n",
"| Experiment: 1 | Episode: 314 | Score: -164.82 | Avg Score: -157.89 |\n",
"| Experiment: 1 | Episode: 315 | Score: -95.94 | Avg Score: -157.94 |\n",
"| Experiment: 1 | Episode: 316 | Score: -64.22 | Avg Score: -154.8 |\n",
"| Experiment: 1 | Episode: 317 | Score: -117.89 | Avg Score: -152.79 |\n",
"| Experiment: 1 | Episode: 318 | Score: -167.64 | Avg Score: -151.8 |\n",
"| Experiment: 1 | Episode: 319 | Score: -117.9 | Avg Score: -150.09 |\n",
"| Experiment: 1 | Episode: 320 | Score: -305.91 | Avg Score: -150.03 |\n",
"| Experiment: 1 | Episode: 321 | Score: -97.54 | Avg Score: -149.51 |\n",
"| Experiment: 1 | Episode: 322 | Score: -179.8 | Avg Score: -150.83 |\n",
"| Experiment: 1 | Episode: 323 | Score: -274.62 | Avg Score: -152.43 |\n",
"| Experiment: 1 | Episode: 324 | Score: -287.21 | Avg Score: -155.0 |\n",
"| Experiment: 1 | Episode: 325 | Score: -116.08 | Avg Score: -155.29 |\n",
"| Experiment: 1 | Episode: 326 | Score: -101.31 | Avg Score: -155.46 |\n",
"| Experiment: 1 | Episode: 327 | Score: -129.54 | Avg Score: -155.65 |\n",
"| Experiment: 1 | Episode: 328 | Score: -109.19 | Avg Score: -156.25 |\n",
"| Experiment: 1 | Episode: 329 | Score: -148.27 | Avg Score: -155.76 |\n",
"| Experiment: 1 | Episode: 330 | Score: -169.09 | Avg Score: -155.83 |\n",
"| Experiment: 1 | Episode: 331 | Score: -116.55 | Avg Score: -154.91 |\n",
"| Experiment: 1 | Episode: 332 | Score: -112.15 | Avg Score: -153.68 |\n",
"| Experiment: 1 | Episode: 333 | Score: -53.62 | Avg Score: -152.42 |\n",
"| Experiment: 1 | Episode: 334 | Score: -120.83 | Avg Score: -152.72 |\n",
"| Experiment: 1 | Episode: 335 | Score: -89.61 | Avg Score: -152.44 |\n",
"| Experiment: 1 | Episode: 336 | Score: -226.47 | Avg Score: -153.48 |\n",
"| Experiment: 1 | Episode: 337 | Score: -295.36 | Avg Score: -154.45 |\n",
"| Experiment: 1 | Episode: 338 | Score: -268.15 | Avg Score: -155.33 |\n",
"| Experiment: 1 | Episode: 339 | Score: -161.85 | Avg Score: -155.82 |\n",
"| Experiment: 1 | Episode: 340 | Score: -311.59 | Avg Score: -157.19 |\n",
"| Experiment: 1 | Episode: 341 | Score: -81.38 | Avg Score: -157.1 |\n",
"| Experiment: 1 | Episode: 342 | Score: -235.24 | Avg Score: -158.15 |\n",
"| Experiment: 1 | Episode: 343 | Score: 8.67 | Avg Score: -157.14 |\n",
"| Experiment: 1 | Episode: 344 | Score: -115.23 | Avg Score: -157.92 |\n",
"| Experiment: 1 | Episode: 345 | Score: -54.69 | Avg Score: -155.29 |\n",
"| Experiment: 1 | Episode: 346 | Score: -270.46 | Avg Score: -157.15 |\n",
"| Experiment: 1 | Episode: 347 | Score: -61.98 | Avg Score: -156.42 |\n",
"| Experiment: 1 | Episode: 348 | Score: -139.38 | Avg Score: -156.96 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 | Episode: 349 | Score: -213.1 | Avg Score: -157.42 |\n",
"| Experiment: 1 | Episode: 350 | Score: -67.72 | Avg Score: -156.55 |\n",
"| Experiment: 1 | Episode: 351 | Score: -78.44 | Avg Score: -154.81 |\n",
"| Experiment: 1 | Episode: 352 | Score: -97.94 | Avg Score: -151.99 |\n",
"| Experiment: 1 | Episode: 353 | Score: -188.64 | Avg Score: -150.59 |\n",
"| Experiment: 1 | Episode: 354 | Score: -199.76 | Avg Score: -149.97 |\n",
"| Experiment: 1 | Episode: 355 | Score: -293.53 | Avg Score: -148.46 |\n",
"| Experiment: 1 | Episode: 356 | Score: -115.97 | Avg Score: -148.62 |\n",
"| Experiment: 1 | Episode: 357 | Score: -277.27 | Avg Score: -150.44 |\n",
"| Experiment: 1 | Episode: 358 | Score: -212.96 | Avg Score: -150.59 |\n",
"| Experiment: 1 | Episode: 359 | Score: -134.56 | Avg Score: -150.4 |\n",
"| Experiment: 1 | Episode: 360 | Score: -102.71 | Avg Score: -149.58 |\n",
"| Experiment: 1 | Episode: 361 | Score: -118.97 | Avg Score: -148.73 |\n",
"| Experiment: 1 | Episode: 362 | Score: -36.41 | Avg Score: -146.31 |\n",
"| Experiment: 1 | Episode: 363 | Score: -99.81 | Avg Score: -145.25 |\n",
"| Experiment: 1 | Episode: 364 | Score: -63.18 | Avg Score: -144.46 |\n",
"| Experiment: 1 | Episode: 365 | Score: -91.43 | Avg Score: -144.0 |\n",
"| Experiment: 1 | Episode: 366 | Score: -287.7 | Avg Score: -144.62 |\n",
"| Experiment: 1 | Episode: 367 | Score: -80.81 | Avg Score: -144.31 |\n",
"| Experiment: 1 | Episode: 368 | Score: -197.73 | Avg Score: -145.71 |\n",
"| Experiment: 1 | Episode: 369 | Score: -172.94 | Avg Score: -146.64 |\n",
"| Experiment: 1 | Episode: 370 | Score: -152.7 | Avg Score: -145.29 |\n",
"| Experiment: 1 | Episode: 371 | Score: -88.39 | Avg Score: -142.49 |\n",
"| Experiment: 1 | Episode: 372 | Score: -67.3 | Avg Score: -142.46 |\n",
"| Experiment: 1 | Episode: 373 | Score: -123.69 | Avg Score: -142.41 |\n",
"| Experiment: 1 | Episode: 374 | Score: -90.44 | Avg Score: -142.4 |\n",
"| Experiment: 1 | Episode: 375 | Score: -118.0 | Avg Score: -142.67 |\n",
"| Experiment: 1 | Episode: 376 | Score: -82.33 | Avg Score: -143.19 |\n",
"| Experiment: 1 | Episode: 377 | Score: -64.98 | Avg Score: -143.27 |\n",
"| Experiment: 1 | Episode: 378 | Score: -112.87 | Avg Score: -143.21 |\n",
"| Experiment: 1 | Episode: 379 | Score: -80.14 | Avg Score: -143.38 |\n",
"| Experiment: 1 | Episode: 380 | Score: -189.41 | Avg Score: -143.56 |\n",
"| Experiment: 1 | Episode: 381 | Score: -127.71 | Avg Score: -144.05 |\n",
"| Experiment: 1 | Episode: 382 | Score: -64.41 | Avg Score: -144.14 |\n",
"| Experiment: 1 | Episode: 383 | Score: -120.82 | Avg Score: -144.28 |\n",
"| Experiment: 1 | Episode: 384 | Score: -313.6 | Avg Score: -145.5 |\n",
"| Experiment: 1 | Episode: 385 | Score: -107.47 | Avg Score: -145.77 |\n",
"| Experiment: 1 | Episode: 386 | Score: -183.2 | Avg Score: -146.19 |\n",
"| Experiment: 1 | Episode: 387 | Score: -172.32 | Avg Score: -144.89 |\n",
"| Experiment: 1 | Episode: 388 | Score: -138.19 | Avg Score: -145.31 |\n",
"| Experiment: 1 | Episode: 389 | Score: -49.68 | Avg Score: -144.03 |\n",
"| Experiment: 1 | Episode: 390 | Score: -225.45 | Avg Score: -144.77 |\n",
"| Experiment: 1 | Episode: 391 | Score: -92.29 | Avg Score: -145.14 |\n",
"| Experiment: 1 | Episode: 392 | Score: -371.15 | Avg Score: -147.87 |\n",
"| Experiment: 1 | Episode: 393 | Score: -75.24 | Avg Score: -148.07 |\n",
"| Experiment: 1 | Episode: 394 | Score: -101.84 | Avg Score: -145.75 |\n",
"| Experiment: 1 | Episode: 395 | Score: -304.5 | Avg Score: -148.15 |\n",
"| Experiment: 1 | Episode: 396 | Score: -120.75 | Avg Score: -148.55 |\n",
"| Experiment: 1 | Episode: 397 | Score: -138.28 | Avg Score: -149.13 |\n",
"| Experiment: 1 | Episode: 398 | Score: -184.58 | Avg Score: -150.3 |\n",
"| Experiment: 1 | Episode: 399 | Score: -127.37 | Avg Score: -149.84 |\n",
"| Experiment: 1 | Episode: 400 | Score: -174.46 | Avg Score: -149.87 |\n",
"| Experiment: 1 | Episode: 401 | Score: -315.13 | Avg Score: -152.28 |\n",
"| Experiment: 1 | Episode: 402 | Score: -175.55 | Avg Score: -152.05 |\n",
"| Experiment: 1 | Episode: 403 | Score: -83.88 | Avg Score: -151.59 |\n",
"| Experiment: 1 | Episode: 404 | Score: -289.43 | Avg Score: -153.42 |\n",
"| Experiment: 1 | Episode: 405 | Score: -133.41 | Avg Score: -153.79 |\n",
"| Experiment: 1 | Episode: 406 | Score: -179.97 | Avg Score: -155.61 |\n",
"| Experiment: 1 | Episode: 407 | Score: -126.66 | Avg Score: -155.32 |\n",
"| Experiment: 1 | Episode: 408 | Score: -81.22 | Avg Score: -155.13 |\n",
"| Experiment: 1 | Episode: 409 | Score: -258.68 | Avg Score: -154.48 |\n",
"| Experiment: 1 | Episode: 410 | Score: -5.22 | Avg Score: -153.17 |\n",
"| Experiment: 1 | Episode: 411 | Score: -171.08 | Avg Score: -153.24 |\n",
"| Experiment: 1 | Episode: 412 | Score: -73.9 | Avg Score: -151.45 |\n",
"| Experiment: 1 | Episode: 413 | Score: -111.25 | Avg Score: -148.53 |\n",
"| Experiment: 1 | Episode: 414 | Score: -362.8 | Avg Score: -150.51 |\n",
"| Experiment: 1 | Episode: 415 | Score: -112.16 | Avg Score: -150.68 |\n",
"| Experiment: 1 | Episode: 416 | Score: -95.21 | Avg Score: -150.99 |\n",
"| Experiment: 1 | Episode: 417 | Score: -149.3 | Avg Score: -151.3 |\n",
"| Experiment: 1 | Episode: 418 | Score: -22.76 | Avg Score: -149.85 |\n",
"| Experiment: 1 | Episode: 419 | Score: -196.93 | Avg Score: -150.64 |\n",
"| Experiment: 1 | Episode: 420 | Score: -146.98 | Avg Score: -149.05 |\n",
"| Experiment: 1 | Episode: 421 | Score: -74.15 | Avg Score: -148.82 |\n",
"| Experiment: 1 | Episode: 422 | Score: -74.47 | Avg Score: -147.76 |\n",
"| Experiment: 1 | Episode: 423 | Score: -171.26 | Avg Score: -146.73 |\n",
"| Experiment: 1 | Episode: 424 | Score: -94.0 | Avg Score: -144.8 |\n",
"| Experiment: 1 | Episode: 425 | Score: -152.53 | Avg Score: -145.16 |\n",
"| Experiment: 1 | Episode: 426 | Score: -192.5 | Avg Score: -146.08 |\n",
"| Experiment: 1 | Episode: 427 | Score: -263.52 | Avg Score: -147.42 |\n",
"| Experiment: 1 | Episode: 428 | Score: -48.79 | Avg Score: -146.81 |\n",
"| Experiment: 1 | Episode: 429 | Score: -199.99 | Avg Score: -147.33 |\n",
"| Experiment: 1 | Episode: 430 | Score: -101.79 | Avg Score: -146.66 |\n",
"| Experiment: 1 | Episode: 431 | Score: -162.89 | Avg Score: -147.12 |\n",
"| Experiment: 1 | Episode: 432 | Score: -503.57 | Avg Score: -151.03 |\n",
"| Experiment: 1 | Episode: 433 | Score: -142.1 | Avg Score: -151.92 |\n",
"| Experiment: 1 | Episode: 434 | Score: -65.87 | Avg Score: -151.37 |\n",
"| Experiment: 1 | Episode: 435 | Score: -86.48 | Avg Score: -151.34 |\n",
"| Experiment: 1 | Episode: 436 | Score: -419.67 | Avg Score: -153.27 |\n",
"| Experiment: 1 | Episode: 437 | Score: -79.46 | Avg Score: -151.11 |\n",
"| Experiment: 1 | Episode: 438 | Score: -79.1 | Avg Score: -149.22 |\n",
"| Experiment: 1 | Episode: 439 | Score: -107.37 | Avg Score: -148.67 |\n",
"| Experiment: 1 | Episode: 440 | Score: -95.06 | Avg Score: -146.51 |\n",
"| Experiment: 1 | Episode: 441 | Score: -235.86 | Avg Score: -148.05 |\n",
"| Experiment: 1 | Episode: 442 | Score: -56.8 | Avg Score: -146.27 |\n",
"| Experiment: 1 | Episode: 443 | Score: -288.71 | Avg Score: -149.24 |\n",
"| Experiment: 1 | Episode: 444 | Score: -91.04 | Avg Score: -149.0 |\n",
"| Experiment: 1 | Episode: 445 | Score: -174.43 | Avg Score: -150.2 |\n",
"| Experiment: 1 | Episode: 446 | Score: -266.58 | Avg Score: -150.16 |\n",
"| Experiment: 1 | Episode: 447 | Score: -79.99 | Avg Score: -150.34 |\n",
"| Experiment: 1 | Episode: 448 | Score: -126.63 | Avg Score: -150.21 |\n",
"| Experiment: 1 | Episode: 449 | Score: -70.43 | Avg Score: -148.79 |\n",
"| Experiment: 1 | Episode: 450 | Score: -83.93 | Avg Score: -148.95 |\n",
"| Experiment: 1 | Episode: 451 | Score: -76.37 | Avg Score: -148.93 |\n",
"| Experiment: 1 | Episode: 452 | Score: -102.71 | Avg Score: -148.98 |\n",
"| Experiment: 1 | Episode: 453 | Score: -74.45 | Avg Score: -147.83 |\n",
"| Experiment: 1 | Episode: 454 | Score: -165.27 | Avg Score: -147.49 |\n",
"| Experiment: 1 | Episode: 455 | Score: -185.82 | Avg Score: -146.41 |\n",
"| Experiment: 1 | Episode: 456 | Score: -137.79 | Avg Score: -146.63 |\n",
"| Experiment: 1 | Episode: 457 | Score: -57.9 | Avg Score: -144.44 |\n",
"| Experiment: 1 | Episode: 458 | Score: -244.95 | Avg Score: -144.76 |\n",
"| Experiment: 1 | Episode: 459 | Score: -110.66 | Avg Score: -144.52 |\n",
"| Experiment: 1 | Episode: 460 | Score: -194.41 | Avg Score: -145.43 |\n",
"| Experiment: 1 | Episode: 461 | Score: -262.97 | Avg Score: -146.87 |\n",
"| Experiment: 1 | Episode: 462 | Score: -107.35 | Avg Score: -147.58 |\n",
"| Experiment: 1 | Episode: 463 | Score: -110.18 | Avg Score: -147.69 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 | Episode: 464 | Score: -87.55 | Avg Score: -147.93 |\n",
"| Experiment: 1 | Episode: 465 | Score: -220.74 | Avg Score: -149.22 |\n",
"| Experiment: 1 | Episode: 466 | Score: -326.92 | Avg Score: -149.62 |\n",
"| Experiment: 1 | Episode: 467 | Score: -220.82 | Avg Score: -151.02 |\n",
"| Experiment: 1 | Episode: 468 | Score: -170.78 | Avg Score: -150.75 |\n",
"| Experiment: 1 | Episode: 469 | Score: -129.74 | Avg Score: -150.31 |\n",
"| Experiment: 1 | Episode: 470 | Score: -84.16 | Avg Score: -149.63 |\n",
"| Experiment: 1 | Episode: 471 | Score: -104.38 | Avg Score: -149.79 |\n",
"| Experiment: 1 | Episode: 472 | Score: -348.89 | Avg Score: -152.6 |\n",
"| Experiment: 1 | Episode: 473 | Score: -74.55 | Avg Score: -152.11 |\n",
"| Experiment: 1 | Episode: 474 | Score: -74.69 | Avg Score: -151.96 |\n",
"| Experiment: 1 | Episode: 475 | Score: -124.85 | Avg Score: -152.02 |\n",
"| Experiment: 1 | Episode: 476 | Score: -103.19 | Avg Score: -152.23 |\n",
"| Experiment: 1 | Episode: 477 | Score: -102.96 | Avg Score: -152.61 |\n",
"| Experiment: 1 | Episode: 478 | Score: -165.89 | Avg Score: -153.14 |\n",
"| Experiment: 1 | Episode: 479 | Score: -232.54 | Avg Score: -154.67 |\n",
"| Experiment: 1 | Episode: 480 | Score: -71.63 | Avg Score: -153.49 |\n",
"| Experiment: 1 | Episode: 481 | Score: -54.13 | Avg Score: -152.75 |\n",
"| Experiment: 1 | Episode: 482 | Score: -115.4 | Avg Score: -153.26 |\n",
"| Experiment: 1 | Episode: 483 | Score: -144.61 | Avg Score: -153.5 |\n",
"| Experiment: 1 | Episode: 484 | Score: -32.88 | Avg Score: -150.69 |\n",
"| Experiment: 1 | Episode: 485 | Score: -266.39 | Avg Score: -152.28 |\n",
"| Experiment: 1 | Episode: 486 | Score: -54.1 | Avg Score: -150.99 |\n",
"| Experiment: 1 | Episode: 487 | Score: -166.82 | Avg Score: -150.94 |\n",
"| Experiment: 1 | Episode: 488 | Score: -92.75 | Avg Score: -150.48 |\n",
"| Experiment: 1 | Episode: 489 | Score: -113.88 | Avg Score: -151.13 |\n",
"| Experiment: 1 | Episode: 490 | Score: -78.25 | Avg Score: -149.65 |\n",
"| Experiment: 1 | Episode: 491 | Score: -54.2 | Avg Score: -149.27 |\n",
"| Experiment: 1 | Episode: 492 | Score: -138.18 | Avg Score: -146.94 |\n",
"| Experiment: 1 | Episode: 493 | Score: -157.88 | Avg Score: -147.77 |\n",
"| Experiment: 1 | Episode: 494 | Score: -79.12 | Avg Score: -147.54 |\n",
"| Experiment: 1 | Episode: 495 | Score: -140.08 | Avg Score: -145.9 |\n",
"| Experiment: 1 | Episode: 496 | Score: -158.04 | Avg Score: -146.27 |\n",
"| Experiment: 1 | Episode: 497 | Score: -105.81 | Avg Score: -145.95 |\n",
"| Experiment: 1 | Episode: 498 | Score: -125.56 | Avg Score: -145.36 |\n",
"| Experiment: 1 | Episode: 499 | Score: -236.84 | Avg Score: -146.45 |\n",
"| Experiment: 1 | Episode: 500 | Score: -169.36 | Avg Score: -146.4 |\n",
"| Experiment: 1 | Episode: 501 | Score: -96.32 | Avg Score: -144.21 |\n",
"| Experiment: 1 | Episode: 502 | Score: -363.84 | Avg Score: -146.09 |\n",
"| Experiment: 1 | Episode: 503 | Score: -62.83 | Avg Score: -145.88 |\n",
"| Experiment: 1 | Episode: 504 | Score: -139.64 | Avg Score: -144.39 |\n",
"| Experiment: 1 | Episode: 505 | Score: -116.16 | Avg Score: -144.21 |\n",
"| Experiment: 1 | Episode: 506 | Score: -53.03 | Avg Score: -142.94 |\n",
"| Experiment: 1 | Episode: 507 | Score: -309.0 | Avg Score: -144.77 |\n",
"| Experiment: 1 | Episode: 508 | Score: -93.81 | Avg Score: -144.89 |\n",
"| Experiment: 1 | Episode: 509 | Score: -91.32 | Avg Score: -143.22 |\n",
"| Experiment: 1 | Episode: 510 | Score: -90.09 | Avg Score: -144.07 |\n",
"| Experiment: 1 | Episode: 511 | Score: -161.69 | Avg Score: -143.97 |\n",
"| Experiment: 1 | Episode: 512 | Score: -184.39 | Avg Score: -145.08 |\n",
"| Experiment: 1 | Episode: 513 | Score: -130.26 | Avg Score: -145.27 |\n",
"| Experiment: 1 | Episode: 514 | Score: -80.58 | Avg Score: -142.45 |\n",
"| Experiment: 1 | Episode: 515 | Score: 1.96 | Avg Score: -141.31 |\n",
"| Experiment: 1 | Episode: 516 | Score: -95.66 | Avg Score: -141.31 |\n",
"| Experiment: 1 | Episode: 517 | Score: -215.98 | Avg Score: -141.98 |\n",
"| Experiment: 1 | Episode: 518 | Score: -160.1 | Avg Score: -143.35 |\n",
"| Experiment: 1 | Episode: 519 | Score: -109.65 | Avg Score: -142.48 |\n",
"| Experiment: 1 | Episode: 520 | Score: -132.32 | Avg Score: -142.33 |\n",
"| Experiment: 1 | Episode: 521 | Score: -303.01 | Avg Score: -144.62 |\n",
"| Experiment: 1 | Episode: 522 | Score: -302.95 | Avg Score: -146.9 |\n",
"| Experiment: 1 | Episode: 523 | Score: -309.13 | Avg Score: -148.28 |\n",
"| Experiment: 1 | Episode: 524 | Score: -101.16 | Avg Score: -148.35 |\n",
"| Experiment: 1 | Episode: 525 | Score: -119.44 | Avg Score: -148.02 |\n",
"| Experiment: 1 | Episode: 526 | Score: -350.82 | Avg Score: -149.61 |\n",
"| Experiment: 1 | Episode: 527 | Score: -97.59 | Avg Score: -147.95 |\n",
"| Experiment: 1 | Episode: 528 | Score: -108.66 | Avg Score: -148.55 |\n",
"| Experiment: 1 | Episode: 529 | Score: -223.02 | Avg Score: -148.78 |\n",
"| Experiment: 1 | Episode: 530 | Score: -169.76 | Avg Score: -149.46 |\n",
"| Experiment: 1 | Episode: 531 | Score: -271.22 | Avg Score: -150.54 |\n",
"| Experiment: 1 | Episode: 532 | Score: -139.23 | Avg Score: -146.9 |\n",
"| Experiment: 1 | Episode: 533 | Score: -231.53 | Avg Score: -147.79 |\n",
"| Experiment: 1 | Episode: 534 | Score: -118.73 | Avg Score: -148.32 |\n",
"| Experiment: 1 | Episode: 535 | Score: -211.31 | Avg Score: -149.57 |\n",
"| Experiment: 1 | Episode: 536 | Score: -98.71 | Avg Score: -146.36 |\n",
"| Experiment: 1 | Episode: 537 | Score: -189.71 | Avg Score: -147.46 |\n",
"| Experiment: 1 | Episode: 538 | Score: -101.35 | Avg Score: -147.68 |\n",
"| Experiment: 1 | Episode: 539 | Score: -67.97 | Avg Score: -147.29 |\n",
"| Experiment: 1 | Episode: 540 | Score: -164.66 | Avg Score: -147.98 |\n",
"| Experiment: 1 | Episode: 541 | Score: -307.27 | Avg Score: -148.7 |\n",
"| Experiment: 1 | Episode: 542 | Score: -200.82 | Avg Score: -150.14 |\n",
"| Experiment: 1 | Episode: 543 | Score: -92.65 | Avg Score: -148.18 |\n",
"| Experiment: 1 | Episode: 544 | Score: -129.88 | Avg Score: -148.57 |\n",
"| Experiment: 1 | Episode: 545 | Score: -171.79 | Avg Score: -148.54 |\n",
"| Experiment: 1 | Episode: 546 | Score: -185.94 | Avg Score: -147.73 |\n",
"| Experiment: 1 | Episode: 547 | Score: -71.13 | Avg Score: -147.65 |\n",
"| Experiment: 1 | Episode: 548 | Score: -129.8 | Avg Score: -147.68 |\n",
"| Experiment: 1 | Episode: 549 | Score: -405.87 | Avg Score: -151.03 |\n",
"| Experiment: 1 | Episode: 550 | Score: -118.86 | Avg Score: -151.38 |\n",
"| Experiment: 1 | Episode: 551 | Score: -180.92 | Avg Score: -152.43 |\n",
"| Experiment: 1 | Episode: 552 | Score: -290.97 | Avg Score: -154.31 |\n",
"| Experiment: 1 | Episode: 553 | Score: -292.33 | Avg Score: -156.49 |\n",
"| Experiment: 1 | Episode: 554 | Score: -102.02 | Avg Score: -155.86 |\n",
"| Experiment: 1 | Episode: 555 | Score: -38.05 | Avg Score: -154.38 |\n",
"| Experiment: 1 | Episode: 556 | Score: -79.64 | Avg Score: -153.8 |\n",
"| Experiment: 1 | Episode: 557 | Score: -210.9 | Avg Score: -155.33 |\n",
"| Experiment: 1 | Episode: 558 | Score: -88.04 | Avg Score: -153.76 |\n",
"| Experiment: 1 | Episode: 559 | Score: -92.14 | Avg Score: -153.57 |\n",
"| Experiment: 1 | Episode: 560 | Score: -184.61 | Avg Score: -153.47 |\n",
"| Experiment: 1 | Episode: 561 | Score: -37.27 | Avg Score: -151.22 |\n",
"| Experiment: 1 | Episode: 562 | Score: -110.29 | Avg Score: -151.25 |\n",
"| Experiment: 1 | Episode: 563 | Score: -67.12 | Avg Score: -150.82 |\n",
"| Experiment: 1 | Episode: 564 | Score: -192.82 | Avg Score: -151.87 |\n",
"| Experiment: 1 | Episode: 565 | Score: -43.36 | Avg Score: -150.09 |\n",
"| Experiment: 1 | Episode: 566 | Score: -288.63 | Avg Score: -149.71 |\n",
"| Experiment: 1 | Episode: 567 | Score: -33.56 | Avg Score: -147.84 |\n",
"| Experiment: 1 | Episode: 568 | Score: -143.21 | Avg Score: -147.56 |\n",
"| Experiment: 1 | Episode: 569 | Score: -206.0 | Avg Score: -148.33 |\n",
"| Experiment: 1 | Episode: 570 | Score: -126.26 | Avg Score: -148.75 |\n",
"| Experiment: 1 | Episode: 571 | Score: -79.22 | Avg Score: -148.5 |\n",
"| Experiment: 1 | Episode: 572 | Score: -118.93 | Avg Score: -146.2 |\n",
"| Experiment: 1 | Episode: 573 | Score: -150.06 | Avg Score: -146.95 |\n",
"| Experiment: 1 | Episode: 574 | Score: -304.39 | Avg Score: -149.25 |\n",
"| Experiment: 1 | Episode: 575 | Score: -99.0 | Avg Score: -148.99 |\n",
"| Experiment: 1 | Episode: 576 | Score: -97.14 | Avg Score: -148.93 |\n",
"| Experiment: 1 | Episode: 577 | Score: -166.29 | Avg Score: -149.56 |\n",
"| Experiment: 1 | Episode: 578 | Score: -60.12 | Avg Score: -148.5 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 | Episode: 579 | Score: -87.13 | Avg Score: -147.05 |\n",
"| Experiment: 1 | Episode: 580 | Score: -389.18 | Avg Score: -150.23 |\n",
"| Experiment: 1 | Episode: 581 | Score: -147.27 | Avg Score: -151.16 |\n",
"| Experiment: 1 | Episode: 582 | Score: -168.49 | Avg Score: -151.69 |\n",
"| Experiment: 1 | Episode: 583 | Score: -102.54 | Avg Score: -151.27 |\n",
"| Experiment: 1 | Episode: 584 | Score: -171.84 | Avg Score: -152.66 |\n",
"| Experiment: 1 | Episode: 585 | Score: -13.23 | Avg Score: -150.13 |\n",
"| Experiment: 1 | Episode: 586 | Score: -237.65 | Avg Score: -151.96 |\n",
"| Experiment: 1 | Episode: 587 | Score: -78.06 | Avg Score: -151.07 |\n",
"| Experiment: 1 | Episode: 588 | Score: -218.41 | Avg Score: -152.33 |\n",
"| Experiment: 1 | Episode: 589 | Score: -357.46 | Avg Score: -154.77 |\n",
"| Experiment: 1 | Episode: 590 | Score: -100.73 | Avg Score: -154.99 |\n",
"| Experiment: 1 | Episode: 591 | Score: -122.87 | Avg Score: -155.68 |\n",
"| Experiment: 1 | Episode: 592 | Score: -124.05 | Avg Score: -155.54 |\n",
"| Experiment: 1 | Episode: 593 | Score: -98.57 | Avg Score: -154.94 |\n",
"| Experiment: 1 | Episode: 594 | Score: -92.34 | Avg Score: -155.07 |\n",
"| Experiment: 1 | Episode: 595 | Score: -190.46 | Avg Score: -155.58 |\n",
"| Experiment: 1 | Episode: 596 | Score: -199.97 | Avg Score: -156.0 |\n",
"| Experiment: 1 | Episode: 597 | Score: -297.18 | Avg Score: -157.91 |\n",
"| Experiment: 1 | Episode: 598 | Score: -66.48 | Avg Score: -157.32 |\n",
"| Experiment: 1 | Episode: 599 | Score: -148.24 | Avg Score: -156.43 |\n",
"| Experiment: 1 | Episode: 600 | Score: -252.29 | Avg Score: -157.26 |\n",
"| Experiment: 1 | Episode: 601 | Score: -126.74 | Avg Score: -157.57 |\n",
"| Experiment: 1 | Episode: 602 | Score: -198.86 | Avg Score: -155.92 |\n",
"| Experiment: 1 | Episode: 603 | Score: -115.61 | Avg Score: -156.45 |\n",
"| Experiment: 1 | Episode: 604 | Score: -83.06 | Avg Score: -155.88 |\n",
"| Experiment: 1 | Episode: 605 | Score: -24.64 | Avg Score: -154.97 |\n",
"| Experiment: 1 | Episode: 606 | Score: -87.03 | Avg Score: -155.31 |\n",
"| Experiment: 1 | Episode: 607 | Score: -89.76 | Avg Score: -153.11 |\n",
"| Experiment: 1 | Episode: 608 | Score: -168.76 | Avg Score: -153.86 |\n",
"| Experiment: 1 | Episode: 609 | Score: -107.57 | Avg Score: -154.03 |\n",
"| Experiment: 1 | Episode: 610 | Score: -70.24 | Avg Score: -153.83 |\n",
"| Experiment: 1 | Episode: 611 | Score: -125.72 | Avg Score: -153.47 |\n",
"| Experiment: 1 | Episode: 612 | Score: -126.12 | Avg Score: -152.88 |\n",
"| Experiment: 1 | Episode: 613 | Score: -118.93 | Avg Score: -152.77 |\n",
"| Experiment: 1 | Episode: 614 | Score: -90.32 | Avg Score: -152.87 |\n",
"| Experiment: 1 | Episode: 615 | Score: 30.9 | Avg Score: -152.58 |\n",
"| Experiment: 1 | Episode: 616 | Score: -90.14 | Avg Score: -152.52 |\n",
"| Experiment: 1 | Episode: 617 | Score: -240.4 | Avg Score: -152.77 |\n",
"| Experiment: 1 | Episode: 618 | Score: -73.53 | Avg Score: -151.9 |\n",
"| Experiment: 1 | Episode: 619 | Score: -178.51 | Avg Score: -152.59 |\n",
"| Experiment: 1 | Episode: 620 | Score: -272.5 | Avg Score: -153.99 |\n",
"| Experiment: 1 | Episode: 621 | Score: -62.14 | Avg Score: -151.58 |\n",
"| Experiment: 1 | Episode: 622 | Score: -95.25 | Avg Score: -149.51 |\n",
"| Experiment: 1 | Episode: 623 | Score: -165.96 | Avg Score: -148.08 |\n",
"| Experiment: 1 | Episode: 624 | Score: -250.25 | Avg Score: -149.57 |\n",
"| Experiment: 1 | Episode: 625 | Score: -111.98 | Avg Score: -149.49 |\n",
"| Experiment: 1 | Episode: 626 | Score: -60.22 | Avg Score: -146.59 |\n",
"| Experiment: 1 | Episode: 627 | Score: -91.97 | Avg Score: -146.53 |\n",
"| Experiment: 1 | Episode: 628 | Score: -83.57 | Avg Score: -146.28 |\n",
"| Experiment: 1 | Episode: 629 | Score: -208.74 | Avg Score: -146.14 |\n",
"| Experiment: 1 | Episode: 630 | Score: -112.98 | Avg Score: -145.57 |\n",
"| Experiment: 1 | Episode: 631 | Score: -57.06 | Avg Score: -143.43 |\n",
"| Experiment: 1 | Episode: 632 | Score: -333.93 | Avg Score: -145.37 |\n",
"| Experiment: 1 | Episode: 633 | Score: -234.14 | Avg Score: -145.4 |\n",
"| Experiment: 1 | Episode: 634 | Score: -71.68 | Avg Score: -144.93 |\n",
"| Experiment: 1 | Episode: 635 | Score: -74.45 | Avg Score: -143.56 |\n",
"| Experiment: 1 | Episode: 636 | Score: -77.44 | Avg Score: -143.35 |\n",
"| Experiment: 1 | Episode: 637 | Score: -84.44 | Avg Score: -142.29 |\n",
"| Experiment: 1 | Episode: 638 | Score: -95.63 | Avg Score: -142.24 |\n",
"| Experiment: 1 | Episode: 639 | Score: -77.52 | Avg Score: -142.33 |\n",
"| Experiment: 1 | Episode: 640 | Score: -90.44 | Avg Score: -141.59 |\n",
"| Experiment: 1 | Episode: 641 | Score: -112.71 | Avg Score: -139.65 |\n",
"| Experiment: 1 | Episode: 642 | Score: -94.24 | Avg Score: -138.58 |\n",
"| Experiment: 1 | Episode: 643 | Score: -69.78 | Avg Score: -138.35 |\n",
"| Experiment: 1 | Episode: 644 | Score: -205.34 | Avg Score: -139.11 |\n",
"| Experiment: 1 | Episode: 645 | Score: -110.24 | Avg Score: -138.49 |\n",
"| Experiment: 1 | Episode: 646 | Score: -61.83 | Avg Score: -137.25 |\n",
"| Experiment: 1 | Episode: 647 | Score: -102.2 | Avg Score: -137.56 |\n",
"| Experiment: 1 | Episode: 648 | Score: -139.67 | Avg Score: -137.66 |\n",
"| Experiment: 1 | Episode: 649 | Score: -49.71 | Avg Score: -134.1 |\n",
"| Experiment: 1 | Episode: 650 | Score: -188.46 | Avg Score: -134.79 |\n",
"| Experiment: 1 | Episode: 651 | Score: -274.06 | Avg Score: -135.72 |\n",
"| Experiment: 1 | Episode: 652 | Score: -87.58 | Avg Score: -133.69 |\n",
"| Experiment: 1 | Episode: 653 | Score: -38.51 | Avg Score: -131.15 |\n",
"| Experiment: 1 | Episode: 654 | Score: -94.2 | Avg Score: -131.07 |\n",
"| Experiment: 1 | Episode: 655 | Score: -97.77 | Avg Score: -131.67 |\n",
"| Experiment: 1 | Episode: 656 | Score: -95.78 | Avg Score: -131.83 |\n",
"| Experiment: 1 | Episode: 657 | Score: -255.87 | Avg Score: -132.28 |\n",
"| Experiment: 1 | Episode: 658 | Score: -103.64 | Avg Score: -132.44 |\n",
"| Experiment: 1 | Episode: 659 | Score: -92.2 | Avg Score: -132.44 |\n",
"| Experiment: 1 | Episode: 660 | Score: -68.97 | Avg Score: -131.28 |\n",
"| Experiment: 1 | Episode: 661 | Score: -235.42 | Avg Score: -133.26 |\n",
"| Experiment: 1 | Episode: 662 | Score: -397.53 | Avg Score: -136.14 |\n",
"| Experiment: 1 | Episode: 663 | Score: -129.11 | Avg Score: -136.76 |\n",
"| Experiment: 1 | Episode: 664 | Score: -70.28 | Avg Score: -135.53 |\n",
"| Experiment: 1 | Episode: 665 | Score: -311.87 | Avg Score: -138.22 |\n",
"| Experiment: 1 | Episode: 666 | Score: -131.1 | Avg Score: -136.64 |\n",
"| Experiment: 1 | Episode: 667 | Score: -31.31 | Avg Score: -136.62 |\n",
"| Experiment: 1 | Episode: 668 | Score: -66.5 | Avg Score: -135.85 |\n",
"| Experiment: 1 | Episode: 669 | Score: -202.42 | Avg Score: -135.81 |\n",
"| Experiment: 1 | Episode: 670 | Score: -113.7 | Avg Score: -135.69 |\n",
"| Experiment: 1 | Episode: 671 | Score: -53.09 | Avg Score: -135.43 |\n",
"| Experiment: 1 | Episode: 672 | Score: -135.84 | Avg Score: -135.6 |\n",
"| Experiment: 1 | Episode: 673 | Score: -212.08 | Avg Score: -136.22 |\n",
"| Experiment: 1 | Episode: 674 | Score: -79.59 | Avg Score: -133.97 |\n",
"| Experiment: 1 | Episode: 675 | Score: -97.61 | Avg Score: -133.96 |\n",
"| Experiment: 1 | Episode: 676 | Score: -99.24 | Avg Score: -133.98 |\n",
"| Experiment: 1 | Episode: 677 | Score: -216.76 | Avg Score: -134.48 |\n",
"| Experiment: 1 | Episode: 678 | Score: -62.61 | Avg Score: -134.51 |\n",
"| Experiment: 1 | Episode: 679 | Score: -184.61 | Avg Score: -135.48 |\n",
"| Experiment: 1 | Episode: 680 | Score: -75.75 | Avg Score: -132.35 |\n",
"| Experiment: 1 | Episode: 681 | Score: -67.47 | Avg Score: -131.55 |\n",
"| Experiment: 1 | Episode: 682 | Score: -205.32 | Avg Score: -131.92 |\n",
"| Experiment: 1 | Episode: 683 | Score: -98.62 | Avg Score: -131.88 |\n",
"| Experiment: 1 | Episode: 684 | Score: -6.04 | Avg Score: -130.22 |\n",
"| Experiment: 1 | Episode: 685 | Score: -114.22 | Avg Score: -131.23 |\n",
"| Experiment: 1 | Episode: 686 | Score: -241.43 | Avg Score: -131.27 |\n",
"| Experiment: 1 | Episode: 687 | Score: -109.76 | Avg Score: -131.58 |\n",
"| Experiment: 1 | Episode: 688 | Score: -118.12 | Avg Score: -130.58 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 | Episode: 689 | Score: -73.95 | Avg Score: -127.75 |\n",
"| Experiment: 1 | Episode: 690 | Score: -83.3 | Avg Score: -127.57 |\n",
"| Experiment: 1 | Episode: 691 | Score: -127.99 | Avg Score: -127.62 |\n",
"| Experiment: 1 | Episode: 692 | Score: -89.47 | Avg Score: -127.28 |\n",
"| Experiment: 1 | Episode: 693 | Score: -61.79 | Avg Score: -126.91 |\n",
"| Experiment: 1 | Episode: 694 | Score: -162.2 | Avg Score: -127.61 |\n",
"| Experiment: 1 | Episode: 695 | Score: -263.23 | Avg Score: -128.34 |\n",
"| Experiment: 1 | Episode: 696 | Score: -166.08 | Avg Score: -128.0 |\n",
"| Experiment: 1 | Episode: 697 | Score: -163.28 | Avg Score: -126.66 |\n",
"| Experiment: 1 | Episode: 698 | Score: -278.61 | Avg Score: -128.78 |\n",
"| Experiment: 1 | Episode: 699 | Score: -98.6 | Avg Score: -128.28 |\n",
"| Experiment: 1 | Episode: 700 | Score: -121.64 | Avg Score: -126.98 |\n",
"| Experiment: 1 | Episode: 701 | Score: -82.26 | Avg Score: -126.53 |\n",
"| Experiment: 1 | Episode: 702 | Score: -209.17 | Avg Score: -126.63 |\n",
"| Experiment: 1 | Episode: 703 | Score: -79.3 | Avg Score: -126.27 |\n",
"| Experiment: 1 | Episode: 704 | Score: -370.91 | Avg Score: -129.15 |\n",
"| Experiment: 1 | Episode: 705 | Score: -258.61 | Avg Score: -131.49 |\n",
"| Experiment: 1 | Episode: 706 | Score: -76.32 | Avg Score: -131.38 |\n",
"| Experiment: 1 | Episode: 707 | Score: -141.88 | Avg Score: -131.9 |\n",
"| Experiment: 1 | Episode: 708 | Score: -200.06 | Avg Score: -132.22 |\n",
"| Experiment: 1 | Episode: 709 | Score: -136.87 | Avg Score: -132.51 |\n",
"| Experiment: 1 | Episode: 710 | Score: -56.9 | Avg Score: -132.38 |\n",
"| Experiment: 1 | Episode: 711 | Score: -76.84 | Avg Score: -131.89 |\n",
"| Experiment: 1 | Episode: 712 | Score: -192.03 | Avg Score: -132.55 |\n",
"| Experiment: 1 | Episode: 713 | Score: -103.77 | Avg Score: -132.39 |\n",
"| Experiment: 1 | Episode: 714 | Score: -68.83 | Avg Score: -132.18 |\n",
"| Experiment: 1 | Episode: 715 | Score: -175.62 | Avg Score: -134.25 |\n",
"| Experiment: 1 | Episode: 716 | Score: -127.78 | Avg Score: -134.62 |\n",
"| Experiment: 1 | Episode: 717 | Score: -123.59 | Avg Score: -133.45 |\n",
"| Experiment: 1 | Episode: 718 | Score: -68.87 | Avg Score: -133.41 |\n",
"| Experiment: 1 | Episode: 719 | Score: -250.37 | Avg Score: -134.13 |\n",
"| Experiment: 1 | Episode: 720 | Score: -65.95 | Avg Score: -132.06 |\n",
"| Experiment: 1 | Episode: 721 | Score: -100.84 | Avg Score: -132.45 |\n",
"| Experiment: 1 | Episode: 722 | Score: -88.2 | Avg Score: -132.38 |\n",
"| Experiment: 1 | Episode: 723 | Score: -61.42 | Avg Score: -131.33 |\n",
"| Experiment: 1 | Episode: 724 | Score: -60.87 | Avg Score: -129.44 |\n",
"| Experiment: 1 | Episode: 725 | Score: -81.31 | Avg Score: -129.13 |\n",
"| Experiment: 1 | Episode: 726 | Score: -161.72 | Avg Score: -130.15 |\n",
"| Experiment: 1 | Episode: 727 | Score: -128.6 | Avg Score: -130.51 |\n",
"| Experiment: 1 | Episode: 728 | Score: -276.94 | Avg Score: -132.45 |\n",
"| Experiment: 1 | Episode: 729 | Score: -370.38 | Avg Score: -134.06 |\n",
"| Experiment: 1 | Episode: 730 | Score: -165.93 | Avg Score: -134.59 |\n",
"| Experiment: 1 | Episode: 731 | Score: -99.42 | Avg Score: -135.02 |\n",
"| Experiment: 1 | Episode: 732 | Score: -121.53 | Avg Score: -132.89 |\n",
"| Experiment: 1 | Episode: 733 | Score: -77.62 | Avg Score: -131.33 |\n",
"| Experiment: 1 | Episode: 734 | Score: -87.28 | Avg Score: -131.48 |\n",
"| Experiment: 1 | Episode: 735 | Score: -105.3 | Avg Score: -131.79 |\n",
"| Experiment: 1 | Episode: 736 | Score: -80.52 | Avg Score: -131.82 |\n",
"| Experiment: 1 | Episode: 737 | Score: -47.26 | Avg Score: -131.45 |\n",
"| Experiment: 1 | Episode: 738 | Score: -139.66 | Avg Score: -131.89 |\n",
"| Experiment: 1 | Episode: 739 | Score: -237.93 | Avg Score: -133.49 |\n",
"| Experiment: 1 | Episode: 740 | Score: -113.91 | Avg Score: -133.73 |\n",
"| Experiment: 1 | Episode: 741 | Score: -168.19 | Avg Score: -134.28 |\n",
"| Experiment: 1 | Episode: 742 | Score: -263.55 | Avg Score: -135.98 |\n",
"| Experiment: 1 | Episode: 743 | Score: -103.47 | Avg Score: -136.31 |\n",
"| Experiment: 1 | Episode: 744 | Score: -86.93 | Avg Score: -135.13 |\n",
"| Experiment: 1 | Episode: 745 | Score: -362.75 | Avg Score: -137.65 |\n",
"| Experiment: 1 | Episode: 746 | Score: -311.33 | Avg Score: -140.15 |\n",
"| Experiment: 1 | Episode: 747 | Score: -63.7 | Avg Score: -139.76 |\n",
"| Experiment: 1 | Episode: 748 | Score: -156.58 | Avg Score: -139.93 |\n",
"| Experiment: 1 | Episode: 749 | Score: -105.32 | Avg Score: -140.49 |\n",
"| Experiment: 1 | Episode: 750 | Score: -147.53 | Avg Score: -140.08 |\n",
"| Experiment: 1 | Episode: 751 | Score: -75.17 | Avg Score: -138.09 |\n",
"| Experiment: 1 | Episode: 752 | Score: -17.57 | Avg Score: -137.39 |\n",
"| Experiment: 1 | Episode: 753 | Score: -83.93 | Avg Score: -137.85 |\n",
"| Experiment: 1 | Episode: 754 | Score: -129.87 | Avg Score: -138.2 |\n",
"| Experiment: 1 | Episode: 755 | Score: -116.5 | Avg Score: -138.39 |\n",
"| Experiment: 1 | Episode: 756 | Score: -62.92 | Avg Score: -138.06 |\n",
"| Experiment: 1 | Episode: 757 | Score: -59.02 | Avg Score: -136.09 |\n",
"| Experiment: 1 | Episode: 758 | Score: -154.02 | Avg Score: -136.6 |\n",
"| Experiment: 1 | Episode: 759 | Score: -180.69 | Avg Score: -137.48 |\n",
"| Experiment: 1 | Episode: 760 | Score: -66.22 | Avg Score: -137.45 |\n",
"| Experiment: 1 | Episode: 761 | Score: -245.86 | Avg Score: -137.56 |\n",
"| Experiment: 1 | Episode: 762 | Score: -144.82 | Avg Score: -135.03 |\n",
"| Experiment: 1 | Episode: 763 | Score: -185.17 | Avg Score: -135.59 |\n",
"| Experiment: 1 | Episode: 764 | Score: -162.38 | Avg Score: -136.51 |\n",
"| Experiment: 1 | Episode: 765 | Score: -143.32 | Avg Score: -134.83 |\n",
"| Experiment: 1 | Episode: 766 | Score: -69.52 | Avg Score: -134.21 |\n",
"| Experiment: 1 | Episode: 767 | Score: -90.44 | Avg Score: -134.8 |\n",
"| Experiment: 1 | Episode: 768 | Score: -124.67 | Avg Score: -135.38 |\n",
"| Experiment: 1 | Episode: 769 | Score: -56.35 | Avg Score: -133.92 |\n",
"| Experiment: 1 | Episode: 770 | Score: -44.42 | Avg Score: -133.23 |\n",
"| Experiment: 1 | Episode: 771 | Score: -107.7 | Avg Score: -133.78 |\n",
"| Experiment: 1 | Episode: 772 | Score: -109.17 | Avg Score: -133.51 |\n",
"| Experiment: 1 | Episode: 773 | Score: -61.38 | Avg Score: -132.0 |\n",
"| Experiment: 1 | Episode: 774 | Score: -253.15 | Avg Score: -133.74 |\n",
"| Experiment: 1 | Episode: 775 | Score: -196.14 | Avg Score: -134.72 |\n",
"| Experiment: 1 | Episode: 776 | Score: -114.88 | Avg Score: -134.88 |\n",
"| Experiment: 1 | Episode: 777 | Score: -120.49 | Avg Score: -133.92 |\n",
"| Experiment: 1 | Episode: 778 | Score: -346.83 | Avg Score: -136.76 |\n",
"| Experiment: 1 | Episode: 779 | Score: -33.9 | Avg Score: -135.25 |\n",
"| Experiment: 1 | Episode: 780 | Score: -103.46 | Avg Score: -135.53 |\n",
"| Experiment: 1 | Episode: 781 | Score: -96.73 | Avg Score: -135.82 |\n",
"| Experiment: 1 | Episode: 782 | Score: -162.77 | Avg Score: -135.4 |\n",
"| Experiment: 1 | Episode: 783 | Score: -113.93 | Avg Score: -135.55 |\n",
"| Experiment: 1 | Episode: 784 | Score: -105.04 | Avg Score: -136.54 |\n",
"| Experiment: 1 | Episode: 785 | Score: -303.11 | Avg Score: -138.43 |\n",
"| Experiment: 1 | Episode: 786 | Score: -119.76 | Avg Score: -137.21 |\n",
"| Experiment: 1 | Episode: 787 | Score: -191.28 | Avg Score: -138.03 |\n",
"| Experiment: 1 | Episode: 788 | Score: -82.36 | Avg Score: -137.67 |\n",
"| Experiment: 1 | Episode: 789 | Score: -84.5 | Avg Score: -137.78 |\n",
"| Experiment: 1 | Episode: 790 | Score: -216.29 | Avg Score: -139.1 |\n",
"| Experiment: 1 | Episode: 791 | Score: -222.21 | Avg Score: -140.05 |\n",
"| Experiment: 1 | Episode: 792 | Score: -218.23 | Avg Score: -141.33 |\n",
"| Experiment: 1 | Episode: 793 | Score: -196.08 | Avg Score: -142.68 |\n",
"| Experiment: 1 | Episode: 794 | Score: -190.08 | Avg Score: -142.96 |\n",
"| Experiment: 1 | Episode: 795 | Score: -125.76 | Avg Score: -141.58 |\n",
"| Experiment: 1 | Episode: 796 | Score: -113.73 | Avg Score: -141.06 |\n",
"| Experiment: 1 | Episode: 797 | Score: -345.5 | Avg Score: -142.88 |\n",
"| Experiment: 1 | Episode: 798 | Score: -49.57 | Avg Score: -140.59 |\n",
"| Experiment: 1 | Episode: 799 | Score: -198.15 | Avg Score: -141.59 |\n",
"| Experiment: 1 | Episode: 800 | Score: -77.42 | Avg Score: -141.14 |\n",
"| Experiment: 1 | Episode: 801 | Score: -92.94 | Avg Score: -141.25 |\n",
"| Experiment: 1 | Episode: 802 | Score: -66.8 | Avg Score: -139.83 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 | Episode: 803 | Score: -141.51 | Avg Score: -140.45 |\n",
"| Experiment: 1 | Episode: 804 | Score: -238.88 | Avg Score: -139.13 |\n",
"| Experiment: 1 | Episode: 805 | Score: -106.16 | Avg Score: -137.6 |\n",
"| Experiment: 1 | Episode: 806 | Score: -80.38 | Avg Score: -137.64 |\n",
"| Experiment: 1 | Episode: 807 | Score: -76.41 | Avg Score: -136.99 |\n",
"| Experiment: 1 | Episode: 808 | Score: -217.74 | Avg Score: -137.17 |\n",
"| Experiment: 1 | Episode: 809 | Score: -90.36 | Avg Score: -136.7 |\n",
"| Experiment: 1 | Episode: 810 | Score: -125.83 | Avg Score: -137.39 |\n",
"| Experiment: 1 | Episode: 811 | Score: -112.98 | Avg Score: -137.75 |\n",
"| Experiment: 1 | Episode: 812 | Score: -91.25 | Avg Score: -136.74 |\n",
"| Experiment: 1 | Episode: 813 | Score: 13.49 | Avg Score: -135.57 |\n",
"| Experiment: 1 | Episode: 814 | Score: -198.14 | Avg Score: -136.87 |\n",
"| Experiment: 1 | Episode: 815 | Score: -72.81 | Avg Score: -135.84 |\n",
"| Experiment: 1 | Episode: 816 | Score: -122.1 | Avg Score: -135.78 |\n",
"| Experiment: 1 | Episode: 817 | Score: -85.32 | Avg Score: -135.4 |\n",
"| Experiment: 1 | Episode: 818 | Score: -207.29 | Avg Score: -136.78 |\n",
"| Experiment: 1 | Episode: 819 | Score: -85.97 | Avg Score: -135.14 |\n",
"| Experiment: 1 | Episode: 820 | Score: -129.98 | Avg Score: -135.78 |\n",
"| Experiment: 1 | Episode: 821 | Score: -122.26 | Avg Score: -135.99 |\n",
"| Experiment: 1 | Episode: 822 | Score: -35.7 | Avg Score: -135.47 |\n",
"| Experiment: 1 | Episode: 823 | Score: -235.47 | Avg Score: -137.21 |\n",
"| Experiment: 1 | Episode: 824 | Score: -111.98 | Avg Score: -137.72 |\n",
"| Experiment: 1 | Episode: 825 | Score: -139.61 | Avg Score: -138.3 |\n",
"| Experiment: 1 | Episode: 826 | Score: -24.96 | Avg Score: -136.93 |\n",
"| Experiment: 1 | Episode: 827 | Score: -69.79 | Avg Score: -136.35 |\n",
"| Experiment: 1 | Episode: 828 | Score: -96.21 | Avg Score: -134.54 |\n",
"| Experiment: 1 | Episode: 829 | Score: -173.6 | Avg Score: -132.57 |\n",
"| Experiment: 1 | Episode: 830 | Score: -110.2 | Avg Score: -132.01 |\n",
"| Experiment: 1 | Episode: 831 | Score: -242.0 | Avg Score: -133.44 |\n",
"| Experiment: 1 | Episode: 832 | Score: -114.92 | Avg Score: -133.37 |\n",
"| Experiment: 1 | Episode: 833 | Score: -315.73 | Avg Score: -135.75 |\n",
"| Experiment: 1 | Episode: 834 | Score: -148.33 | Avg Score: -136.37 |\n",
"| Experiment: 1 | Episode: 835 | Score: -266.54 | Avg Score: -137.98 |\n",
"| Experiment: 1 | Episode: 836 | Score: -96.59 | Avg Score: -138.14 |\n",
"| Experiment: 1 | Episode: 837 | Score: -116.48 | Avg Score: -138.83 |\n",
"| Experiment: 1 | Episode: 838 | Score: -240.64 | Avg Score: -139.84 |\n",
"| Experiment: 1 | Episode: 839 | Score: -147.44 | Avg Score: -138.94 |\n",
"| Experiment: 1 | Episode: 840 | Score: -190.84 | Avg Score: -139.7 |\n",
"| Experiment: 1 | Episode: 841 | Score: -34.22 | Avg Score: -138.36 |\n",
"| Experiment: 1 | Episode: 842 | Score: -73.48 | Avg Score: -136.46 |\n",
"| Experiment: 1 | Episode: 843 | Score: -104.08 | Avg Score: -136.47 |\n",
"| Experiment: 1 | Episode: 844 | Score: -96.58 | Avg Score: -136.57 |\n",
"| Experiment: 1 | Episode: 845 | Score: -129.25 | Avg Score: -134.23 |\n",
"| Experiment: 1 | Episode: 846 | Score: -128.93 | Avg Score: -132.41 |\n",
"| Experiment: 1 | Episode: 847 | Score: -278.55 | Avg Score: -134.56 |\n",
"| Experiment: 1 | Episode: 848 | Score: -77.69 | Avg Score: -133.77 |\n",
"| Experiment: 1 | Episode: 849 | Score: -357.05 | Avg Score: -136.28 |\n",
"| Experiment: 1 | Episode: 850 | Score: -53.26 | Avg Score: -135.34 |\n",
"| Experiment: 1 | Episode: 851 | Score: -68.44 | Avg Score: -135.27 |\n",
"| Experiment: 1 | Episode: 852 | Score: -62.94 | Avg Score: -135.73 |\n",
"| Experiment: 1 | Episode: 853 | Score: -267.21 | Avg Score: -137.56 |\n",
"| Experiment: 1 | Episode: 854 | Score: -186.31 | Avg Score: -138.13 |\n",
"| Experiment: 1 | Episode: 855 | Score: -69.89 | Avg Score: -137.66 |\n",
"| Experiment: 1 | Episode: 856 | Score: -267.66 | Avg Score: -139.71 |\n",
"| Experiment: 1 | Episode: 857 | Score: -47.09 | Avg Score: -139.59 |\n",
"| Experiment: 1 | Episode: 858 | Score: -69.56 | Avg Score: -138.74 |\n",
"| Experiment: 1 | Episode: 859 | Score: -87.2 | Avg Score: -137.81 |\n",
"| Experiment: 1 | Episode: 860 | Score: -61.63 | Avg Score: -137.76 |\n",
"| Experiment: 1 | Episode: 861 | Score: -96.34 | Avg Score: -136.27 |\n",
"| Experiment: 1 | Episode: 862 | Score: -282.6 | Avg Score: -137.64 |\n",
"| Experiment: 1 | Episode: 863 | Score: -275.35 | Avg Score: -138.55 |\n",
"| Experiment: 1 | Episode: 864 | Score: -249.22 | Avg Score: -139.41 |\n",
"| Experiment: 1 | Episode: 865 | Score: -320.04 | Avg Score: -141.18 |\n",
"| Experiment: 1 | Episode: 866 | Score: -111.72 | Avg Score: -141.6 |\n",
"| Experiment: 1 | Episode: 867 | Score: -300.03 | Avg Score: -143.7 |\n",
"| Experiment: 1 | Episode: 868 | Score: -139.51 | Avg Score: -143.85 |\n",
"| Experiment: 1 | Episode: 869 | Score: -212.84 | Avg Score: -145.41 |\n",
"| Experiment: 1 | Episode: 870 | Score: -131.66 | Avg Score: -146.29 |\n",
"| Experiment: 1 | Episode: 871 | Score: -40.88 | Avg Score: -145.62 |\n",
"| Experiment: 1 | Episode: 872 | Score: -115.32 | Avg Score: -145.68 |\n",
"| Experiment: 1 | Episode: 873 | Score: -50.72 | Avg Score: -145.57 |\n",
"| Experiment: 1 | Episode: 874 | Score: -260.64 | Avg Score: -145.65 |\n",
"| Experiment: 1 | Episode: 875 | Score: -127.04 | Avg Score: -144.96 |\n",
"| Experiment: 1 | Episode: 876 | Score: -94.05 | Avg Score: -144.75 |\n",
"| Experiment: 1 | Episode: 877 | Score: -153.76 | Avg Score: -145.08 |\n",
"| Experiment: 1 | Episode: 878 | Score: -113.67 | Avg Score: -142.75 |\n",
"| Experiment: 1 | Episode: 879 | Score: -81.12 | Avg Score: -143.22 |\n",
"| Experiment: 1 | Episode: 880 | Score: -99.89 | Avg Score: -143.19 |\n",
"| Experiment: 1 | Episode: 881 | Score: -166.3 | Avg Score: -143.88 |\n",
"| Experiment: 1 | Episode: 882 | Score: -214.52 | Avg Score: -144.4 |\n",
"| Experiment: 1 | Episode: 883 | Score: -162.87 | Avg Score: -144.89 |\n",
"| Experiment: 1 | Episode: 884 | Score: -83.63 | Avg Score: -144.67 |\n",
"| Experiment: 1 | Episode: 885 | Score: -105.67 | Avg Score: -142.7 |\n",
"| Experiment: 1 | Episode: 886 | Score: -144.72 | Avg Score: -142.95 |\n",
"| Experiment: 1 | Episode: 887 | Score: -47.18 | Avg Score: -141.51 |\n",
"| Experiment: 1 | Episode: 888 | Score: -129.05 | Avg Score: -141.98 |\n",
"| Experiment: 1 | Episode: 889 | Score: -74.05 | Avg Score: -141.87 |\n",
"| Experiment: 1 | Episode: 890 | Score: -96.63 | Avg Score: -140.67 |\n",
"| Experiment: 1 | Episode: 891 | Score: -82.78 | Avg Score: -139.28 |\n",
"| Experiment: 1 | Episode: 892 | Score: -168.61 | Avg Score: -138.78 |\n",
"| Experiment: 1 | Episode: 893 | Score: -93.61 | Avg Score: -137.76 |\n",
"| Experiment: 1 | Episode: 894 | Score: -6.69 | Avg Score: -135.93 |\n",
"| Experiment: 1 | Episode: 895 | Score: -300.82 | Avg Score: -137.68 |\n",
"| Experiment: 1 | Episode: 896 | Score: -109.28 | Avg Score: -137.63 |\n",
"| Experiment: 1 | Episode: 897 | Score: -93.63 | Avg Score: -135.11 |\n",
"| Experiment: 1 | Episode: 898 | Score: -257.97 | Avg Score: -137.2 |\n",
"| Experiment: 1 | Episode: 899 | Score: -119.02 | Avg Score: -136.41 |\n",
"| Experiment: 1 | Episode: 900 | Score: -135.39 | Avg Score: -136.99 |\n",
"| Experiment: 1 | Episode: 901 | Score: -51.43 | Avg Score: -136.57 |\n",
"| Experiment: 1 | Episode: 902 | Score: -52.14 | Avg Score: -136.42 |\n",
"| Experiment: 1 | Episode: 903 | Score: -158.04 | Avg Score: -136.59 |\n",
"| Experiment: 1 | Episode: 904 | Score: -106.35 | Avg Score: -135.26 |\n",
"| Experiment: 1 | Episode: 905 | Score: -172.04 | Avg Score: -135.92 |\n",
"| Experiment: 1 | Episode: 906 | Score: -131.73 | Avg Score: -136.44 |\n",
"| Experiment: 1 | Episode: 907 | Score: -51.05 | Avg Score: -136.18 |\n",
"| Experiment: 1 | Episode: 908 | Score: -79.95 | Avg Score: -134.8 |\n",
"| Experiment: 1 | Episode: 909 | Score: -8.88 | Avg Score: -133.99 |\n",
"| Experiment: 1 | Episode: 910 | Score: -243.35 | Avg Score: -135.16 |\n",
"| Experiment: 1 | Episode: 911 | Score: -122.13 | Avg Score: -135.26 |\n",
"| Experiment: 1 | Episode: 912 | Score: -93.55 | Avg Score: -135.28 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 | Episode: 913 | Score: -78.54 | Avg Score: -136.2 |\n",
"| Experiment: 1 | Episode: 914 | Score: -171.5 | Avg Score: -135.93 |\n",
"| Experiment: 1 | Episode: 915 | Score: -151.96 | Avg Score: -136.72 |\n",
"| Experiment: 1 | Episode: 916 | Score: -71.94 | Avg Score: -136.22 |\n",
"| Experiment: 1 | Episode: 917 | Score: -77.47 | Avg Score: -136.14 |\n",
"| Experiment: 1 | Episode: 918 | Score: -231.29 | Avg Score: -136.38 |\n",
"| Experiment: 1 | Episode: 919 | Score: -121.87 | Avg Score: -136.74 |\n",
"| Experiment: 1 | Episode: 920 | Score: -342.17 | Avg Score: -138.87 |\n",
"| Experiment: 1 | Episode: 921 | Score: -114.39 | Avg Score: -138.79 |\n",
"| Experiment: 1 | Episode: 922 | Score: -14.53 | Avg Score: -138.57 |\n",
"| Experiment: 1 | Episode: 923 | Score: -93.83 | Avg Score: -137.16 |\n",
"| Experiment: 1 | Episode: 924 | Score: -225.26 | Avg Score: -138.29 |\n",
"| Experiment: 1 | Episode: 925 | Score: -76.84 | Avg Score: -137.66 |\n",
"| Experiment: 1 | Episode: 926 | Score: -177.18 | Avg Score: -139.19 |\n",
"| Experiment: 1 | Episode: 927 | Score: -199.62 | Avg Score: -140.48 |\n",
"| Experiment: 1 | Episode: 928 | Score: -159.57 | Avg Score: -141.12 |\n",
"| Experiment: 1 | Episode: 929 | Score: -44.17 | Avg Score: -139.82 |\n",
"| Experiment: 1 | Episode: 930 | Score: -80.0 | Avg Score: -139.52 |\n",
"| Experiment: 1 | Episode: 931 | Score: -209.93 | Avg Score: -139.2 |\n",
"| Experiment: 1 | Episode: 932 | Score: 99.54 | Avg Score: -137.06 |\n",
"| Experiment: 1 | Episode: 933 | Score: -104.48 | Avg Score: -134.94 |\n",
"| Experiment: 1 | Episode: 934 | Score: -95.11 | Avg Score: -134.41 |\n",
"| Experiment: 1 | Episode: 935 | Score: -44.71 | Avg Score: -132.19 |\n",
"| Experiment: 1 | Episode: 936 | Score: -13.09 | Avg Score: -131.36 |\n",
"| Experiment: 1 | Episode: 937 | Score: -85.63 | Avg Score: -131.05 |\n",
"| Experiment: 1 | Episode: 938 | Score: -122.59 | Avg Score: -129.87 |\n",
"| Experiment: 1 | Episode: 939 | Score: -55.33 | Avg Score: -128.95 |\n",
"| Experiment: 1 | Episode: 940 | Score: -102.74 | Avg Score: -128.07 |\n",
"| Experiment: 1 | Episode: 941 | Score: -120.85 | Avg Score: -128.93 |\n",
"| Experiment: 1 | Episode: 942 | Score: -75.3 | Avg Score: -128.95 |\n",
"| Experiment: 1 | Episode: 943 | Score: -61.15 | Avg Score: -128.52 |\n",
"| Experiment: 1 | Episode: 944 | Score: -39.86 | Avg Score: -127.96 |\n",
"| Experiment: 1 | Episode: 945 | Score: -147.82 | Avg Score: -128.14 |\n",
"| Experiment: 1 | Episode: 946 | Score: -45.86 | Avg Score: -127.31 |\n",
"| Experiment: 1 | Episode: 947 | Score: -98.93 | Avg Score: -125.51 |\n",
"| Experiment: 1 | Episode: 948 | Score: -221.37 | Avg Score: -126.95 |\n",
"| Experiment: 1 | Episode: 949 | Score: -72.59 | Avg Score: -124.11 |\n",
"| Experiment: 1 | Episode: 950 | Score: -308.32 | Avg Score: -126.66 |\n",
"| Experiment: 1 | Episode: 951 | Score: -489.34 | Avg Score: -130.87 |\n",
"| Experiment: 1 | Episode: 952 | Score: -98.46 | Avg Score: -131.22 |\n",
"| Experiment: 1 | Episode: 953 | Score: -95.52 | Avg Score: -129.5 |\n",
"| Experiment: 1 | Episode: 954 | Score: -91.19 | Avg Score: -128.55 |\n",
"| Experiment: 1 | Episode: 955 | Score: -6.21 | Avg Score: -127.92 |\n",
"| Experiment: 1 | Episode: 956 | Score: -187.28 | Avg Score: -127.11 |\n",
"| Experiment: 1 | Episode: 957 | Score: -207.22 | Avg Score: -128.71 |\n",
"| Experiment: 1 | Episode: 958 | Score: -213.69 | Avg Score: -130.15 |\n",
"| Experiment: 1 | Episode: 959 | Score: -181.92 | Avg Score: -131.1 |\n",
"| Experiment: 1 | Episode: 960 | Score: -261.77 | Avg Score: -133.1 |\n",
"| Experiment: 1 | Episode: 961 | Score: -35.42 | Avg Score: -132.49 |\n",
"| Experiment: 1 | Episode: 962 | Score: -39.8 | Avg Score: -130.07 |\n",
"| Experiment: 1 | Episode: 963 | Score: 49.54 | Avg Score: -126.82 |\n",
"| Experiment: 1 | Episode: 964 | Score: -127.52 | Avg Score: -125.6 |\n",
"| Experiment: 1 | Episode: 965 | Score: -101.05 | Avg Score: -123.41 |\n",
"| Experiment: 1 | Episode: 966 | Score: -162.17 | Avg Score: -123.92 |\n",
"| Experiment: 1 | Episode: 967 | Score: -6.72 | Avg Score: -120.98 |\n",
"| Experiment: 1 | Episode: 968 | Score: -122.45 | Avg Score: -120.81 |\n",
"| Experiment: 1 | Episode: 969 | Score: -115.04 | Avg Score: -119.83 |\n",
"| Experiment: 1 | Episode: 970 | Score: -82.65 | Avg Score: -119.34 |\n",
"| Experiment: 1 | Episode: 971 | Score: -60.49 | Avg Score: -119.54 |\n",
"| Experiment: 1 | Episode: 972 | Score: -122.82 | Avg Score: -119.61 |\n",
"| Experiment: 1 | Episode: 973 | Score: -87.69 | Avg Score: -119.98 |\n",
"| Experiment: 1 | Episode: 974 | Score: -89.22 | Avg Score: -118.27 |\n",
"| Experiment: 1 | Episode: 975 | Score: -116.01 | Avg Score: -118.16 |\n",
"| Experiment: 1 | Episode: 976 | Score: -24.57 | Avg Score: -117.46 |\n",
"| Experiment: 1 | Episode: 977 | Score: -141.83 | Avg Score: -117.35 |\n",
"| Experiment: 1 | Episode: 978 | Score: -211.67 | Avg Score: -118.33 |\n",
"| Experiment: 1 | Episode: 979 | Score: -144.46 | Avg Score: -118.96 |\n",
"| Experiment: 1 | Episode: 980 | Score: -371.37 | Avg Score: -121.67 |\n",
"| Experiment: 1 | Episode: 981 | Score: -89.43 | Avg Score: -120.9 |\n",
"| Experiment: 1 | Episode: 982 | Score: -126.78 | Avg Score: -120.03 |\n",
"| Experiment: 1 | Episode: 983 | Score: -252.45 | Avg Score: -120.92 |\n",
"| Experiment: 1 | Episode: 984 | Score: -205.28 | Avg Score: -122.14 |\n",
"| Experiment: 1 | Episode: 985 | Score: -218.89 | Avg Score: -123.27 |\n",
"| Experiment: 1 | Episode: 986 | Score: -167.84 | Avg Score: -123.5 |\n",
"| Experiment: 1 | Episode: 987 | Score: -82.03 | Avg Score: -123.85 |\n",
"| Experiment: 1 | Episode: 988 | Score: -75.32 | Avg Score: -123.31 |\n",
"| Experiment: 1 | Episode: 989 | Score: -160.51 | Avg Score: -124.18 |\n",
"| Experiment: 1 | Episode: 990 | Score: -94.27 | Avg Score: -124.15 |\n",
"| Experiment: 1 | Episode: 991 | Score: -288.38 | Avg Score: -126.21 |\n",
"| Experiment: 1 | Episode: 992 | Score: -279.86 | Avg Score: -127.32 |\n",
"| Experiment: 1 | Episode: 993 | Score: -265.44 | Avg Score: -129.04 |\n",
"| Experiment: 1 | Episode: 994 | Score: -126.68 | Avg Score: -130.24 |\n",
"| Experiment: 1 | Episode: 995 | Score: -166.41 | Avg Score: -128.9 |\n",
"| Experiment: 1 | Episode: 996 | Score: -242.22 | Avg Score: -130.23 |\n",
"| Experiment: 1 | Episode: 997 | Score: -121.15 | Avg Score: -130.5 |\n",
"| Experiment: 1 | Episode: 998 | Score: -69.3 | Avg Score: -128.62 |\n",
"| Experiment: 1 | Episode: 999 | Score: -288.77 | Avg Score: -130.31 |\n",
"| Experiment: 1 | Episode: 1000 | Score: -121.78 | Avg Score: -130.18 |\n",
"| Experiment: 1 | Episode: 1001 | Score: 26.53 | Avg Score: -129.4 |\n",
"| Experiment: 1 | Episode: 1002 | Score: -27.55 | Avg Score: -129.15 |\n",
"| Experiment: 1 | Episode: 1003 | Score: -93.91 | Avg Score: -128.51 |\n",
"| Experiment: 1 | Episode: 1004 | Score: -68.76 | Avg Score: -128.13 |\n",
"| Experiment: 1 | Episode: 1005 | Score: -100.82 | Avg Score: -127.42 |\n",
"| Experiment: 1 | Episode: 1006 | Score: -64.25 | Avg Score: -126.75 |\n",
"| Experiment: 1 | Episode: 1007 | Score: -117.95 | Avg Score: -127.42 |\n",
"| Experiment: 1 | Episode: 1008 | Score: -181.98 | Avg Score: -128.44 |\n",
"| Experiment: 1 | Episode: 1009 | Score: -108.63 | Avg Score: -129.43 |\n",
"| Experiment: 1 | Episode: 1010 | Score: -109.9 | Avg Score: -128.1 |\n",
"| Experiment: 1 | Episode: 1011 | Score: -225.39 | Avg Score: -129.13 |\n",
"| Experiment: 1 | Episode: 1012 | Score: -76.37 | Avg Score: -128.96 |\n",
"| Experiment: 1 | Episode: 1013 | Score: -116.1 | Avg Score: -129.34 |\n",
"| Experiment: 1 | Episode: 1014 | Score: -53.32 | Avg Score: -128.15 |\n",
"| Experiment: 1 | Episode: 1015 | Score: -38.95 | Avg Score: -127.02 |\n",
"| Experiment: 1 | Episode: 1016 | Score: -300.01 | Avg Score: -129.3 |\n",
"| Experiment: 1 | Episode: 1017 | Score: -115.38 | Avg Score: -129.68 |\n",
"| Experiment: 1 | Episode: 1018 | Score: -119.4 | Avg Score: -128.56 |\n",
"| Experiment: 1 | Episode: 1019 | Score: -88.58 | Avg Score: -128.23 |\n",
"| Experiment: 1 | Episode: 1020 | Score: -162.9 | Avg Score: -126.44 |\n",
"| Experiment: 1 | Episode: 1021 | Score: -113.74 | Avg Score: -126.43 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 | Episode: 1022 | Score: -145.91 | Avg Score: -127.75 |\n",
"| Experiment: 1 | Episode: 1023 | Score: -122.22 | Avg Score: -128.03 |\n",
"| Experiment: 1 | Episode: 1024 | Score: -77.27 | Avg Score: -126.55 |\n",
"| Experiment: 1 | Episode: 1025 | Score: -88.16 | Avg Score: -126.66 |\n",
"| Experiment: 1 | Episode: 1026 | Score: -106.02 | Avg Score: -125.95 |\n",
"| Experiment: 1 | Episode: 1027 | Score: -290.67 | Avg Score: -126.86 |\n",
"| Experiment: 1 | Episode: 1028 | Score: -69.11 | Avg Score: -125.96 |\n",
"| Experiment: 1 | Episode: 1029 | Score: -38.09 | Avg Score: -125.9 |\n",
"| Experiment: 1 | Episode: 1030 | Score: -82.33 | Avg Score: -125.92 |\n",
"| Experiment: 1 | Episode: 1031 | Score: -106.74 | Avg Score: -124.89 |\n",
"| Experiment: 1 | Episode: 1032 | Score: -310.71 | Avg Score: -128.99 |\n",
"| Experiment: 1 | Episode: 1033 | Score: -102.14 | Avg Score: -128.97 |\n",
"| Experiment: 1 | Episode: 1034 | Score: -58.19 | Avg Score: -128.6 |\n",
"| Experiment: 1 | Episode: 1035 | Score: -84.2 | Avg Score: -128.99 |\n",
"| Experiment: 1 | Episode: 1036 | Score: -83.13 | Avg Score: -129.69 |\n",
"| Experiment: 1 | Episode: 1037 | Score: -222.53 | Avg Score: -131.06 |\n",
"| Experiment: 1 | Episode: 1038 | Score: -201.11 | Avg Score: -131.85 |\n",
"| Experiment: 1 | Episode: 1039 | Score: -138.59 | Avg Score: -132.68 |\n",
"| Experiment: 1 | Episode: 1040 | Score: -69.66 | Avg Score: -132.35 |\n",
"| Experiment: 1 | Episode: 1041 | Score: -121.93 | Avg Score: -132.36 |\n",
"| Experiment: 1 | Episode: 1042 | Score: -141.1 | Avg Score: -133.02 |\n",
"| Experiment: 1 | Episode: 1043 | Score: -93.53 | Avg Score: -133.34 |\n",
"| Experiment: 1 | Episode: 1044 | Score: -61.58 | Avg Score: -133.56 |\n",
"| Experiment: 1 | Episode: 1045 | Score: -56.74 | Avg Score: -132.65 |\n",
"| Experiment: 1 | Episode: 1046 | Score: -133.32 | Avg Score: -133.52 |\n",
"| Experiment: 1 | Episode: 1047 | Score: -117.55 | Avg Score: -133.71 |\n",
"| Experiment: 1 | Episode: 1048 | Score: -106.64 | Avg Score: -132.56 |\n",
"| Experiment: 1 | Episode: 1049 | Score: -81.88 | Avg Score: -132.65 |\n",
"| Experiment: 1 | Episode: 1050 | Score: -92.39 | Avg Score: -130.5 |\n",
"| Experiment: 1 | Episode: 1051 | Score: -86.02 | Avg Score: -126.46 |\n",
"| Experiment: 1 | Episode: 1052 | Score: -237.14 | Avg Score: -127.85 |\n",
"| Experiment: 1 | Episode: 1053 | Score: -183.67 | Avg Score: -128.73 |\n",
"| Experiment: 1 | Episode: 1054 | Score: -77.8 | Avg Score: -128.6 |\n",
"| Experiment: 1 | Episode: 1055 | Score: -99.42 | Avg Score: -129.53 |\n",
"| Experiment: 1 | Episode: 1056 | Score: -118.69 | Avg Score: -128.84 |\n",
"| Experiment: 1 | Episode: 1057 | Score: -97.75 | Avg Score: -127.75 |\n",
"| Experiment: 1 | Episode: 1058 | Score: -68.37 | Avg Score: -126.3 |\n",
"| Experiment: 1 | Episode: 1059 | Score: -79.42 | Avg Score: -125.27 |\n",
"| Experiment: 1 | Episode: 1060 | Score: -161.59 | Avg Score: -124.27 |\n",
"| Experiment: 1 | Episode: 1061 | Score: -224.0 | Avg Score: -126.15 |\n",
"| Experiment: 1 | Episode: 1062 | Score: -174.25 | Avg Score: -127.5 |\n",
"| Experiment: 1 | Episode: 1063 | Score: -71.87 | Avg Score: -128.71 |\n",
"| Experiment: 1 | Episode: 1064 | Score: -107.68 | Avg Score: -128.51 |\n",
"| Experiment: 1 | Episode: 1065 | Score: -94.32 | Avg Score: -128.45 |\n",
"| Experiment: 1 | Episode: 1066 | Score: -154.18 | Avg Score: -128.37 |\n",
"| Experiment: 1 | Episode: 1067 | Score: -57.72 | Avg Score: -128.88 |\n",
"| Experiment: 1 | Episode: 1068 | Score: -120.47 | Avg Score: -128.86 |\n",
"| Experiment: 1 | Episode: 1069 | Score: -35.69 | Avg Score: -128.06 |\n",
"| Experiment: 1 | Episode: 1070 | Score: -115.25 | Avg Score: -128.39 |\n",
"| Experiment: 1 | Episode: 1071 | Score: -204.09 | Avg Score: -129.83 |\n",
"| Experiment: 1 | Episode: 1072 | Score: -80.77 | Avg Score: -129.41 |\n",
"| Experiment: 1 | Episode: 1073 | Score: -131.26 | Avg Score: -129.84 |\n",
"| Experiment: 1 | Episode: 1074 | Score: -119.57 | Avg Score: -130.14 |\n",
"| Experiment: 1 | Episode: 1075 | Score: -121.7 | Avg Score: -130.2 |\n",
"| Experiment: 1 | Episode: 1076 | Score: -177.26 | Avg Score: -131.73 |\n",
"| Experiment: 1 | Episode: 1077 | Score: -138.43 | Avg Score: -131.69 |\n",
"| Experiment: 1 | Episode: 1078 | Score: -143.84 | Avg Score: -131.02 |\n",
"| Experiment: 1 | Episode: 1079 | Score: -107.58 | Avg Score: -130.65 |\n",
"| Experiment: 1 | Episode: 1080 | Score: -15.59 | Avg Score: -127.09 |\n",
"| Experiment: 1 | Episode: 1081 | Score: -125.29 | Avg Score: -127.45 |\n",
"| Experiment: 1 | Episode: 1082 | Score: -184.98 | Avg Score: -128.03 |\n",
"| Experiment: 1 | Episode: 1083 | Score: -102.38 | Avg Score: -126.53 |\n",
"| Experiment: 1 | Episode: 1084 | Score: -121.27 | Avg Score: -125.69 |\n",
"| Experiment: 1 | Episode: 1085 | Score: -262.23 | Avg Score: -126.12 |\n",
"| Experiment: 1 | Episode: 1086 | Score: -122.01 | Avg Score: -125.66 |\n",
"| Experiment: 1 | Episode: 1087 | Score: -112.84 | Avg Score: -125.97 |\n",
"| Experiment: 1 | Episode: 1088 | Score: -196.11 | Avg Score: -127.18 |\n",
"| Experiment: 1 | Episode: 1089 | Score: -57.22 | Avg Score: -126.15 |\n",
"| Experiment: 1 | Episode: 1090 | Score: -134.65 | Avg Score: -126.55 |\n",
"| Experiment: 1 | Episode: 1091 | Score: -64.71 | Avg Score: -124.31 |\n",
"| Experiment: 1 | Episode: 1092 | Score: -79.51 | Avg Score: -122.31 |\n",
"| Experiment: 1 | Episode: 1093 | Score: -257.54 | Avg Score: -122.23 |\n",
"| Experiment: 1 | Episode: 1094 | Score: -203.27 | Avg Score: -123.0 |\n",
"| Experiment: 1 | Episode: 1095 | Score: -91.24 | Avg Score: -122.25 |\n",
"| Experiment: 1 | Episode: 1096 | Score: -89.36 | Avg Score: -120.72 |\n",
"| Experiment: 1 | Episode: 1097 | Score: -92.3 | Avg Score: -120.43 |\n",
"| Experiment: 1 | Episode: 1098 | Score: -107.64 | Avg Score: -120.81 |\n",
"| Experiment: 1 | Episode: 1099 | Score: -160.86 | Avg Score: -119.53 |\n",
"| Experiment: 1 | Episode: 1100 | Score: -63.73 | Avg Score: -118.95 |\n",
"| Experiment: 1 | Episode: 1101 | Score: -75.93 | Avg Score: -119.98 |\n",
"| Experiment: 1 | Episode: 1102 | Score: -74.3 | Avg Score: -120.45 |\n",
"| Experiment: 1 | Episode: 1103 | Score: -111.82 | Avg Score: -120.62 |\n",
"| Experiment: 1 | Episode: 1104 | Score: -114.91 | Avg Score: -121.09 |\n",
"| Experiment: 1 | Episode: 1105 | Score: -38.3 | Avg Score: -120.46 |\n",
"| Experiment: 1 | Episode: 1106 | Score: -311.41 | Avg Score: -122.93 |\n",
"| Experiment: 1 | Episode: 1107 | Score: -121.48 | Avg Score: -122.97 |\n",
"| Experiment: 1 | Episode: 1108 | Score: -58.41 | Avg Score: -121.73 |\n",
"| Experiment: 1 | Episode: 1109 | Score: -135.7 | Avg Score: -122.0 |\n",
"| Experiment: 1 | Episode: 1110 | Score: -115.81 | Avg Score: -122.06 |\n",
"| Experiment: 1 | Episode: 1111 | Score: -77.09 | Avg Score: -120.58 |\n",
"| Experiment: 1 | Episode: 1112 | Score: -91.56 | Avg Score: -120.73 |\n",
"| Experiment: 1 | Episode: 1113 | Score: -113.91 | Avg Score: -120.71 |\n",
"| Experiment: 1 | Episode: 1114 | Score: -49.76 | Avg Score: -120.67 |\n",
"| Experiment: 1 | Episode: 1115 | Score: -47.1 | Avg Score: -120.75 |\n",
"| Experiment: 1 | Episode: 1116 | Score: -65.18 | Avg Score: -118.41 |\n",
"| Experiment: 1 | Episode: 1117 | Score: -74.69 | Avg Score: -118.0 |\n",
"| Experiment: 1 | Episode: 1118 | Score: -86.64 | Avg Score: -117.67 |\n",
"| Experiment: 1 | Episode: 1119 | Score: -139.51 | Avg Score: -118.18 |\n",
"| Experiment: 1 | Episode: 1120 | Score: -56.47 | Avg Score: -117.12 |\n",
"| Experiment: 1 | Episode: 1121 | Score: -146.36 | Avg Score: -117.44 |\n",
"| Experiment: 1 | Episode: 1122 | Score: -80.46 | Avg Score: -116.79 |\n",
"| Experiment: 1 | Episode: 1123 | Score: -135.2 | Avg Score: -116.92 |\n",
"| Experiment: 1 | Episode: 1124 | Score: -37.58 | Avg Score: -116.52 |\n",
"| Experiment: 1 | Episode: 1125 | Score: -48.79 | Avg Score: -116.13 |\n",
"| Experiment: 1 | Episode: 1126 | Score: -74.27 | Avg Score: -115.81 |\n",
"| Experiment: 1 | Episode: 1127 | Score: -115.56 | Avg Score: -114.06 |\n",
"| Experiment: 1 | Episode: 1128 | Score: -187.11 | Avg Score: -115.24 |\n",
"| Experiment: 1 | Episode: 1129 | Score: -48.55 | Avg Score: -115.34 |\n",
"| Experiment: 1 | Episode: 1130 | Score: -97.57 | Avg Score: -115.5 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 | Episode: 1131 | Score: -140.19 | Avg Score: -115.83 |\n",
"| Experiment: 1 | Episode: 1132 | Score: -89.03 | Avg Score: -113.61 |\n",
"| Experiment: 1 | Episode: 1133 | Score: -73.69 | Avg Score: -113.33 |\n",
"| Experiment: 1 | Episode: 1134 | Score: -101.83 | Avg Score: -113.77 |\n",
"| Experiment: 1 | Episode: 1135 | Score: -55.04 | Avg Score: -113.47 |\n",
"| Experiment: 1 | Episode: 1136 | Score: -141.61 | Avg Score: -114.06 |\n",
"| Experiment: 1 | Episode: 1137 | Score: -118.94 | Avg Score: -113.02 |\n",
"| Experiment: 1 | Episode: 1138 | Score: -31.54 | Avg Score: -111.33 |\n",
"| Experiment: 1 | Episode: 1139 | Score: -155.5 | Avg Score: -111.5 |\n",
"| Experiment: 1 | Episode: 1140 | Score: -100.55 | Avg Score: -111.81 |\n",
"| Experiment: 1 | Episode: 1141 | Score: -77.05 | Avg Score: -111.36 |\n",
"| Experiment: 1 | Episode: 1142 | Score: -105.45 | Avg Score: -111.0 |\n",
"| Experiment: 1 | Episode: 1143 | Score: -52.56 | Avg Score: -110.59 |\n",
"| Experiment: 1 | Episode: 1144 | Score: -91.18 | Avg Score: -110.89 |\n",
"| Experiment: 1 | Episode: 1145 | Score: -103.01 | Avg Score: -111.35 |\n",
"| Experiment: 1 | Episode: 1146 | Score: -185.97 | Avg Score: -111.88 |\n",
"| Experiment: 1 | Episode: 1147 | Score: -173.86 | Avg Score: -112.44 |\n",
"| Experiment: 1 | Episode: 1148 | Score: -94.25 | Avg Score: -112.32 |\n",
"| Experiment: 1 | Episode: 1149 | Score: -94.2 | Avg Score: -112.44 |\n",
"| Experiment: 1 | Episode: 1150 | Score: -224.83 | Avg Score: -113.76 |\n",
"| Experiment: 1 | Episode: 1151 | Score: -51.25 | Avg Score: -113.42 |\n",
"| Experiment: 1 | Episode: 1152 | Score: -87.11 | Avg Score: -111.91 |\n",
"| Experiment: 1 | Episode: 1153 | Score: -90.74 | Avg Score: -110.99 |\n",
"| Experiment: 1 | Episode: 1154 | Score: -67.93 | Avg Score: -110.89 |\n",
"| Experiment: 1 | Episode: 1155 | Score: -82.13 | Avg Score: -110.71 |\n",
"| Experiment: 1 | Episode: 1156 | Score: -81.72 | Avg Score: -110.34 |\n",
"| Experiment: 1 | Episode: 1157 | Score: -45.38 | Avg Score: -109.82 |\n",
"| Experiment: 1 | Episode: 1158 | Score: -38.55 | Avg Score: -109.52 |\n",
"| Experiment: 1 | Episode: 1159 | Score: -158.65 | Avg Score: -110.31 |\n",
"| Experiment: 1 | Episode: 1160 | Score: -63.67 | Avg Score: -109.34 |\n",
"| Experiment: 1 | Episode: 1161 | Score: -88.02 | Avg Score: -107.98 |\n",
"| Experiment: 1 | Episode: 1162 | Score: -118.73 | Avg Score: -107.42 |\n",
"| Experiment: 1 | Episode: 1163 | Score: -141.25 | Avg Score: -108.11 |\n",
"| Experiment: 1 | Episode: 1164 | Score: -37.12 | Avg Score: -107.41 |\n",
"| Experiment: 1 | Episode: 1165 | Score: -17.7 | Avg Score: -106.64 |\n",
"| Experiment: 1 | Episode: 1166 | Score: -79.93 | Avg Score: -105.9 |\n",
"| Experiment: 1 | Episode: 1167 | Score: -45.44 | Avg Score: -105.78 |\n",
"| Experiment: 1 | Episode: 1168 | Score: -49.24 | Avg Score: -105.06 |\n",
"| Experiment: 1 | Episode: 1169 | Score: -185.28 | Avg Score: -106.56 |\n",
"| Experiment: 1 | Episode: 1170 | Score: -79.19 | Avg Score: -106.2 |\n",
"| Experiment: 1 | Episode: 1171 | Score: -129.43 | Avg Score: -105.45 |\n",
"| Experiment: 1 | Episode: 1172 | Score: -100.51 | Avg Score: -105.65 |\n",
"| Experiment: 1 | Episode: 1173 | Score: -65.52 | Avg Score: -104.99 |\n",
"| Experiment: 1 | Episode: 1174 | Score: -145.62 | Avg Score: -105.25 |\n",
"| Experiment: 1 | Episode: 1175 | Score: -51.01 | Avg Score: -104.55 |\n",
"| Experiment: 1 | Episode: 1176 | Score: -134.53 | Avg Score: -104.12 |\n",
"| Experiment: 1 | Episode: 1177 | Score: -88.26 | Avg Score: -103.62 |\n",
"| Experiment: 1 | Episode: 1178 | Score: -93.31 | Avg Score: -103.11 |\n",
"| Experiment: 1 | Episode: 1179 | Score: -102.1 | Avg Score: -103.06 |\n",
"| Experiment: 1 | Episode: 1180 | Score: -73.94 | Avg Score: -103.64 |\n",
"| Experiment: 1 | Episode: 1181 | Score: -128.31 | Avg Score: -103.67 |\n",
"| Experiment: 1 | Episode: 1182 | Score: -206.45 | Avg Score: -103.89 |\n",
"| Experiment: 1 | Episode: 1183 | Score: -73.56 | Avg Score: -103.6 |\n",
"| Experiment: 1 | Episode: 1184 | Score: -81.59 | Avg Score: -103.2 |\n",
"| Experiment: 1 | Episode: 1185 | Score: -29.29 | Avg Score: -100.87 |\n",
"| Experiment: 1 | Episode: 1186 | Score: -154.36 | Avg Score: -101.2 |\n",
"| Experiment: 1 | Episode: 1187 | Score: -87.67 | Avg Score: -100.94 |\n",
"| Experiment: 1 | Episode: 1188 | Score: -58.65 | Avg Score: -99.57 |\n",
"| Experiment: 1 | Episode: 1189 | Score: -65.23 | Avg Score: -99.65 |\n",
"| Experiment: 1 | Episode: 1190 | Score: -115.26 | Avg Score: -99.46 |\n",
"| Experiment: 1 | Episode: 1191 | Score: -11.99 | Avg Score: -98.93 |\n",
"| Experiment: 1 | Episode: 1192 | Score: -84.45 | Avg Score: -98.98 |\n",
"| Experiment: 1 | Episode: 1193 | Score: -10.85 | Avg Score: -96.51 |\n",
"| Experiment: 1 | Episode: 1194 | Score: -73.81 | Avg Score: -95.22 |\n",
"| Experiment: 1 | Episode: 1195 | Score: -310.79 | Avg Score: -97.41 |\n",
"| Experiment: 1 | Episode: 1196 | Score: -169.23 | Avg Score: -98.21 |\n",
"| Experiment: 1 | Episode: 1197 | Score: -64.93 | Avg Score: -97.94 |\n",
"| Experiment: 1 | Episode: 1198 | Score: -73.85 | Avg Score: -97.6 |\n",
"| Experiment: 1 | Episode: 1199 | Score: -155.74 | Avg Score: -97.55 |\n",
"| Experiment: 1 | Episode: 1200 | Score: -261.16 | Avg Score: -99.52 |\n",
"| Experiment: 1 | Episode: 1201 | Score: -73.09 | Avg Score: -99.49 |\n",
"| Experiment: 1 | Episode: 1202 | Score: -83.71 | Avg Score: -99.59 |\n",
"| Experiment: 1 | Episode: 1203 | Score: -111.95 | Avg Score: -99.59 |\n",
"| Experiment: 1 | Episode: 1204 | Score: -224.68 | Avg Score: -100.69 |\n",
"| Experiment: 1 | Episode: 1205 | Score: -109.56 | Avg Score: -101.4 |\n",
"| Experiment: 1 | Episode: 1206 | Score: -100.24 | Avg Score: -99.29 |\n",
"| Experiment: 1 | Episode: 1207 | Score: -139.0 | Avg Score: -99.46 |\n",
"| Experiment: 1 | Episode: 1208 | Score: -20.24 | Avg Score: -99.08 |\n",
"| Experiment: 1 | Episode: 1209 | Score: -240.76 | Avg Score: -100.13 |\n",
"| Experiment: 1 | Episode: 1210 | Score: -147.41 | Avg Score: -100.45 |\n",
"| Experiment: 1 | Episode: 1211 | Score: -89.82 | Avg Score: -100.57 |\n",
"| Experiment: 1 | Episode: 1212 | Score: -114.88 | Avg Score: -100.81 |\n",
"| Experiment: 1 | Episode: 1213 | Score: -19.11 | Avg Score: -99.86 |\n",
"| Experiment: 1 | Episode: 1214 | Score: -46.87 | Avg Score: -99.83 |\n",
"| Experiment: 1 | Episode: 1215 | Score: -124.42 | Avg Score: -100.6 |\n",
"| Experiment: 1 | Episode: 1216 | Score: -59.07 | Avg Score: -100.54 |\n",
"| Experiment: 1 | Episode: 1217 | Score: -89.89 | Avg Score: -100.7 |\n",
"| Experiment: 1 | Episode: 1218 | Score: -58.45 | Avg Score: -100.41 |\n",
"| Experiment: 1 | Episode: 1219 | Score: -104.53 | Avg Score: -100.06 |\n",
"| Experiment: 1 | Episode: 1220 | Score: -103.4 | Avg Score: -100.53 |\n",
"| Experiment: 1 | Episode: 1221 | Score: -78.34 | Avg Score: -99.85 |\n",
"| Experiment: 1 | Episode: 1222 | Score: -177.93 | Avg Score: -100.83 |\n",
"| Experiment: 1 | Episode: 1223 | Score: -106.83 | Avg Score: -100.54 |\n",
"| Experiment: 1 | Episode: 1224 | Score: -79.9 | Avg Score: -100.97 |\n",
"| Experiment: 1 | Episode: 1225 | Score: -60.6 | Avg Score: -101.08 |\n",
"| Experiment: 1 | Episode: 1226 | Score: -91.78 | Avg Score: -101.26 |\n",
"| Experiment: 1 | Episode: 1227 | Score: -98.4 | Avg Score: -101.09 |\n",
"| Experiment: 1 | Episode: 1228 | Score: -140.62 | Avg Score: -100.62 |\n",
"| Experiment: 1 | Episode: 1229 | Score: -131.76 | Avg Score: -101.46 |\n",
"| Experiment: 1 | Episode: 1230 | Score: -72.26 | Avg Score: -101.2 |\n",
"| Experiment: 1 | Episode: 1231 | Score: -113.82 | Avg Score: -100.94 |\n",
"| Experiment: 1 | Episode: 1232 | Score: -100.44 | Avg Score: -101.05 |\n",
"| Experiment: 1 | Episode: 1233 | Score: -118.26 | Avg Score: -101.5 |\n",
"| Experiment: 1 | Episode: 1234 | Score: -98.76 | Avg Score: -101.47 |\n",
"| Experiment: 1 | Episode: 1235 | Score: -135.5 | Avg Score: -102.27 |\n",
"| Experiment: 1 | Episode: 1236 | Score: -100.21 | Avg Score: -101.86 |\n",
"| Experiment: 1 | Episode: 1237 | Score: -71.25 | Avg Score: -101.38 |\n",
"| Experiment: 1 | Episode: 1238 | Score: -128.02 | Avg Score: -102.35 |\n",
"| Experiment: 1 | Episode: 1239 | Score: -90.31 | Avg Score: -101.69 |\n",
"| Experiment: 1 | Episode: 1240 | Score: -103.66 | Avg Score: -101.73 |\n",
"| Experiment: 1 | Episode: 1241 | Score: -159.66 | Avg Score: -102.55 |\n",
"| Experiment: 1 | Episode: 1242 | Score: -115.77 | Avg Score: -102.65 |\n",
"| Experiment: 1 | Episode: 1243 | Score: -70.97 | Avg Score: -102.84 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 | Episode: 1244 | Score: -81.83 | Avg Score: -102.75 |\n",
"| Experiment: 1 | Episode: 1245 | Score: -81.72 | Avg Score: -102.53 |\n",
"| Experiment: 1 | Episode: 1246 | Score: -73.54 | Avg Score: -101.41 |\n",
"| Experiment: 1 | Episode: 1247 | Score: -60.23 | Avg Score: -100.27 |\n",
"| Experiment: 1 | Episode: 1248 | Score: -145.51 | Avg Score: -100.78 |\n",
"| Experiment: 1 | Episode: 1249 | Score: -89.86 | Avg Score: -100.74 |\n",
"| Experiment: 1 | Episode: 1250 | Score: -173.38 | Avg Score: -100.23 |\n",
"| Experiment: 1 | Episode: 1251 | Score: -114.74 | Avg Score: -100.86 |\n",
"| Experiment: 1 | Episode: 1252 | Score: -137.44 | Avg Score: -101.36 |\n",
"| Experiment: 1 | Episode: 1253 | Score: -222.63 | Avg Score: -102.68 |\n",
"| Experiment: 1 | Episode: 1254 | Score: -107.46 | Avg Score: -103.08 |\n",
"| Experiment: 1 | Episode: 1255 | Score: -90.23 | Avg Score: -103.16 |\n",
"| Experiment: 1 | Episode: 1256 | Score: 21.59 | Avg Score: -102.13 |\n",
"| Experiment: 1 | Episode: 1257 | Score: -112.32 | Avg Score: -102.8 |\n",
"| Experiment: 1 | Episode: 1258 | Score: -103.07 | Avg Score: -103.44 |\n",
"| Experiment: 1 | Episode: 1259 | Score: -122.53 | Avg Score: -103.08 |\n",
"| Experiment: 1 | Episode: 1260 | Score: -185.53 | Avg Score: -104.3 |\n",
"| Experiment: 1 | Episode: 1261 | Score: -85.96 | Avg Score: -104.28 |\n",
"| Experiment: 1 | Episode: 1262 | Score: -118.78 | Avg Score: -104.28 |\n",
"| Experiment: 1 | Episode: 1263 | Score: -143.3 | Avg Score: -104.3 |\n",
"| Experiment: 1 | Episode: 1264 | Score: -86.42 | Avg Score: -104.79 |\n",
"| Experiment: 1 | Episode: 1265 | Score: -122.15 | Avg Score: -105.84 |\n",
"| Experiment: 1 | Episode: 1266 | Score: -142.79 | Avg Score: -106.47 |\n",
"| Experiment: 1 | Episode: 1267 | Score: -248.43 | Avg Score: -108.5 |\n",
"| Experiment: 1 | Episode: 1268 | Score: -67.74 | Avg Score: -108.68 |\n",
"| Experiment: 1 | Episode: 1269 | Score: -348.54 | Avg Score: -110.31 |\n",
"| Experiment: 1 | Episode: 1270 | Score: -76.4 | Avg Score: -110.28 |\n",
"| Experiment: 1 | Episode: 1271 | Score: -69.34 | Avg Score: -109.68 |\n",
"| Experiment: 1 | Episode: 1272 | Score: -28.65 | Avg Score: -108.97 |\n",
"| Experiment: 1 | Episode: 1273 | Score: -48.1 | Avg Score: -108.79 |\n",
"| Experiment: 1 | Episode: 1274 | Score: -87.36 | Avg Score: -108.21 |\n",
"| Experiment: 1 | Episode: 1275 | Score: -100.73 | Avg Score: -108.71 |\n",
"| Experiment: 1 | Episode: 1276 | Score: -42.42 | Avg Score: -107.78 |\n",
"| Experiment: 1 | Episode: 1277 | Score: -88.98 | Avg Score: -107.79 |\n",
"| Experiment: 1 | Episode: 1278 | Score: -127.6 | Avg Score: -108.13 |\n",
"| Experiment: 1 | Episode: 1279 | Score: -180.6 | Avg Score: -108.92 |\n",
"| Experiment: 1 | Episode: 1280 | Score: -160.17 | Avg Score: -109.78 |\n",
"| Experiment: 1 | Episode: 1281 | Score: -140.09 | Avg Score: -109.9 |\n",
"| Experiment: 1 | Episode: 1282 | Score: -71.38 | Avg Score: -108.55 |\n",
"| Experiment: 1 | Episode: 1283 | Score: -149.59 | Avg Score: -109.31 |\n",
"| Experiment: 1 | Episode: 1284 | Score: -99.89 | Avg Score: -109.49 |\n",
"| Experiment: 1 | Episode: 1285 | Score: -133.24 | Avg Score: -110.53 |\n",
"| Experiment: 1 | Episode: 1286 | Score: -148.5 | Avg Score: -110.47 |\n",
"| Experiment: 1 | Episode: 1287 | Score: -212.64 | Avg Score: -111.72 |\n",
"| Experiment: 1 | Episode: 1288 | Score: -87.03 | Avg Score: -112.01 |\n",
"| Experiment: 1 | Episode: 1289 | Score: -102.32 | Avg Score: -112.38 |\n",
"| Experiment: 1 | Episode: 1290 | Score: -115.7 | Avg Score: -112.38 |\n",
"| Experiment: 1 | Episode: 1291 | Score: -68.56 | Avg Score: -112.95 |\n",
"| Experiment: 1 | Episode: 1292 | Score: -151.69 | Avg Score: -113.62 |\n",
"| Experiment: 1 | Episode: 1293 | Score: -109.1 | Avg Score: -114.6 |\n",
"| Experiment: 1 | Episode: 1294 | Score: -102.57 | Avg Score: -114.89 |\n",
"| Experiment: 1 | Episode: 1295 | Score: -117.49 | Avg Score: -112.96 |\n",
"| Experiment: 1 | Episode: 1296 | Score: -88.41 | Avg Score: -112.15 |\n",
"| Experiment: 1 | Episode: 1297 | Score: -43.53 | Avg Score: -111.94 |\n",
"| Experiment: 1 | Episode: 1298 | Score: -34.28 | Avg Score: -111.54 |\n",
"| Experiment: 1 | Episode: 1299 | Score: -100.81 | Avg Score: -110.99 |\n",
"| Experiment: 1 | Episode: 1300 | Score: -201.92 | Avg Score: -110.4 |\n",
"| Experiment: 1 | Episode: 1301 | Score: -112.8 | Avg Score: -110.79 |\n",
"| Experiment: 1 | Episode: 1302 | Score: -145.22 | Avg Score: -111.41 |\n",
"| Experiment: 1 | Episode: 1303 | Score: -80.79 | Avg Score: -111.1 |\n",
"| Experiment: 1 | Episode: 1304 | Score: -79.86 | Avg Score: -109.65 |\n",
"| Experiment: 1 | Episode: 1305 | Score: -109.39 | Avg Score: -109.65 |\n",
"| Experiment: 1 | Episode: 1306 | Score: -88.06 | Avg Score: -109.53 |\n",
"| Experiment: 1 | Episode: 1307 | Score: -21.93 | Avg Score: -108.36 |\n",
"| Experiment: 1 | Episode: 1308 | Score: -122.94 | Avg Score: -109.38 |\n",
"| Experiment: 1 | Episode: 1309 | Score: -133.44 | Avg Score: -108.31 |\n",
"| Experiment: 1 | Episode: 1310 | Score: -44.26 | Avg Score: -107.28 |\n",
"| Experiment: 1 | Episode: 1311 | Score: -72.46 | Avg Score: -107.1 |\n",
"| Experiment: 1 | Episode: 1312 | Score: -83.94 | Avg Score: -106.8 |\n",
"| Experiment: 1 | Episode: 1313 | Score: -71.12 | Avg Score: -107.32 |\n",
"| Experiment: 1 | Episode: 1314 | Score: -95.06 | Avg Score: -107.8 |\n",
"| Experiment: 1 | Episode: 1315 | Score: -79.71 | Avg Score: -107.35 |\n",
"| Experiment: 1 | Episode: 1316 | Score: -94.87 | Avg Score: -107.71 |\n",
"| Experiment: 1 | Episode: 1317 | Score: -124.84 | Avg Score: -108.06 |\n",
"| Experiment: 1 | Episode: 1318 | Score: -125.74 | Avg Score: -108.73 |\n",
"| Experiment: 1 | Episode: 1319 | Score: -130.53 | Avg Score: -108.99 |\n",
"| Experiment: 1 | Episode: 1320 | Score: -97.11 | Avg Score: -108.93 |\n",
"| Experiment: 1 | Episode: 1321 | Score: -93.07 | Avg Score: -109.08 |\n",
"| Experiment: 1 | Episode: 1322 | Score: -187.35 | Avg Score: -109.17 |\n",
"| Experiment: 1 | Episode: 1323 | Score: -144.81 | Avg Score: -109.55 |\n",
"| Experiment: 1 | Episode: 1324 | Score: -119.83 | Avg Score: -109.95 |\n",
"| Experiment: 1 | Episode: 1325 | Score: -136.61 | Avg Score: -110.71 |\n",
"| Experiment: 1 | Episode: 1326 | Score: -130.18 | Avg Score: -111.09 |\n",
"| Experiment: 1 | Episode: 1327 | Score: -50.03 | Avg Score: -110.61 |\n",
"| Experiment: 1 | Episode: 1328 | Score: -133.62 | Avg Score: -110.54 |\n",
"| Experiment: 1 | Episode: 1329 | Score: -79.82 | Avg Score: -110.02 |\n",
"| Experiment: 1 | Episode: 1330 | Score: -136.58 | Avg Score: -110.66 |\n",
"| Experiment: 1 | Episode: 1331 | Score: -119.52 | Avg Score: -110.72 |\n",
"| Experiment: 1 | Episode: 1332 | Score: -53.4 | Avg Score: -110.25 |\n",
"| Experiment: 1 | Episode: 1333 | Score: -77.3 | Avg Score: -109.84 |\n",
"| Experiment: 1 | Episode: 1334 | Score: -164.05 | Avg Score: -110.49 |\n",
"| Experiment: 1 | Episode: 1335 | Score: -90.72 | Avg Score: -110.04 |\n",
"| Experiment: 1 | Episode: 1336 | Score: -126.67 | Avg Score: -110.31 |\n",
"| Experiment: 1 | Episode: 1337 | Score: -117.21 | Avg Score: -110.77 |\n",
"| Experiment: 1 | Episode: 1338 | Score: -76.81 | Avg Score: -110.26 |\n",
"| Experiment: 1 | Episode: 1339 | Score: -148.22 | Avg Score: -110.84 |\n",
"| Experiment: 1 | Episode: 1340 | Score: -71.41 | Avg Score: -110.51 |\n",
"| Experiment: 1 | Episode: 1341 | Score: -56.59 | Avg Score: -109.48 |\n",
"| Experiment: 1 | Episode: 1342 | Score: -157.1 | Avg Score: -109.9 |\n",
"| Experiment: 1 | Episode: 1343 | Score: -119.59 | Avg Score: -110.38 |\n",
"| Experiment: 1 | Episode: 1344 | Score: -115.22 | Avg Score: -110.72 |\n",
"| Experiment: 1 | Episode: 1345 | Score: -84.11 | Avg Score: -110.74 |\n",
"| Experiment: 1 | Episode: 1346 | Score: -119.02 | Avg Score: -111.19 |\n",
"| Experiment: 1 | Episode: 1347 | Score: -67.62 | Avg Score: -111.27 |\n",
"| Experiment: 1 | Episode: 1348 | Score: -114.38 | Avg Score: -110.96 |\n",
"| Experiment: 1 | Episode: 1349 | Score: -52.59 | Avg Score: -110.58 |\n",
"| Experiment: 1 | Episode: 1350 | Score: -139.94 | Avg Score: -110.25 |\n",
"| Experiment: 1 | Episode: 1351 | Score: -85.06 | Avg Score: -109.95 |\n",
"| Experiment: 1 | Episode: 1352 | Score: -144.07 | Avg Score: -110.02 |\n",
"| Experiment: 1 | Episode: 1353 | Score: -78.92 | Avg Score: -108.58 |\n",
"| Experiment: 1 | Episode: 1354 | Score: -306.52 | Avg Score: -110.57 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 | Episode: 1355 | Score: -74.37 | Avg Score: -110.41 |\n",
"| Experiment: 1 | Episode: 1356 | Score: -206.87 | Avg Score: -112.7 |\n",
"| Experiment: 1 | Episode: 1357 | Score: -206.47 | Avg Score: -113.64 |\n",
"| Experiment: 1 | Episode: 1358 | Score: -167.57 | Avg Score: -114.29 |\n",
"| Experiment: 1 | Episode: 1359 | Score: -116.62 | Avg Score: -114.23 |\n",
"| Experiment: 1 | Episode: 1360 | Score: -75.12 | Avg Score: -113.12 |\n",
"| Experiment: 1 | Episode: 1361 | Score: -271.72 | Avg Score: -114.98 |\n",
"| Experiment: 1 | Episode: 1362 | Score: -73.41 | Avg Score: -114.53 |\n",
"| Experiment: 1 | Episode: 1363 | Score: -88.51 | Avg Score: -113.98 |\n",
"| Experiment: 1 | Episode: 1364 | Score: -113.19 | Avg Score: -114.25 |\n",
"| Experiment: 1 | Episode: 1365 | Score: -118.36 | Avg Score: -114.21 |\n",
"| Experiment: 1 | Episode: 1366 | Score: -173.5 | Avg Score: -114.52 |\n",
"| Experiment: 1 | Episode: 1367 | Score: -75.29 | Avg Score: -112.78 |\n",
"| Experiment: 1 | Episode: 1368 | Score: -77.06 | Avg Score: -112.88 |\n",
"| Experiment: 1 | Episode: 1369 | Score: -64.62 | Avg Score: -110.04 |\n",
"| Experiment: 1 | Episode: 1370 | Score: 100.45 | Avg Score: -108.27 |\n",
"| Experiment: 1 | Episode: 1371 | Score: -191.06 | Avg Score: -109.49 |\n",
"| Experiment: 1 | Episode: 1372 | Score: -83.28 | Avg Score: -110.03 |\n",
"| Experiment: 1 | Episode: 1373 | Score: -134.45 | Avg Score: -110.9 |\n",
"| Experiment: 1 | Episode: 1374 | Score: -108.3 | Avg Score: -111.11 |\n",
"| Experiment: 1 | Episode: 1375 | Score: -71.56 | Avg Score: -110.81 |\n",
"| Experiment: 1 | Episode: 1376 | Score: -91.0 | Avg Score: -111.3 |\n",
"| Experiment: 1 | Episode: 1377 | Score: -110.59 | Avg Score: -111.52 |\n",
"| Experiment: 1 | Episode: 1378 | Score: -202.35 | Avg Score: -112.26 |\n",
"| Experiment: 1 | Episode: 1379 | Score: -120.62 | Avg Score: -111.66 |\n",
"| Experiment: 1 | Episode: 1380 | Score: -78.73 | Avg Score: -110.85 |\n",
"| Experiment: 1 | Episode: 1381 | Score: -205.17 | Avg Score: -111.5 |\n",
"| Experiment: 1 | Episode: 1382 | Score: -79.05 | Avg Score: -111.58 |\n",
"| Experiment: 1 | Episode: 1383 | Score: -131.04 | Avg Score: -111.39 |\n",
"| Experiment: 1 | Episode: 1384 | Score: -104.98 | Avg Score: -111.44 |\n",
"| Experiment: 1 | Episode: 1385 | Score: -74.4 | Avg Score: -110.85 |\n",
"| Experiment: 1 | Episode: 1386 | Score: -61.55 | Avg Score: -109.98 |\n",
"| Experiment: 1 | Episode: 1387 | Score: -98.29 | Avg Score: -108.84 |\n",
"| Experiment: 1 | Episode: 1388 | Score: -190.69 | Avg Score: -109.88 |\n",
"| Experiment: 1 | Episode: 1389 | Score: -150.41 | Avg Score: -110.36 |\n",
"| Experiment: 1 | Episode: 1390 | Score: -164.46 | Avg Score: -110.85 |\n",
"| Experiment: 1 | Episode: 1391 | Score: -50.4 | Avg Score: -110.66 |\n",
"| Experiment: 1 | Episode: 1392 | Score: -69.5 | Avg Score: -109.84 |\n",
"| Experiment: 1 | Episode: 1393 | Score: -100.46 | Avg Score: -109.76 |\n",
"| Experiment: 1 | Episode: 1394 | Score: -240.22 | Avg Score: -111.13 |\n",
"| Experiment: 1 | Episode: 1395 | Score: -115.68 | Avg Score: -111.11 |\n",
"| Experiment: 1 | Episode: 1396 | Score: -27.69 | Avg Score: -110.51 |\n",
"| Experiment: 1 | Episode: 1397 | Score: -88.15 | Avg Score: -110.95 |\n",
"| Experiment: 1 | Episode: 1398 | Score: -107.55 | Avg Score: -111.69 |\n",
"| Experiment: 1 | Episode: 1399 | Score: -92.25 | Avg Score: -111.6 |\n",
"| Experiment: 1 | Episode: 1400 | Score: -81.21 | Avg Score: -110.39 |\n",
"| Experiment: 1 | Episode: 1401 | Score: -122.86 | Avg Score: -110.49 |\n",
"| Experiment: 1 | Episode: 1402 | Score: -23.78 | Avg Score: -109.28 |\n",
"| Experiment: 1 | Episode: 1403 | Score: -82.98 | Avg Score: -109.3 |\n",
"| Experiment: 1 | Episode: 1404 | Score: -90.38 | Avg Score: -109.41 |\n",
"| Experiment: 1 | Episode: 1405 | Score: -107.11 | Avg Score: -109.38 |\n",
"| Experiment: 1 | Episode: 1406 | Score: -47.17 | Avg Score: -108.97 |\n",
"| Experiment: 1 | Episode: 1407 | Score: -90.74 | Avg Score: -109.66 |\n",
"| Experiment: 1 | Episode: 1408 | Score: -270.47 | Avg Score: -111.14 |\n",
"| Experiment: 1 | Episode: 1409 | Score: -60.82 | Avg Score: -110.41 |\n",
"| Experiment: 1 | Episode: 1410 | Score: -62.79 | Avg Score: -110.6 |\n",
"| Experiment: 1 | Episode: 1411 | Score: -48.96 | Avg Score: -110.36 |\n",
"| Experiment: 1 | Episode: 1412 | Score: -231.54 | Avg Score: -111.84 |\n",
"| Experiment: 1 | Episode: 1413 | Score: -130.92 | Avg Score: -112.44 |\n",
"| Experiment: 1 | Episode: 1414 | Score: -102.42 | Avg Score: -112.51 |\n",
"| Experiment: 1 | Episode: 1415 | Score: -132.15 | Avg Score: -113.03 |\n",
"| Experiment: 1 | Episode: 1416 | Score: -252.9 | Avg Score: -114.61 |\n",
"| Experiment: 1 | Episode: 1417 | Score: -149.41 | Avg Score: -114.86 |\n",
"| Experiment: 1 | Episode: 1418 | Score: -89.83 | Avg Score: -114.5 |\n",
"| Experiment: 1 | Episode: 1419 | Score: -63.01 | Avg Score: -113.83 |\n",
"| Experiment: 1 | Episode: 1420 | Score: -190.82 | Avg Score: -114.76 |\n",
"| Experiment: 1 | Episode: 1421 | Score: -101.49 | Avg Score: -114.85 |\n",
"| Experiment: 1 | Episode: 1422 | Score: -47.06 | Avg Score: -113.44 |\n",
"| Experiment: 1 | Episode: 1423 | Score: -61.07 | Avg Score: -112.61 |\n",
"| Experiment: 1 | Episode: 1424 | Score: -37.96 | Avg Score: -111.79 |\n",
"| Experiment: 1 | Episode: 1425 | Score: -105.58 | Avg Score: -111.48 |\n",
"| Experiment: 1 | Episode: 1426 | Score: -66.35 | Avg Score: -110.84 |\n",
"| Experiment: 1 | Episode: 1427 | Score: -87.87 | Avg Score: -111.22 |\n",
"| Experiment: 1 | Episode: 1428 | Score: -106.82 | Avg Score: -110.95 |\n",
"| Experiment: 1 | Episode: 1429 | Score: -104.71 | Avg Score: -111.2 |\n",
"| Experiment: 1 | Episode: 1430 | Score: -119.34 | Avg Score: -111.03 |\n",
"| Experiment: 1 | Episode: 1431 | Score: -470.37 | Avg Score: -114.53 |\n",
"| Experiment: 1 | Episode: 1432 | Score: -64.96 | Avg Score: -114.65 |\n",
"| Experiment: 1 | Episode: 1433 | Score: -51.75 | Avg Score: -114.39 |\n",
"| Experiment: 1 | Episode: 1434 | Score: -60.2 | Avg Score: -113.36 |\n",
"| Experiment: 1 | Episode: 1435 | Score: -126.82 | Avg Score: -113.72 |\n",
"| Experiment: 1 | Episode: 1436 | Score: -132.76 | Avg Score: -113.78 |\n",
"| Experiment: 1 | Episode: 1437 | Score: -107.98 | Avg Score: -113.69 |\n",
"| Experiment: 1 | Episode: 1438 | Score: -101.39 | Avg Score: -113.93 |\n",
"| Experiment: 1 | Episode: 1439 | Score: 95.93 | Avg Score: -111.49 |\n",
"| Experiment: 1 | Episode: 1440 | Score: -119.16 | Avg Score: -111.97 |\n",
"| Experiment: 1 | Episode: 1441 | Score: -163.6 | Avg Score: -113.04 |\n",
"| Experiment: 1 | Episode: 1442 | Score: -46.82 | Avg Score: -111.94 |\n",
"| Experiment: 1 | Episode: 1443 | Score: -92.03 | Avg Score: -111.66 |\n",
"| Experiment: 1 | Episode: 1444 | Score: -134.34 | Avg Score: -111.85 |\n",
"| Experiment: 1 | Episode: 1445 | Score: -101.14 | Avg Score: -112.02 |\n",
"| Experiment: 1 | Episode: 1446 | Score: -118.66 | Avg Score: -112.02 |\n",
"| Experiment: 1 | Episode: 1447 | Score: -58.83 | Avg Score: -111.93 |\n",
"| Experiment: 1 | Episode: 1448 | Score: -167.22 | Avg Score: -112.46 |\n",
"| Experiment: 1 | Episode: 1449 | Score: -161.13 | Avg Score: -113.54 |\n",
"| Experiment: 1 | Episode: 1450 | Score: -310.86 | Avg Score: -115.25 |\n",
"| Experiment: 1 | Episode: 1451 | Score: -313.58 | Avg Score: -117.54 |\n",
"| Experiment: 1 | Episode: 1452 | Score: -64.13 | Avg Score: -116.74 |\n",
"| Experiment: 1 | Episode: 1453 | Score: -37.14 | Avg Score: -116.32 |\n",
"| Experiment: 1 | Episode: 1454 | Score: -341.63 | Avg Score: -116.67 |\n",
"| Experiment: 1 | Episode: 1455 | Score: -62.17 | Avg Score: -116.55 |\n",
"| Experiment: 1 | Episode: 1456 | Score: -104.67 | Avg Score: -115.53 |\n",
"| Experiment: 1 | Episode: 1457 | Score: -43.49 | Avg Score: -113.9 |\n",
"| Experiment: 1 | Episode: 1458 | Score: -86.42 | Avg Score: -113.09 |\n",
"| Experiment: 1 | Episode: 1459 | Score: -118.59 | Avg Score: -113.11 |\n",
"| Experiment: 1 | Episode: 1460 | Score: -96.06 | Avg Score: -113.32 |\n",
"| Experiment: 1 | Episode: 1461 | Score: -94.42 | Avg Score: -111.54 |\n",
"| Experiment: 1 | Episode: 1462 | Score: -71.31 | Avg Score: -111.52 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 | Episode: 1463 | Score: -117.11 | Avg Score: -111.81 |\n",
"| Experiment: 1 | Episode: 1464 | Score: -107.71 | Avg Score: -111.75 |\n",
"| Experiment: 1 | Episode: 1465 | Score: -108.62 | Avg Score: -111.66 |\n",
"| Experiment: 1 | Episode: 1466 | Score: -193.92 | Avg Score: -111.86 |\n",
"| Experiment: 1 | Episode: 1467 | Score: -61.88 | Avg Score: -111.73 |\n",
"| Experiment: 1 | Episode: 1468 | Score: -121.93 | Avg Score: -112.17 |\n",
"| Experiment: 1 | Episode: 1469 | Score: -89.59 | Avg Score: -112.42 |\n",
"| Experiment: 1 | Episode: 1470 | Score: -95.81 | Avg Score: -114.39 |\n",
"| Experiment: 1 | Episode: 1471 | Score: -148.34 | Avg Score: -113.96 |\n",
"| Experiment: 1 | Episode: 1472 | Score: -252.16 | Avg Score: -115.65 |\n",
"| Experiment: 1 | Episode: 1473 | Score: -206.3 | Avg Score: -116.37 |\n",
"| Experiment: 1 | Episode: 1474 | Score: -153.66 | Avg Score: -116.82 |\n",
"| Experiment: 1 | Episode: 1475 | Score: -76.95 | Avg Score: -116.87 |\n",
"| Experiment: 1 | Episode: 1476 | Score: -245.62 | Avg Score: -118.42 |\n",
"| Experiment: 1 | Episode: 1477 | Score: -185.72 | Avg Score: -119.17 |\n",
"| Experiment: 1 | Episode: 1478 | Score: -166.21 | Avg Score: -118.81 |\n",
"| Experiment: 1 | Episode: 1479 | Score: -285.95 | Avg Score: -120.46 |\n",
"| Experiment: 1 | Episode: 1480 | Score: -178.56 | Avg Score: -121.46 |\n",
"| Experiment: 1 | Episode: 1481 | Score: -86.84 | Avg Score: -120.28 |\n",
"| Experiment: 1 | Episode: 1482 | Score: -65.96 | Avg Score: -120.15 |\n",
"| Experiment: 1 | Episode: 1483 | Score: -96.18 | Avg Score: -119.8 |\n",
"| Experiment: 1 | Episode: 1484 | Score: -109.79 | Avg Score: -119.85 |\n",
"| Experiment: 1 | Episode: 1485 | Score: -89.72 | Avg Score: -120.0 |\n",
"| Experiment: 1 | Episode: 1486 | Score: -90.64 | Avg Score: -120.29 |\n",
"| Experiment: 1 | Episode: 1487 | Score: -140.74 | Avg Score: -120.72 |\n",
"| Experiment: 1 | Episode: 1488 | Score: -35.14 | Avg Score: -119.16 |\n",
"| Experiment: 1 | Episode: 1489 | Score: -69.48 | Avg Score: -118.35 |\n",
"| Experiment: 1 | Episode: 1490 | Score: -151.19 | Avg Score: -118.22 |\n",
"| Experiment: 1 | Episode: 1491 | Score: -50.07 | Avg Score: -118.21 |\n",
"| Experiment: 1 | Episode: 1492 | Score: -45.54 | Avg Score: -117.97 |\n",
"| Experiment: 1 | Episode: 1493 | Score: -148.39 | Avg Score: -118.45 |\n",
"| Experiment: 1 | Episode: 1494 | Score: -122.96 | Avg Score: -117.28 |\n",
"| Experiment: 1 | Episode: 1495 | Score: -119.35 | Avg Score: -117.32 |\n",
"| Experiment: 1 | Episode: 1496 | Score: -171.82 | Avg Score: -118.76 |\n",
"| Experiment: 1 | Episode: 1497 | Score: -79.64 | Avg Score: -118.67 |\n",
"| Experiment: 1 | Episode: 1498 | Score: -30.98 | Avg Score: -117.91 |\n",
"| Experiment: 1 | Episode: 1499 | Score: -51.96 | Avg Score: -117.51 |\n",
"| Experiment: 1 | Episode: 1500 | Score: -69.11 | Avg Score: -117.38 |\n",
"| Experiment: 1 | Episode: 1501 | Score: -106.17 | Avg Score: -117.22 |\n",
"| Experiment: 1 | Episode: 1502 | Score: -44.28 | Avg Score: -117.42 |\n",
"| Experiment: 1 | Episode: 1503 | Score: -130.05 | Avg Score: -117.89 |\n",
"| Experiment: 1 | Episode: 1504 | Score: -74.22 | Avg Score: -117.73 |\n",
"| Experiment: 1 | Episode: 1505 | Score: -74.58 | Avg Score: -117.41 |\n",
"| Experiment: 1 | Episode: 1506 | Score: -75.08 | Avg Score: -117.69 |\n",
"| Experiment: 1 | Episode: 1507 | Score: -8.16 | Avg Score: -116.86 |\n",
"| Experiment: 1 | Episode: 1508 | Score: -98.28 | Avg Score: -115.14 |\n",
"| Experiment: 1 | Episode: 1509 | Score: -114.0 | Avg Score: -115.67 |\n",
"| Experiment: 1 | Episode: 1510 | Score: -148.45 | Avg Score: -116.53 |\n",
"| Experiment: 1 | Episode: 1511 | Score: -169.07 | Avg Score: -117.73 |\n",
"| Experiment: 1 | Episode: 1512 | Score: -128.28 | Avg Score: -116.69 |\n",
"| Experiment: 1 | Episode: 1513 | Score: -135.1 | Avg Score: -116.74 |\n",
"| Experiment: 1 | Episode: 1514 | Score: -137.33 | Avg Score: -117.09 |\n",
"| Experiment: 1 | Episode: 1515 | Score: -314.52 | Avg Score: -118.91 |\n",
"| Experiment: 1 | Episode: 1516 | Score: -144.49 | Avg Score: -117.83 |\n",
"| Experiment: 1 | Episode: 1517 | Score: -80.52 | Avg Score: -117.14 |\n",
"| Experiment: 1 | Episode: 1518 | Score: -53.3 | Avg Score: -116.77 |\n",
"| Experiment: 1 | Episode: 1519 | Score: -76.79 | Avg Score: -116.91 |\n",
"| Experiment: 1 | Episode: 1520 | Score: -33.42 | Avg Score: -115.34 |\n",
"| Experiment: 1 | Episode: 1521 | Score: -104.63 | Avg Score: -115.37 |\n",
"| Experiment: 1 | Episode: 1522 | Score: -171.67 | Avg Score: -116.61 |\n",
"| Experiment: 1 | Episode: 1523 | Score: -74.29 | Avg Score: -116.74 |\n",
"| Experiment: 1 | Episode: 1524 | Score: -143.07 | Avg Score: -117.8 |\n",
"| Experiment: 1 | Episode: 1525 | Score: -60.41 | Avg Score: -117.34 |\n",
"| Experiment: 1 | Episode: 1526 | Score: -139.75 | Avg Score: -118.08 |\n",
"| Experiment: 1 | Episode: 1527 | Score: -118.52 | Avg Score: -118.38 |\n",
"| Experiment: 1 | Episode: 1528 | Score: -57.73 | Avg Score: -117.89 |\n",
"| Experiment: 1 | Episode: 1529 | Score: -31.84 | Avg Score: -117.17 |\n",
"| Experiment: 1 | Episode: 1530 | Score: -83.25 | Avg Score: -116.8 |\n",
"| Experiment: 1 | Episode: 1531 | Score: -86.89 | Avg Score: -112.97 |\n",
"| Experiment: 1 | Episode: 1532 | Score: -232.53 | Avg Score: -114.65 |\n",
"| Experiment: 1 | Episode: 1533 | Score: -97.21 | Avg Score: -115.1 |\n",
"| Experiment: 1 | Episode: 1534 | Score: -85.08 | Avg Score: -115.35 |\n",
"| Experiment: 1 | Episode: 1535 | Score: -102.31 | Avg Score: -115.1 |\n",
"| Experiment: 1 | Episode: 1536 | Score: -115.47 | Avg Score: -114.93 |\n",
"| Experiment: 1 | Episode: 1537 | Score: -44.96 | Avg Score: -114.3 |\n",
"| Experiment: 1 | Episode: 1538 | Score: -132.48 | Avg Score: -114.61 |\n",
"| Experiment: 1 | Episode: 1539 | Score: -62.79 | Avg Score: -116.2 |\n",
"| Experiment: 1 | Episode: 1540 | Score: -77.4 | Avg Score: -115.78 |\n",
"| Experiment: 1 | Episode: 1541 | Score: -124.65 | Avg Score: -115.39 |\n",
"| Experiment: 1 | Episode: 1542 | Score: -4.06 | Avg Score: -114.96 |\n",
"| Experiment: 1 | Episode: 1543 | Score: -82.3 | Avg Score: -114.87 |\n",
"| Experiment: 1 | Episode: 1544 | Score: -70.94 | Avg Score: -114.23 |\n",
"| Experiment: 1 | Episode: 1545 | Score: -138.5 | Avg Score: -114.61 |\n",
"| Experiment: 1 | Episode: 1546 | Score: -137.89 | Avg Score: -114.8 |\n",
"| Experiment: 1 | Episode: 1547 | Score: -67.89 | Avg Score: -114.89 |\n",
"| Experiment: 1 | Episode: 1548 | Score: -98.66 | Avg Score: -114.2 |\n",
"| Experiment: 1 | Episode: 1549 | Score: -82.27 | Avg Score: -113.42 |\n",
"| Experiment: 1 | Episode: 1550 | Score: -181.61 | Avg Score: -112.12 |\n",
"| Experiment: 1 | Episode: 1551 | Score: -156.26 | Avg Score: -110.55 |\n",
"| Experiment: 1 | Episode: 1552 | Score: -70.28 | Avg Score: -110.61 |\n",
"| Experiment: 1 | Episode: 1553 | Score: -87.02 | Avg Score: -111.11 |\n",
"| Experiment: 1 | Episode: 1554 | Score: -222.71 | Avg Score: -109.92 |\n",
"| Experiment: 1 | Episode: 1555 | Score: -77.43 | Avg Score: -110.07 |\n",
"| Experiment: 1 | Episode: 1556 | Score: -57.34 | Avg Score: -109.6 |\n",
"| Experiment: 1 | Episode: 1557 | Score: -63.54 | Avg Score: -109.8 |\n",
"| Experiment: 1 | Episode: 1558 | Score: -45.34 | Avg Score: -109.39 |\n",
"| Experiment: 1 | Episode: 1559 | Score: -113.66 | Avg Score: -109.34 |\n",
"| Experiment: 1 | Episode: 1560 | Score: -118.11 | Avg Score: -109.56 |\n",
"| Experiment: 1 | Episode: 1561 | Score: -183.99 | Avg Score: -110.46 |\n",
"| Experiment: 1 | Episode: 1562 | Score: 27.39 | Avg Score: -109.47 |\n",
"| Experiment: 1 | Episode: 1563 | Score: -69.96 | Avg Score: -109.0 |\n",
"| Experiment: 1 | Episode: 1564 | Score: -98.32 | Avg Score: -108.9 |\n",
"| Experiment: 1 | Episode: 1565 | Score: -77.92 | Avg Score: -108.6 |\n",
"| Experiment: 1 | Episode: 1566 | Score: -352.0 | Avg Score: -110.18 |\n",
"| Experiment: 1 | Episode: 1567 | Score: -135.87 | Avg Score: -110.92 |\n",
"| Experiment: 1 | Episode: 1568 | Score: -43.43 | Avg Score: -110.13 |\n",
"| Experiment: 1 | Episode: 1569 | Score: -153.08 | Avg Score: -110.77 |\n",
"| Experiment: 1 | Episode: 1570 | Score: -28.32 | Avg Score: -110.09 |\n",
"| Experiment: 1 | Episode: 1571 | Score: -131.81 | Avg Score: -109.93 |\n",
"| Experiment: 1 | Episode: 1572 | Score: -35.61 | Avg Score: -107.76 |\n",
"| Experiment: 1 | Episode: 1573 | Score: -66.32 | Avg Score: -106.36 |\n",
"| Experiment: 1 | Episode: 1574 | Score: -79.19 | Avg Score: -105.62 |\n",
"| Experiment: 1 | Episode: 1575 | Score: -81.62 | Avg Score: -105.66 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 | Episode: 1576 | Score: -76.96 | Avg Score: -103.98 |\n",
"| Experiment: 1 | Episode: 1577 | Score: -150.47 | Avg Score: -103.63 |\n",
"| Experiment: 1 | Episode: 1578 | Score: -94.41 | Avg Score: -102.91 |\n",
"| Experiment: 1 | Episode: 1579 | Score: -44.59 | Avg Score: -100.49 |\n",
"| Experiment: 1 | Episode: 1580 | Score: -86.3 | Avg Score: -99.57 |\n",
"| Experiment: 1 | Episode: 1581 | Score: -18.28 | Avg Score: -98.89 |\n",
"| Experiment: 1 | Episode: 1582 | Score: -38.38 | Avg Score: -98.61 |\n",
"| Experiment: 1 | Episode: 1583 | Score: -242.85 | Avg Score: -100.08 |\n",
"| Experiment: 1 | Episode: 1584 | Score: -159.71 | Avg Score: -100.58 |\n",
"| Experiment: 1 | Episode: 1585 | Score: -85.6 | Avg Score: -100.53 |\n",
"| Experiment: 1 | Episode: 1586 | Score: -82.11 | Avg Score: -100.45 |\n",
"| Experiment: 1 | Episode: 1587 | Score: -56.38 | Avg Score: -99.61 |\n",
"| Experiment: 1 | Episode: 1588 | Score: -80.04 | Avg Score: -100.05 |\n",
"| Experiment: 1 | Episode: 1589 | Score: -117.46 | Avg Score: -100.53 |\n",
"| Experiment: 1 | Episode: 1590 | Score: -46.66 | Avg Score: -99.49 |\n",
"| Experiment: 1 | Episode: 1591 | Score: -258.06 | Avg Score: -101.57 |\n",
"| Experiment: 1 | Episode: 1592 | Score: -29.85 | Avg Score: -101.41 |\n",
"| Experiment: 1 | Episode: 1593 | Score: -191.84 | Avg Score: -101.85 |\n",
"| Experiment: 1 | Episode: 1594 | Score: -40.75 | Avg Score: -101.02 |\n",
"| Experiment: 1 | Episode: 1595 | Score: -211.71 | Avg Score: -101.95 |\n",
"| Experiment: 1 | Episode: 1596 | Score: -156.4 | Avg Score: -101.79 |\n",
"| Experiment: 1 | Episode: 1597 | Score: -71.5 | Avg Score: -101.71 |\n",
"| Experiment: 1 | Episode: 1598 | Score: -41.21 | Avg Score: -101.81 |\n",
"| Experiment: 1 | Episode: 1599 | Score: -165.84 | Avg Score: -102.95 |\n",
"| Experiment: 1 | Episode: 1600 | Score: -87.79 | Avg Score: -103.14 |\n",
"| Experiment: 1 | Episode: 1601 | Score: -128.9 | Avg Score: -103.37 |\n",
"| Experiment: 1 | Episode: 1602 | Score: -65.97 | Avg Score: -103.58 |\n",
"| Experiment: 1 | Episode: 1603 | Score: -9.76 | Avg Score: -102.38 |\n",
"| Experiment: 1 | Episode: 1604 | Score: -69.52 | Avg Score: -102.33 |\n",
"| Experiment: 1 | Episode: 1605 | Score: -246.84 | Avg Score: -104.06 |\n",
"| Experiment: 1 | Episode: 1606 | Score: -65.66 | Avg Score: -103.96 |\n",
"| Experiment: 1 | Episode: 1607 | Score: -66.18 | Avg Score: -104.54 |\n",
"| Experiment: 1 | Episode: 1608 | Score: -99.8 | Avg Score: -104.56 |\n",
"| Experiment: 1 | Episode: 1609 | Score: -97.98 | Avg Score: -104.4 |\n",
"| Experiment: 1 | Episode: 1610 | Score: -81.5 | Avg Score: -103.73 |\n",
"| Experiment: 1 | Episode: 1611 | Score: -34.74 | Avg Score: -102.39 |\n",
"| Experiment: 1 | Episode: 1612 | Score: -78.96 | Avg Score: -101.89 |\n",
"| Experiment: 1 | Episode: 1613 | Score: -156.51 | Avg Score: -102.11 |\n",
"| Experiment: 1 | Episode: 1614 | Score: -108.13 | Avg Score: -101.81 |\n",
"| Experiment: 1 | Episode: 1615 | Score: -220.86 | Avg Score: -100.88 |\n",
"| Experiment: 1 | Episode: 1616 | Score: -201.94 | Avg Score: -101.45 |\n",
"| Experiment: 1 | Episode: 1617 | Score: -62.95 | Avg Score: -101.28 |\n",
"| Experiment: 1 | Episode: 1618 | Score: -106.24 | Avg Score: -101.81 |\n",
"| Experiment: 1 | Episode: 1619 | Score: -99.13 | Avg Score: -102.03 |\n",
"| Experiment: 1 | Episode: 1620 | Score: -44.65 | Avg Score: -102.14 |\n",
"| Experiment: 1 | Episode: 1621 | Score: -142.89 | Avg Score: -102.52 |\n",
"| Experiment: 1 | Episode: 1622 | Score: -30.65 | Avg Score: -101.11 |\n",
"| Experiment: 1 | Episode: 1623 | Score: -67.3 | Avg Score: -101.04 |\n",
"| Experiment: 1 | Episode: 1624 | Score: -32.06 | Avg Score: -99.93 |\n",
"| Experiment: 1 | Episode: 1625 | Score: -238.92 | Avg Score: -101.72 |\n",
"| Experiment: 1 | Episode: 1626 | Score: -66.78 | Avg Score: -100.99 |\n",
"| Experiment: 1 | Episode: 1627 | Score: -36.01 | Avg Score: -100.16 |\n",
"| Experiment: 1 | Episode: 1628 | Score: -169.5 | Avg Score: -101.28 |\n",
"| Experiment: 1 | Episode: 1629 | Score: -224.68 | Avg Score: -103.21 |\n",
"| Experiment: 1 | Episode: 1630 | Score: -99.71 | Avg Score: -103.38 |\n",
"| Experiment: 1 | Episode: 1631 | Score: -33.44 | Avg Score: -102.84 |\n",
"| Experiment: 1 | Episode: 1632 | Score: -250.14 | Avg Score: -103.02 |\n",
"| Experiment: 1 | Episode: 1633 | Score: -214.91 | Avg Score: -104.19 |\n",
"| Experiment: 1 | Episode: 1634 | Score: -310.49 | Avg Score: -106.45 |\n",
"| Experiment: 1 | Episode: 1635 | Score: -235.69 | Avg Score: -107.78 |\n",
"| Experiment: 1 | Episode: 1636 | Score: -139.0 | Avg Score: -108.02 |\n",
"| Experiment: 1 | Episode: 1637 | Score: -97.9 | Avg Score: -108.55 |\n",
"| Experiment: 1 | Episode: 1638 | Score: -54.99 | Avg Score: -107.77 |\n",
"| Experiment: 1 | Episode: 1639 | Score: -83.09 | Avg Score: -107.97 |\n",
"| Experiment: 1 | Episode: 1640 | Score: -128.4 | Avg Score: -108.48 |\n",
"| Experiment: 1 | Episode: 1641 | Score: -47.25 | Avg Score: -107.71 |\n",
"| Experiment: 1 | Episode: 1642 | Score: -191.42 | Avg Score: -109.58 |\n",
"| Experiment: 1 | Episode: 1643 | Score: -78.54 | Avg Score: -109.55 |\n",
"| Experiment: 1 | Episode: 1644 | Score: -31.66 | Avg Score: -109.15 |\n",
"| Experiment: 1 | Episode: 1645 | Score: -187.45 | Avg Score: -109.64 |\n",
"| Experiment: 1 | Episode: 1646 | Score: -201.45 | Avg Score: -110.28 |\n",
"| Experiment: 1 | Episode: 1647 | Score: -208.52 | Avg Score: -111.68 |\n",
"| Experiment: 1 | Episode: 1648 | Score: -43.07 | Avg Score: -111.13 |\n",
"| Experiment: 1 | Episode: 1649 | Score: -94.56 | Avg Score: -111.25 |\n",
"| Experiment: 1 | Episode: 1650 | Score: -21.88 | Avg Score: -109.65 |\n",
"| Experiment: 1 | Episode: 1651 | Score: -97.9 | Avg Score: -109.07 |\n",
"| Experiment: 1 | Episode: 1652 | Score: -193.3 | Avg Score: -110.3 |\n",
"| Experiment: 1 | Episode: 1653 | Score: -39.03 | Avg Score: -109.82 |\n",
"| Experiment: 1 | Episode: 1654 | Score: -83.42 | Avg Score: -108.43 |\n",
"| Experiment: 1 | Episode: 1655 | Score: -172.09 | Avg Score: -109.38 |\n",
"| Experiment: 1 | Episode: 1656 | Score: -291.7 | Avg Score: -111.72 |\n",
"| Experiment: 1 | Episode: 1657 | Score: -24.93 | Avg Score: -111.33 |\n",
"| Experiment: 1 | Episode: 1658 | Score: -76.12 | Avg Score: -111.64 |\n",
"| Experiment: 1 | Episode: 1659 | Score: -77.91 | Avg Score: -111.28 |\n",
"| Experiment: 1 | Episode: 1660 | Score: -274.97 | Avg Score: -112.85 |\n",
"| Experiment: 1 | Episode: 1661 | Score: -113.76 | Avg Score: -112.15 |\n",
"| Experiment: 1 | Episode: 1662 | Score: -69.11 | Avg Score: -113.11 |\n",
"| Experiment: 1 | Episode: 1663 | Score: -168.98 | Avg Score: -114.1 |\n",
"| Experiment: 1 | Episode: 1664 | Score: -88.44 | Avg Score: -114.01 |\n",
"| Experiment: 1 | Episode: 1665 | Score: -55.73 | Avg Score: -113.78 |\n",
"| Experiment: 1 | Episode: 1666 | Score: -86.41 | Avg Score: -111.13 |\n",
"| Experiment: 1 | Episode: 1667 | Score: -76.23 | Avg Score: -110.53 |\n",
"| Experiment: 1 | Episode: 1668 | Score: -18.02 | Avg Score: -110.28 |\n",
"| Experiment: 1 | Episode: 1669 | Score: -117.1 | Avg Score: -109.92 |\n",
"| Experiment: 1 | Episode: 1670 | Score: 13.23 | Avg Score: -109.5 |\n",
"| Experiment: 1 | Episode: 1671 | Score: -58.76 | Avg Score: -108.77 |\n",
"| Experiment: 1 | Episode: 1672 | Score: -32.82 | Avg Score: -108.74 |\n",
"| Experiment: 1 | Episode: 1673 | Score: -76.1 | Avg Score: -108.84 |\n",
"| Experiment: 1 | Episode: 1674 | Score: -194.59 | Avg Score: -110.0 |\n",
"| Experiment: 1 | Episode: 1675 | Score: -153.77 | Avg Score: -110.72 |\n",
"| Experiment: 1 | Episode: 1676 | Score: -147.79 | Avg Score: -111.43 |\n",
"| Experiment: 1 | Episode: 1677 | Score: -143.78 | Avg Score: -111.36 |\n",
"| Experiment: 1 | Episode: 1678 | Score: -118.17 | Avg Score: -111.6 |\n",
"| Experiment: 1 | Episode: 1679 | Score: 21.35 | Avg Score: -110.94 |\n",
"| Experiment: 1 | Episode: 1680 | Score: -104.81 | Avg Score: -111.12 |\n",
"| Experiment: 1 | Episode: 1681 | Score: -41.6 | Avg Score: -111.35 |\n",
"| Experiment: 1 | Episode: 1682 | Score: -172.1 | Avg Score: -112.69 |\n",
"| Experiment: 1 | Episode: 1683 | Score: -213.09 | Avg Score: -112.39 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 | Episode: 1684 | Score: -94.86 | Avg Score: -111.75 |\n",
"| Experiment: 1 | Episode: 1685 | Score: -77.45 | Avg Score: -111.66 |\n",
"| Experiment: 1 | Episode: 1686 | Score: -26.62 | Avg Score: -111.11 |\n",
"| Experiment: 1 | Episode: 1687 | Score: -23.41 | Avg Score: -110.78 |\n",
"| Experiment: 1 | Episode: 1688 | Score: -146.99 | Avg Score: -111.45 |\n",
"| Experiment: 1 | Episode: 1689 | Score: -81.88 | Avg Score: -111.09 |\n",
"| Experiment: 1 | Episode: 1690 | Score: -58.72 | Avg Score: -111.21 |\n",
"| Experiment: 1 | Episode: 1691 | Score: -206.33 | Avg Score: -110.7 |\n",
"| Experiment: 1 | Episode: 1692 | Score: -132.65 | Avg Score: -111.72 |\n",
"| Experiment: 1 | Episode: 1693 | Score: -74.79 | Avg Score: -110.55 |\n",
"| Experiment: 1 | Episode: 1694 | Score: -81.18 | Avg Score: -110.96 |\n",
"| Experiment: 1 | Episode: 1695 | Score: -71.23 | Avg Score: -109.55 |\n",
"| Experiment: 1 | Episode: 1696 | Score: -40.01 | Avg Score: -108.39 |\n",
"| Experiment: 1 | Episode: 1697 | Score: -51.26 | Avg Score: -108.19 |\n",
"| Experiment: 1 | Episode: 1698 | Score: -129.89 | Avg Score: -109.07 |\n",
"| Experiment: 1 | Episode: 1699 | Score: -84.58 | Avg Score: -108.26 |\n",
"| Experiment: 1 | Episode: 1700 | Score: -109.46 | Avg Score: -108.48 |\n",
"| Experiment: 1 | Episode: 1701 | Score: -59.74 | Avg Score: -107.79 |\n",
"| Experiment: 1 | Episode: 1702 | Score: -15.36 | Avg Score: -107.28 |\n",
"| Experiment: 1 | Episode: 1703 | Score: -117.2 | Avg Score: -108.36 |\n",
"| Experiment: 1 | Episode: 1704 | Score: -79.74 | Avg Score: -108.46 |\n",
"| Experiment: 1 | Episode: 1705 | Score: -125.74 | Avg Score: -107.25 |\n",
"| Experiment: 1 | Episode: 1706 | Score: -85.09 | Avg Score: -107.44 |\n",
"| Experiment: 1 | Episode: 1707 | Score: -121.6 | Avg Score: -107.99 |\n",
"| Experiment: 1 | Episode: 1708 | Score: -111.26 | Avg Score: -108.11 |\n",
"| Experiment: 1 | Episode: 1709 | Score: -84.32 | Avg Score: -107.97 |\n",
"| Experiment: 1 | Episode: 1710 | Score: -114.6 | Avg Score: -108.3 |\n",
"| Experiment: 1 | Episode: 1711 | Score: -61.66 | Avg Score: -108.57 |\n",
"| Experiment: 1 | Episode: 1712 | Score: -108.9 | Avg Score: -108.87 |\n",
"| Experiment: 1 | Episode: 1713 | Score: -80.12 | Avg Score: -108.11 |\n",
"| Experiment: 1 | Episode: 1714 | Score: -71.12 | Avg Score: -107.74 |\n",
"| Experiment: 1 | Episode: 1715 | Score: -82.83 | Avg Score: -106.36 |\n",
"| Experiment: 1 | Episode: 1716 | Score: -58.93 | Avg Score: -104.93 |\n",
"| Experiment: 1 | Episode: 1717 | Score: -149.82 | Avg Score: -105.8 |\n",
"| Experiment: 1 | Episode: 1718 | Score: -158.43 | Avg Score: -106.32 |\n",
"| Experiment: 1 | Episode: 1719 | Score: -51.99 | Avg Score: -105.85 |\n",
"| Experiment: 1 | Episode: 1720 | Score: -64.6 | Avg Score: -106.05 |\n",
"| Experiment: 1 | Episode: 1721 | Score: -24.45 | Avg Score: -104.86 |\n",
"| Experiment: 1 | Episode: 1722 | Score: -158.16 | Avg Score: -106.14 |\n",
"| Experiment: 1 | Episode: 1723 | Score: -91.46 | Avg Score: -106.38 |\n",
"| Experiment: 1 | Episode: 1724 | Score: -151.8 | Avg Score: -107.58 |\n",
"| Experiment: 1 | Episode: 1725 | Score: -106.71 | Avg Score: -106.25 |\n",
"| Experiment: 1 | Episode: 1726 | Score: -72.22 | Avg Score: -106.31 |\n",
"| Experiment: 1 | Episode: 1727 | Score: -105.85 | Avg Score: -107.01 |\n",
"| Experiment: 1 | Episode: 1728 | Score: -43.3 | Avg Score: -105.74 |\n",
"| Experiment: 1 | Episode: 1729 | Score: -61.7 | Avg Score: -104.12 |\n",
"| Experiment: 1 | Episode: 1730 | Score: -88.94 | Avg Score: -104.01 |\n",
"| Experiment: 1 | Episode: 1731 | Score: -86.31 | Avg Score: -104.54 |\n",
"| Experiment: 1 | Episode: 1732 | Score: -83.53 | Avg Score: -102.87 |\n",
"| Experiment: 1 | Episode: 1733 | Score: -125.7 | Avg Score: -101.98 |\n",
"| Experiment: 1 | Episode: 1734 | Score: -90.2 | Avg Score: -99.78 |\n",
"| Experiment: 1 | Episode: 1735 | Score: -58.86 | Avg Score: -98.01 |\n",
"| Experiment: 1 | Episode: 1736 | Score: -108.67 | Avg Score: -97.7 |\n",
"| Experiment: 1 | Episode: 1737 | Score: -53.56 | Avg Score: -97.26 |\n",
"| Experiment: 1 | Episode: 1738 | Score: -49.21 | Avg Score: -97.2 |\n",
"| Experiment: 1 | Episode: 1739 | Score: -72.28 | Avg Score: -97.09 |\n",
"| Experiment: 1 | Episode: 1740 | Score: -232.11 | Avg Score: -98.13 |\n",
"| Experiment: 1 | Episode: 1741 | Score: 8.39 | Avg Score: -97.57 |\n",
"| Experiment: 1 | Episode: 1742 | Score: -27.88 | Avg Score: -95.94 |\n",
"| Experiment: 1 | Episode: 1743 | Score: -97.17 | Avg Score: -96.13 |\n",
"| Experiment: 1 | Episode: 1744 | Score: -26.22 | Avg Score: -96.07 |\n",
"| Experiment: 1 | Episode: 1745 | Score: -37.45 | Avg Score: -94.57 |\n",
"| Experiment: 1 | Episode: 1746 | Score: -49.25 | Avg Score: -93.05 |\n",
"| Experiment: 1 | Episode: 1747 | Score: -107.34 | Avg Score: -92.04 |\n",
"| Experiment: 1 | Episode: 1748 | Score: -127.96 | Avg Score: -92.89 |\n",
"| Experiment: 1 | Episode: 1749 | Score: -127.3 | Avg Score: -93.21 |\n",
"| Experiment: 1 | Episode: 1750 | Score: -119.6 | Avg Score: -94.19 |\n",
"| Experiment: 1 | Episode: 1751 | Score: -135.48 | Avg Score: -94.57 |\n",
"| Experiment: 1 | Episode: 1752 | Score: -93.76 | Avg Score: -93.57 |\n",
"| Experiment: 1 | Episode: 1753 | Score: -106.61 | Avg Score: -94.25 |\n",
"| Experiment: 1 | Episode: 1754 | Score: -81.27 | Avg Score: -94.23 |\n",
"| Experiment: 1 | Episode: 1755 | Score: -51.0 | Avg Score: -93.01 |\n",
"| Experiment: 1 | Episode: 1756 | Score: -283.28 | Avg Score: -92.93 |\n",
"| Experiment: 1 | Episode: 1757 | Score: -104.92 | Avg Score: -93.73 |\n",
"| Experiment: 1 | Episode: 1758 | Score: -50.17 | Avg Score: -93.47 |\n",
"| Experiment: 1 | Episode: 1759 | Score: -119.43 | Avg Score: -93.89 |\n",
"| Experiment: 1 | Episode: 1760 | Score: -142.56 | Avg Score: -92.56 |\n",
"| Experiment: 1 | Episode: 1761 | Score: -241.48 | Avg Score: -93.84 |\n",
"| Experiment: 1 | Episode: 1762 | Score: -77.46 | Avg Score: -93.92 |\n",
"| Experiment: 1 | Episode: 1763 | Score: -197.97 | Avg Score: -94.21 |\n",
"| Experiment: 1 | Episode: 1764 | Score: -73.36 | Avg Score: -94.06 |\n",
"| Experiment: 1 | Episode: 1765 | Score: -65.2 | Avg Score: -94.16 |\n",
"| Experiment: 1 | Episode: 1766 | Score: -68.49 | Avg Score: -93.98 |\n",
"| Experiment: 1 | Episode: 1767 | Score: -76.88 | Avg Score: -93.98 |\n",
"| Experiment: 1 | Episode: 1768 | Score: -96.85 | Avg Score: -94.77 |\n",
"| Experiment: 1 | Episode: 1769 | Score: -272.33 | Avg Score: -96.32 |\n",
"| Experiment: 1 | Episode: 1770 | Score: -51.58 | Avg Score: -96.97 |\n",
"| Experiment: 1 | Episode: 1771 | Score: -82.96 | Avg Score: -97.21 |\n",
"| Experiment: 1 | Episode: 1772 | Score: -59.31 | Avg Score: -97.48 |\n",
"| Experiment: 1 | Episode: 1773 | Score: -55.45 | Avg Score: -97.27 |\n",
"| Experiment: 1 | Episode: 1774 | Score: -101.09 | Avg Score: -96.34 |\n",
"| Experiment: 1 | Episode: 1775 | Score: -57.89 | Avg Score: -95.38 |\n",
"| Experiment: 1 | Episode: 1776 | Score: -44.88 | Avg Score: -94.35 |\n",
"| Experiment: 1 | Episode: 1777 | Score: -88.83 | Avg Score: -93.8 |\n",
"| Experiment: 1 | Episode: 1778 | Score: -187.77 | Avg Score: -94.5 |\n",
"| Experiment: 1 | Episode: 1779 | Score: -58.8 | Avg Score: -95.3 |\n",
"| Experiment: 1 | Episode: 1780 | Score: -127.68 | Avg Score: -95.53 |\n",
"| Experiment: 1 | Episode: 1781 | Score: -16.82 | Avg Score: -95.28 |\n",
"| Experiment: 1 | Episode: 1782 | Score: -75.49 | Avg Score: -94.31 |\n",
"| Experiment: 1 | Episode: 1783 | Score: -105.4 | Avg Score: -93.24 |\n",
"| Experiment: 1 | Episode: 1784 | Score: -142.33 | Avg Score: -93.71 |\n",
"| Experiment: 1 | Episode: 1785 | Score: -63.6 | Avg Score: -93.57 |\n",
"| Experiment: 1 | Episode: 1786 | Score: -53.18 | Avg Score: -93.84 |\n",
"| Experiment: 1 | Episode: 1787 | Score: -50.19 | Avg Score: -94.11 |\n",
"| Experiment: 1 | Episode: 1788 | Score: -231.09 | Avg Score: -94.95 |\n",
"| Experiment: 1 | Episode: 1789 | Score: -169.94 | Avg Score: -95.83 |\n",
"| Experiment: 1 | Episode: 1790 | Score: -189.04 | Avg Score: -97.13 |\n",
"| Experiment: 1 | Episode: 1791 | Score: -70.95 | Avg Score: -95.78 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 | Episode: 1792 | Score: -55.1 | Avg Score: -95.0 |\n",
"| Experiment: 1 | Episode: 1793 | Score: -96.33 | Avg Score: -95.22 |\n",
"| Experiment: 1 | Episode: 1794 | Score: -13.52 | Avg Score: -94.54 |\n",
"| Experiment: 1 | Episode: 1795 | Score: -51.27 | Avg Score: -94.34 |\n",
"| Experiment: 1 | Episode: 1796 | Score: -40.94 | Avg Score: -94.35 |\n",
"| Experiment: 1 | Episode: 1797 | Score: 49.49 | Avg Score: -93.34 |\n",
"| Experiment: 1 | Episode: 1798 | Score: -159.17 | Avg Score: -93.63 |\n",
"| Experiment: 1 | Episode: 1799 | Score: -93.91 | Avg Score: -93.73 |\n",
"| Experiment: 1 | Episode: 1800 | Score: -255.68 | Avg Score: -95.19 |\n",
"| Experiment: 1 | Episode: 1801 | Score: -230.95 | Avg Score: -96.9 |\n",
"| Experiment: 1 | Episode: 1802 | Score: -74.16 | Avg Score: -97.49 |\n",
"| Experiment: 1 | Episode: 1803 | Score: -116.4 | Avg Score: -97.48 |\n",
"| Experiment: 1 | Episode: 1804 | Score: -63.99 | Avg Score: -97.32 |\n",
"| Experiment: 1 | Episode: 1805 | Score: -107.1 | Avg Score: -97.14 |\n",
"| Experiment: 1 | Episode: 1806 | Score: -126.9 | Avg Score: -97.56 |\n",
"| Experiment: 1 | Episode: 1807 | Score: -70.96 | Avg Score: -97.05 |\n",
"| Experiment: 1 | Episode: 1808 | Score: -89.18 | Avg Score: -96.83 |\n",
"| Experiment: 1 | Episode: 1809 | Score: -108.76 | Avg Score: -97.07 |\n",
"| Experiment: 1 | Episode: 1810 | Score: -37.04 | Avg Score: -96.3 |\n",
"| Experiment: 1 | Episode: 1811 | Score: -30.17 | Avg Score: -95.98 |\n",
"| Experiment: 1 | Episode: 1812 | Score: -57.89 | Avg Score: -95.47 |\n",
"| Experiment: 1 | Episode: 1813 | Score: -73.05 | Avg Score: -95.4 |\n",
"| Experiment: 1 | Episode: 1814 | Score: -47.38 | Avg Score: -95.16 |\n",
"| Experiment: 1 | Episode: 1815 | Score: -62.96 | Avg Score: -94.97 |\n",
"| Experiment: 1 | Episode: 1816 | Score: -65.05 | Avg Score: -95.03 |\n",
"| Experiment: 1 | Episode: 1817 | Score: -156.67 | Avg Score: -95.1 |\n",
"| Experiment: 1 | Episode: 1818 | Score: -53.13 | Avg Score: -94.04 |\n",
"| Experiment: 1 | Episode: 1819 | Score: -293.37 | Avg Score: -96.46 |\n",
"| Experiment: 1 | Episode: 1820 | Score: -136.28 | Avg Score: -97.17 |\n",
"| Experiment: 1 | Episode: 1821 | Score: -248.61 | Avg Score: -99.42 |\n",
"| Experiment: 1 | Episode: 1822 | Score: -94.37 | Avg Score: -98.78 |\n",
"| Experiment: 1 | Episode: 1823 | Score: -54.87 | Avg Score: -98.41 |\n",
"| Experiment: 1 | Episode: 1824 | Score: -73.38 | Avg Score: -97.63 |\n",
"| Experiment: 1 | Episode: 1825 | Score: -86.96 | Avg Score: -97.43 |\n",
"| Experiment: 1 | Episode: 1826 | Score: -112.07 | Avg Score: -97.83 |\n",
"| Experiment: 1 | Episode: 1827 | Score: -89.41 | Avg Score: -97.66 |\n",
"| Experiment: 1 | Episode: 1828 | Score: -133.65 | Avg Score: -98.57 |\n",
"| Experiment: 1 | Episode: 1829 | Score: -52.85 | Avg Score: -98.48 |\n",
"| Experiment: 1 | Episode: 1830 | Score: -81.38 | Avg Score: -98.4 |\n",
"| Experiment: 1 | Episode: 1831 | Score: -22.55 | Avg Score: -97.77 |\n",
"| Experiment: 1 | Episode: 1832 | Score: -45.38 | Avg Score: -97.38 |\n",
"| Experiment: 1 | Episode: 1833 | Score: -35.75 | Avg Score: -96.48 |\n",
"| Experiment: 1 | Episode: 1834 | Score: -56.97 | Avg Score: -96.15 |\n",
"| Experiment: 1 | Episode: 1835 | Score: -40.25 | Avg Score: -95.97 |\n",
"| Experiment: 1 | Episode: 1836 | Score: -90.87 | Avg Score: -95.79 |\n",
"| Experiment: 1 | Episode: 1837 | Score: -120.68 | Avg Score: -96.46 |\n",
"| Experiment: 1 | Episode: 1838 | Score: -76.12 | Avg Score: -96.73 |\n",
"| Experiment: 1 | Episode: 1839 | Score: -19.56 | Avg Score: -96.2 |\n",
"| Experiment: 1 | Episode: 1840 | Score: 17.55 | Avg Score: -93.7 |\n",
"| Experiment: 1 | Episode: 1841 | Score: -3.05 | Avg Score: -93.82 |\n",
"| Experiment: 1 | Episode: 1842 | Score: -43.89 | Avg Score: -93.98 |\n",
"| Experiment: 1 | Episode: 1843 | Score: -78.78 | Avg Score: -93.8 |\n",
"| Experiment: 1 | Episode: 1844 | Score: -67.37 | Avg Score: -94.21 |\n",
"| Experiment: 1 | Episode: 1845 | Score: -46.23 | Avg Score: -94.29 |\n",
"| Experiment: 1 | Episode: 1846 | Score: -27.57 | Avg Score: -94.08 |\n",
"| Experiment: 1 | Episode: 1847 | Score: -80.66 | Avg Score: -93.81 |\n",
"| Experiment: 1 | Episode: 1848 | Score: -149.94 | Avg Score: -94.03 |\n",
"| Experiment: 1 | Episode: 1849 | Score: -45.97 | Avg Score: -93.22 |\n",
"| Experiment: 1 | Episode: 1850 | Score: -182.02 | Avg Score: -93.84 |\n",
"| Experiment: 1 | Episode: 1851 | Score: -28.83 | Avg Score: -92.78 |\n",
"| Experiment: 1 | Episode: 1852 | Score: -28.97 | Avg Score: -92.13 |\n",
"| Experiment: 1 | Episode: 1853 | Score: -266.52 | Avg Score: -93.73 |\n",
"| Experiment: 1 | Episode: 1854 | Score: -85.38 | Avg Score: -93.77 |\n",
"| Experiment: 1 | Episode: 1855 | Score: -27.33 | Avg Score: -93.53 |\n",
"| Experiment: 1 | Episode: 1856 | Score: -100.7 | Avg Score: -91.71 |\n",
"| Experiment: 1 | Episode: 1857 | Score: -45.42 | Avg Score: -91.11 |\n",
"| Experiment: 1 | Episode: 1858 | Score: -183.35 | Avg Score: -92.44 |\n",
"| Experiment: 1 | Episode: 1859 | Score: -196.09 | Avg Score: -93.21 |\n",
"| Experiment: 1 | Episode: 1860 | Score: -95.69 | Avg Score: -92.74 |\n",
"| Experiment: 1 | Episode: 1861 | Score: -62.39 | Avg Score: -90.95 |\n",
"| Experiment: 1 | Episode: 1862 | Score: 26.21 | Avg Score: -89.91 |\n",
"| Experiment: 1 | Episode: 1863 | Score: -102.56 | Avg Score: -88.96 |\n",
"| Experiment: 1 | Episode: 1864 | Score: -84.41 | Avg Score: -89.07 |\n",
"| Experiment: 1 | Episode: 1865 | Score: -127.96 | Avg Score: -89.7 |\n",
"| Experiment: 1 | Episode: 1866 | Score: -137.84 | Avg Score: -90.39 |\n",
"| Experiment: 1 | Episode: 1867 | Score: -193.14 | Avg Score: -91.55 |\n",
"| Experiment: 1 | Episode: 1868 | Score: -117.74 | Avg Score: -91.76 |\n",
"| Experiment: 1 | Episode: 1869 | Score: -41.43 | Avg Score: -89.45 |\n",
"| Experiment: 1 | Episode: 1870 | Score: -80.19 | Avg Score: -89.74 |\n",
"| Experiment: 1 | Episode: 1871 | Score: -87.71 | Avg Score: -89.79 |\n",
"| Experiment: 1 | Episode: 1872 | Score: -84.37 | Avg Score: -90.04 |\n",
"| Experiment: 1 | Episode: 1873 | Score: -40.45 | Avg Score: -89.89 |\n",
"| Experiment: 1 | Episode: 1874 | Score: -167.52 | Avg Score: -90.55 |\n",
"| Experiment: 1 | Episode: 1875 | Score: -42.83 | Avg Score: -90.4 |\n",
"| Experiment: 1 | Episode: 1876 | Score: -257.26 | Avg Score: -92.52 |\n",
"| Experiment: 1 | Episode: 1877 | Score: -26.66 | Avg Score: -91.9 |\n",
"| Experiment: 1 | Episode: 1878 | Score: -194.34 | Avg Score: -91.97 |\n",
"| Experiment: 1 | Episode: 1879 | Score: -76.13 | Avg Score: -92.14 |\n",
"| Experiment: 1 | Episode: 1880 | Score: -35.96 | Avg Score: -91.22 |\n",
"| Experiment: 1 | Episode: 1881 | Score: -45.29 | Avg Score: -91.51 |\n",
"| Experiment: 1 | Episode: 1882 | Score: -70.81 | Avg Score: -91.46 |\n",
"| Experiment: 1 | Episode: 1883 | Score: -152.83 | Avg Score: -91.94 |\n",
"| Experiment: 1 | Episode: 1884 | Score: -27.53 | Avg Score: -90.79 |\n",
"| Experiment: 1 | Episode: 1885 | Score: -65.41 | Avg Score: -90.81 |\n",
"| Experiment: 1 | Episode: 1886 | Score: -41.94 | Avg Score: -90.69 |\n",
"| Experiment: 1 | Episode: 1887 | Score: -49.24 | Avg Score: -90.68 |\n",
"| Experiment: 1 | Episode: 1888 | Score: -84.94 | Avg Score: -89.22 |\n",
"| Experiment: 1 | Episode: 1889 | Score: -120.28 | Avg Score: -88.73 |\n",
"| Experiment: 1 | Episode: 1890 | Score: -18.27 | Avg Score: -87.02 |\n",
"| Experiment: 1 | Episode: 1891 | Score: -69.47 | Avg Score: -87.0 |\n",
"| Experiment: 1 | Episode: 1892 | Score: -68.35 | Avg Score: -87.14 |\n",
"| Experiment: 1 | Episode: 1893 | Score: -70.67 | Avg Score: -86.88 |\n",
"| Experiment: 1 | Episode: 1894 | Score: -236.76 | Avg Score: -89.11 |\n",
"| Experiment: 1 | Episode: 1895 | Score: -180.87 | Avg Score: -90.41 |\n",
"| Experiment: 1 | Episode: 1896 | Score: -294.38 | Avg Score: -92.94 |\n",
"| Experiment: 1 | Episode: 1897 | Score: -116.89 | Avg Score: -94.61 |\n",
"| Experiment: 1 | Episode: 1898 | Score: -67.82 | Avg Score: -93.69 |\n",
"| Experiment: 1 | Episode: 1899 | Score: -31.1 | Avg Score: -93.07 |\n",
"| Experiment: 1 | Episode: 1900 | Score: -63.47 | Avg Score: -91.14 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 1 | Episode: 1901 | Score: -29.03 | Avg Score: -89.12 |\n",
"| Experiment: 1 | Episode: 1902 | Score: -90.28 | Avg Score: -89.29 |\n",
"| Experiment: 1 | Episode: 1903 | Score: -39.74 | Avg Score: -88.52 |\n",
"| Experiment: 1 | Episode: 1904 | Score: -40.77 | Avg Score: -88.29 |\n",
"| Experiment: 1 | Episode: 1905 | Score: -94.01 | Avg Score: -88.16 |\n",
"| Experiment: 1 | Episode: 1906 | Score: -148.87 | Avg Score: -88.38 |\n",
"| Experiment: 1 | Episode: 1907 | Score: -183.79 | Avg Score: -89.5 |\n",
"| Experiment: 1 | Episode: 1908 | Score: -162.51 | Avg Score: -90.24 |\n",
"| Experiment: 1 | Episode: 1909 | Score: -414.39 | Avg Score: -93.29 |\n",
"| Experiment: 1 | Episode: 1910 | Score: -70.5 | Avg Score: -93.63 |\n",
"| Experiment: 1 | Episode: 1911 | Score: -211.38 | Avg Score: -95.44 |\n",
"| Experiment: 1 | Episode: 1912 | Score: -42.7 | Avg Score: -95.29 |\n",
"| Experiment: 1 | Episode: 1913 | Score: 32.78 | Avg Score: -94.23 |\n",
"| Experiment: 1 | Episode: 1914 | Score: -6.45 | Avg Score: -93.82 |\n",
"| Experiment: 1 | Episode: 1915 | Score: -29.87 | Avg Score: -93.49 |\n",
"| Experiment: 1 | Episode: 1916 | Score: -100.78 | Avg Score: -93.85 |\n",
"| Experiment: 1 | Episode: 1917 | Score: -84.81 | Avg Score: -93.13 |\n",
"| Experiment: 1 | Episode: 1918 | Score: -48.54 | Avg Score: -93.08 |\n",
"| Experiment: 1 | Episode: 1919 | Score: -69.04 | Avg Score: -90.84 |\n",
"| Experiment: 1 | Episode: 1920 | Score: -87.68 | Avg Score: -90.35 |\n",
"| Experiment: 1 | Episode: 1921 | Score: -101.98 | Avg Score: -88.89 |\n",
"| Experiment: 1 | Episode: 1922 | Score: 4.17 | Avg Score: -87.9 |\n",
"| Experiment: 1 | Episode: 1923 | Score: -66.54 | Avg Score: -88.02 |\n",
"| Experiment: 1 | Episode: 1924 | Score: -13.81 | Avg Score: -87.42 |\n",
"| Experiment: 1 | Episode: 1925 | Score: -139.15 | Avg Score: -87.94 |\n",
"| Experiment: 1 | Episode: 1926 | Score: -153.28 | Avg Score: -88.36 |\n",
"| Experiment: 1 | Episode: 1927 | Score: -107.99 | Avg Score: -88.54 |\n",
"| Experiment: 1 | Episode: 1928 | Score: -76.45 | Avg Score: -87.97 |\n",
"| Experiment: 1 | Episode: 1929 | Score: -49.82 | Avg Score: -87.94 |\n",
"| Experiment: 1 | Episode: 1930 | Score: -94.15 | Avg Score: -88.07 |\n",
"| Experiment: 1 | Episode: 1931 | Score: -54.43 | Avg Score: -88.39 |\n",
"| Experiment: 1 | Episode: 1932 | Score: -23.22 | Avg Score: -88.16 |\n",
"| Experiment: 1 | Episode: 1933 | Score: -84.69 | Avg Score: -88.65 |\n",
"| Experiment: 1 | Episode: 1934 | Score: -15.41 | Avg Score: -88.24 |\n",
"| Experiment: 1 | Episode: 1935 | Score: -28.94 | Avg Score: -88.13 |\n",
"| Experiment: 1 | Episode: 1936 | Score: -60.72 | Avg Score: -87.82 |\n",
"| Experiment: 1 | Episode: 1937 | Score: -93.36 | Avg Score: -87.55 |\n",
"| Experiment: 1 | Episode: 1938 | Score: -53.66 | Avg Score: -87.33 |\n",
"| Experiment: 1 | Episode: 1939 | Score: -104.59 | Avg Score: -88.18 |\n",
"| Experiment: 1 | Episode: 1940 | Score: -46.12 | Avg Score: -88.81 |\n",
"| Experiment: 1 | Episode: 1941 | Score: -31.36 | Avg Score: -89.1 |\n",
"| Experiment: 1 | Episode: 1942 | Score: -65.51 | Avg Score: -89.31 |\n",
"| Experiment: 1 | Episode: 1943 | Score: -286.75 | Avg Score: -91.39 |\n",
"| Experiment: 1 | Episode: 1944 | Score: -71.09 | Avg Score: -91.43 |\n",
"| Experiment: 1 | Episode: 1945 | Score: -92.17 | Avg Score: -91.89 |\n",
"| Experiment: 1 | Episode: 1946 | Score: -88.81 | Avg Score: -92.5 |\n",
"| Experiment: 1 | Episode: 1947 | Score: 3.17 | Avg Score: -91.66 |\n",
"| Experiment: 1 | Episode: 1948 | Score: -127.37 | Avg Score: -91.44 |\n",
"| Experiment: 1 | Episode: 1949 | Score: -176.8 | Avg Score: -92.75 |\n",
"| Experiment: 1 | Episode: 1950 | Score: -70.92 | Avg Score: -91.63 |\n",
"| Experiment: 1 | Episode: 1951 | Score: -150.97 | Avg Score: -92.86 |\n",
"| Experiment: 1 | Episode: 1952 | Score: -161.47 | Avg Score: -94.18 |\n",
"| Experiment: 1 | Episode: 1953 | Score: -14.86 | Avg Score: -91.66 |\n",
"| Experiment: 1 | Episode: 1954 | Score: -68.36 | Avg Score: -91.49 |\n",
"| Experiment: 1 | Episode: 1955 | Score: -123.94 | Avg Score: -92.46 |\n",
"| Experiment: 1 | Episode: 1956 | Score: -3.47 | Avg Score: -91.49 |\n",
"| Experiment: 1 | Episode: 1957 | Score: -51.63 | Avg Score: -91.55 |\n",
"| Experiment: 1 | Episode: 1958 | Score: -44.55 | Avg Score: -90.16 |\n",
"| Experiment: 1 | Episode: 1959 | Score: -30.51 | Avg Score: -88.51 |\n",
"| Experiment: 1 | Episode: 1960 | Score: -109.72 | Avg Score: -88.65 |\n",
"| Experiment: 1 | Episode: 1961 | Score: -189.25 | Avg Score: -89.91 |\n",
"| Experiment: 1 | Episode: 1962 | Score: -36.92 | Avg Score: -90.55 |\n",
"| Experiment: 1 | Episode: 1963 | Score: -29.79 | Avg Score: -89.82 |\n",
"| Experiment: 1 | Episode: 1964 | Score: -104.97 | Avg Score: -90.02 |\n",
"| Experiment: 1 | Episode: 1965 | Score: -63.34 | Avg Score: -89.38 |\n",
"| Experiment: 1 | Episode: 1966 | Score: -54.29 | Avg Score: -88.54 |\n",
"| Experiment: 1 | Episode: 1967 | Score: -110.51 | Avg Score: -87.72 |\n",
"| Experiment: 1 | Episode: 1968 | Score: -100.32 | Avg Score: -87.54 |\n",
"| Experiment: 1 | Episode: 1969 | Score: -250.72 | Avg Score: -89.63 |\n",
"| Experiment: 1 | Episode: 1970 | Score: -71.18 | Avg Score: -89.54 |\n",
"| Experiment: 1 | Episode: 1971 | Score: -113.79 | Avg Score: -89.81 |\n",
"| Experiment: 1 | Episode: 1972 | Score: 2.29 | Avg Score: -88.94 |\n",
"| Experiment: 1 | Episode: 1973 | Score: -62.12 | Avg Score: -89.16 |\n",
"| Experiment: 1 | Episode: 1974 | Score: -84.9 | Avg Score: -88.33 |\n",
"| Experiment: 1 | Episode: 1975 | Score: -229.56 | Avg Score: -90.2 |\n",
"| Experiment: 1 | Episode: 1976 | Score: -112.87 | Avg Score: -88.75 |\n",
"| Experiment: 1 | Episode: 1977 | Score: -104.37 | Avg Score: -89.53 |\n",
"| Experiment: 1 | Episode: 1978 | Score: -113.93 | Avg Score: -88.73 |\n",
"| Experiment: 1 | Episode: 1979 | Score: 2.59 | Avg Score: -87.94 |\n",
"| Experiment: 1 | Episode: 1980 | Score: -98.14 | Avg Score: -88.56 |\n",
"| Experiment: 1 | Episode: 1981 | Score: -208.57 | Avg Score: -90.19 |\n",
"| Experiment: 1 | Episode: 1982 | Score: -107.97 | Avg Score: -90.56 |\n",
"| Experiment: 1 | Episode: 1983 | Score: -65.51 | Avg Score: -89.69 |\n",
"| Experiment: 1 | Episode: 1984 | Score: -91.74 | Avg Score: -90.33 |\n",
"| Experiment: 1 | Episode: 1985 | Score: -103.23 | Avg Score: -90.71 |\n",
"| Experiment: 1 | Episode: 1986 | Score: -23.0 | Avg Score: -90.52 |\n",
"| Experiment: 1 | Episode: 1987 | Score: -178.26 | Avg Score: -91.81 |\n",
"| Experiment: 1 | Episode: 1988 | Score: -53.33 | Avg Score: -91.5 |\n",
"| Experiment: 1 | Episode: 1989 | Score: -130.7 | Avg Score: -91.6 |\n",
"| Experiment: 1 | Episode: 1990 | Score: -33.79 | Avg Score: -91.76 |\n",
"| Experiment: 1 | Episode: 1991 | Score: -70.13 | Avg Score: -91.76 |\n",
"| Experiment: 1 | Episode: 1992 | Score: -8.1 | Avg Score: -91.16 |\n",
"| Experiment: 1 | Episode: 1993 | Score: -84.24 | Avg Score: -91.3 |\n",
"| Experiment: 1 | Episode: 1994 | Score: -76.72 | Avg Score: -89.7 |\n",
"| Experiment: 1 | Episode: 1995 | Score: -7.7 | Avg Score: -87.96 |\n",
"| Experiment: 1 | Episode: 1996 | Score: -110.78 | Avg Score: -86.13 |\n",
"| Experiment: 1 | Episode: 1997 | Score: -24.2 | Avg Score: -85.2 |\n",
"| Experiment: 1 | Episode: 1998 | Score: -261.99 | Avg Score: -87.14 |\n",
"| Experiment: 1 | Episode: 1999 | Score: -236.27 | Avg Score: -89.19 |\n",
"| Experiment: 2 | Episode: 0 | Score: -261.49 | Avg Score: -261.49 |\n",
"| Experiment: 2 | Episode: 1 | Score: -271.91 | Avg Score: -266.7 |\n",
"| Experiment: 2 | Episode: 2 | Score: -264.41 | Avg Score: -265.94 |\n",
"| Experiment: 2 | Episode: 3 | Score: -165.88 | Avg Score: -240.92 |\n",
"| Experiment: 2 | Episode: 4 | Score: -359.92 | Avg Score: -264.72 |\n",
"| Experiment: 2 | Episode: 5 | Score: -196.4 | Avg Score: -253.34 |\n",
"| Experiment: 2 | Episode: 6 | Score: -84.55 | Avg Score: -229.23 |\n",
"| Experiment: 2 | Episode: 7 | Score: -337.28 | Avg Score: -242.73 |\n",
"| Experiment: 2 | Episode: 8 | Score: -196.01 | Avg Score: -237.54 |\n",
"| Experiment: 2 | Episode: 9 | Score: -297.91 | Avg Score: -243.58 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 2 | Episode: 10 | Score: -342.99 | Avg Score: -252.62 |\n",
"| Experiment: 2 | Episode: 11 | Score: -431.98 | Avg Score: -267.56 |\n",
"| Experiment: 2 | Episode: 12 | Score: -374.77 | Avg Score: -275.81 |\n",
"| Experiment: 2 | Episode: 13 | Score: -202.99 | Avg Score: -270.61 |\n",
"| Experiment: 2 | Episode: 14 | Score: -382.07 | Avg Score: -278.04 |\n",
"| Experiment: 2 | Episode: 15 | Score: -90.03 | Avg Score: -266.29 |\n",
"| Experiment: 2 | Episode: 16 | Score: -37.63 | Avg Score: -252.84 |\n",
"| Experiment: 2 | Episode: 17 | Score: -82.32 | Avg Score: -243.36 |\n",
"| Experiment: 2 | Episode: 18 | Score: -283.91 | Avg Score: -245.5 |\n",
"| Experiment: 2 | Episode: 19 | Score: -80.04 | Avg Score: -237.23 |\n",
"| Experiment: 2 | Episode: 20 | Score: -109.45 | Avg Score: -231.14 |\n",
"| Experiment: 2 | Episode: 21 | Score: -363.5 | Avg Score: -237.16 |\n",
"| Experiment: 2 | Episode: 22 | Score: -86.25 | Avg Score: -230.6 |\n",
"| Experiment: 2 | Episode: 23 | Score: -17.65 | Avg Score: -221.72 |\n",
"| Experiment: 2 | Episode: 24 | Score: -169.7 | Avg Score: -219.64 |\n",
"| Experiment: 2 | Episode: 25 | Score: -137.39 | Avg Score: -216.48 |\n",
"| Experiment: 2 | Episode: 26 | Score: -171.63 | Avg Score: -214.82 |\n",
"| Experiment: 2 | Episode: 27 | Score: -168.82 | Avg Score: -213.18 |\n",
"| Experiment: 2 | Episode: 28 | Score: -77.1 | Avg Score: -208.48 |\n",
"| Experiment: 2 | Episode: 29 | Score: -116.94 | Avg Score: -205.43 |\n",
"| Experiment: 2 | Episode: 30 | Score: -104.49 | Avg Score: -202.18 |\n",
"| Experiment: 2 | Episode: 31 | Score: -65.17 | Avg Score: -197.89 |\n",
"| Experiment: 2 | Episode: 32 | Score: -268.7 | Avg Score: -200.04 |\n",
"| Experiment: 2 | Episode: 33 | Score: -171.72 | Avg Score: -199.21 |\n",
"| Experiment: 2 | Episode: 34 | Score: -291.85 | Avg Score: -201.85 |\n",
"| Experiment: 2 | Episode: 35 | Score: -369.45 | Avg Score: -206.51 |\n",
"| Experiment: 2 | Episode: 36 | Score: -281.67 | Avg Score: -208.54 |\n",
"| Experiment: 2 | Episode: 37 | Score: -337.09 | Avg Score: -211.92 |\n",
"| Experiment: 2 | Episode: 38 | Score: -138.69 | Avg Score: -210.05 |\n",
"| Experiment: 2 | Episode: 39 | Score: -383.1 | Avg Score: -214.37 |\n",
"| Experiment: 2 | Episode: 40 | Score: -79.17 | Avg Score: -211.07 |\n",
"| Experiment: 2 | Episode: 41 | Score: -186.85 | Avg Score: -210.5 |\n",
"| Experiment: 2 | Episode: 42 | Score: -172.33 | Avg Score: -209.61 |\n",
"| Experiment: 2 | Episode: 43 | Score: -384.72 | Avg Score: -213.59 |\n",
"| Experiment: 2 | Episode: 44 | Score: -152.62 | Avg Score: -212.24 |\n",
"| Experiment: 2 | Episode: 45 | Score: -85.81 | Avg Score: -209.49 |\n",
"| Experiment: 2 | Episode: 46 | Score: -218.67 | Avg Score: -209.68 |\n",
"| Experiment: 2 | Episode: 47 | Score: -307.26 | Avg Score: -211.72 |\n",
"| Experiment: 2 | Episode: 48 | Score: -286.88 | Avg Score: -213.25 |\n",
"| Experiment: 2 | Episode: 49 | Score: -216.03 | Avg Score: -213.3 |\n",
"| Experiment: 2 | Episode: 50 | Score: -526.86 | Avg Score: -219.45 |\n",
"| Experiment: 2 | Episode: 51 | Score: -87.76 | Avg Score: -216.92 |\n",
"| Experiment: 2 | Episode: 52 | Score: -152.1 | Avg Score: -215.7 |\n",
"| Experiment: 2 | Episode: 53 | Score: -92.92 | Avg Score: -213.42 |\n",
"| Experiment: 2 | Episode: 54 | Score: -86.52 | Avg Score: -211.12 |\n",
"| Experiment: 2 | Episode: 55 | Score: -365.32 | Avg Score: -213.87 |\n",
"| Experiment: 2 | Episode: 56 | Score: -281.68 | Avg Score: -215.06 |\n",
"| Experiment: 2 | Episode: 57 | Score: -138.72 | Avg Score: -213.74 |\n",
"| Experiment: 2 | Episode: 58 | Score: -206.67 | Avg Score: -213.62 |\n",
"| Experiment: 2 | Episode: 59 | Score: -245.78 | Avg Score: -214.16 |\n",
"| Experiment: 2 | Episode: 60 | Score: -140.58 | Avg Score: -212.95 |\n",
"| Experiment: 2 | Episode: 61 | Score: -156.29 | Avg Score: -212.04 |\n",
"| Experiment: 2 | Episode: 62 | Score: -83.64 | Avg Score: -210.0 |\n",
"| Experiment: 2 | Episode: 63 | Score: -94.21 | Avg Score: -208.19 |\n",
"| Experiment: 2 | Episode: 64 | Score: -72.66 | Avg Score: -206.11 |\n",
"| Experiment: 2 | Episode: 65 | Score: -92.84 | Avg Score: -204.39 |\n",
"| Experiment: 2 | Episode: 66 | Score: -329.19 | Avg Score: -206.25 |\n",
"| Experiment: 2 | Episode: 67 | Score: -241.07 | Avg Score: -206.77 |\n",
"| Experiment: 2 | Episode: 68 | Score: -46.26 | Avg Score: -204.44 |\n",
"| Experiment: 2 | Episode: 69 | Score: -151.58 | Avg Score: -203.68 |\n",
"| Experiment: 2 | Episode: 70 | Score: -95.45 | Avg Score: -202.16 |\n",
"| Experiment: 2 | Episode: 71 | Score: -221.58 | Avg Score: -202.43 |\n",
"| Experiment: 2 | Episode: 72 | Score: -141.93 | Avg Score: -201.6 |\n",
"| Experiment: 2 | Episode: 73 | Score: -78.81 | Avg Score: -199.94 |\n",
"| Experiment: 2 | Episode: 74 | Score: -109.98 | Avg Score: -198.74 |\n",
"| Experiment: 2 | Episode: 75 | Score: -154.85 | Avg Score: -198.16 |\n",
"| Experiment: 2 | Episode: 76 | Score: -101.42 | Avg Score: -196.91 |\n",
"| Experiment: 2 | Episode: 77 | Score: -105.49 | Avg Score: -195.74 |\n",
"| Experiment: 2 | Episode: 78 | Score: -39.96 | Avg Score: -193.76 |\n",
"| Experiment: 2 | Episode: 79 | Score: -67.51 | Avg Score: -192.19 |\n",
"| Experiment: 2 | Episode: 80 | Score: -223.69 | Avg Score: -192.57 |\n",
"| Experiment: 2 | Episode: 81 | Score: -335.09 | Avg Score: -194.31 |\n",
"| Experiment: 2 | Episode: 82 | Score: -119.6 | Avg Score: -193.41 |\n",
"| Experiment: 2 | Episode: 83 | Score: -244.52 | Avg Score: -194.02 |\n",
"| Experiment: 2 | Episode: 84 | Score: -522.88 | Avg Score: -197.89 |\n",
"| Experiment: 2 | Episode: 85 | Score: -407.34 | Avg Score: -200.33 |\n",
"| Experiment: 2 | Episode: 86 | Score: -97.27 | Avg Score: -199.14 |\n",
"| Experiment: 2 | Episode: 87 | Score: -247.7 | Avg Score: -199.69 |\n",
"| Experiment: 2 | Episode: 88 | Score: -15.24 | Avg Score: -197.62 |\n",
"| Experiment: 2 | Episode: 89 | Score: -95.83 | Avg Score: -196.49 |\n",
"| Experiment: 2 | Episode: 90 | Score: -105.56 | Avg Score: -195.49 |\n",
"| Experiment: 2 | Episode: 91 | Score: -57.26 | Avg Score: -193.99 |\n",
"| Experiment: 2 | Episode: 92 | Score: -124.55 | Avg Score: -193.24 |\n",
"| Experiment: 2 | Episode: 93 | Score: -66.8 | Avg Score: -191.9 |\n",
"| Experiment: 2 | Episode: 94 | Score: -415.45 | Avg Score: -194.25 |\n",
"| Experiment: 2 | Episode: 95 | Score: -214.43 | Avg Score: -194.46 |\n",
"| Experiment: 2 | Episode: 96 | Score: -132.78 | Avg Score: -193.82 |\n",
"| Experiment: 2 | Episode: 97 | Score: -365.57 | Avg Score: -195.58 |\n",
"| Experiment: 2 | Episode: 98 | Score: -154.51 | Avg Score: -195.16 |\n",
"| Experiment: 2 | Episode: 99 | Score: -394.31 | Avg Score: -197.15 |\n",
"| Experiment: 2 | Episode: 100 | Score: -139.21 | Avg Score: -195.93 |\n",
"| Experiment: 2 | Episode: 101 | Score: -62.74 | Avg Score: -193.84 |\n",
"| Experiment: 2 | Episode: 102 | Score: -88.63 | Avg Score: -192.08 |\n",
"| Experiment: 2 | Episode: 103 | Score: -64.89 | Avg Score: -191.07 |\n",
"| Experiment: 2 | Episode: 104 | Score: -36.61 | Avg Score: -187.84 |\n",
"| Experiment: 2 | Episode: 105 | Score: -90.65 | Avg Score: -186.78 |\n",
"| Experiment: 2 | Episode: 106 | Score: -47.33 | Avg Score: -186.41 |\n",
"| Experiment: 2 | Episode: 107 | Score: -160.09 | Avg Score: -184.64 |\n",
"| Experiment: 2 | Episode: 108 | Score: -105.72 | Avg Score: -183.73 |\n",
"| Experiment: 2 | Episode: 109 | Score: -150.39 | Avg Score: -182.26 |\n",
"| Experiment: 2 | Episode: 110 | Score: -143.39 | Avg Score: -180.26 |\n",
"| Experiment: 2 | Episode: 111 | Score: -80.95 | Avg Score: -176.75 |\n",
"| Experiment: 2 | Episode: 112 | Score: -101.23 | Avg Score: -174.02 |\n",
"| Experiment: 2 | Episode: 113 | Score: -107.58 | Avg Score: -173.06 |\n",
"| Experiment: 2 | Episode: 114 | Score: -81.41 | Avg Score: -170.05 |\n",
"| Experiment: 2 | Episode: 115 | Score: -108.6 | Avg Score: -170.24 |\n",
"| Experiment: 2 | Episode: 116 | Score: -64.52 | Avg Score: -170.51 |\n",
"| Experiment: 2 | Episode: 117 | Score: -96.66 | Avg Score: -170.65 |\n",
"| Experiment: 2 | Episode: 118 | Score: -85.1 | Avg Score: -168.66 |\n",
"| Experiment: 2 | Episode: 119 | Score: -123.36 | Avg Score: -169.1 |\n",
"| Experiment: 2 | Episode: 120 | Score: -229.42 | Avg Score: -170.3 |\n",
"| Experiment: 2 | Episode: 121 | Score: -81.76 | Avg Score: -167.48 |\n",
"| Experiment: 2 | Episode: 122 | Score: -267.11 | Avg Score: -169.29 |\n",
"| Experiment: 2 | Episode: 123 | Score: -93.08 | Avg Score: -170.04 |\n",
"| Experiment: 2 | Episode: 124 | Score: -83.99 | Avg Score: -169.19 |\n",
"| Experiment: 2 | Episode: 125 | Score: -63.46 | Avg Score: -168.45 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 2 | Episode: 126 | Score: -223.05 | Avg Score: -168.96 |\n",
"| Experiment: 2 | Episode: 127 | Score: -102.46 | Avg Score: -168.3 |\n",
"| Experiment: 2 | Episode: 128 | Score: -171.21 | Avg Score: -169.24 |\n",
"| Experiment: 2 | Episode: 129 | Score: -100.25 | Avg Score: -169.07 |\n",
"| Experiment: 2 | Episode: 130 | Score: -241.91 | Avg Score: -170.45 |\n",
"| Experiment: 2 | Episode: 131 | Score: -236.41 | Avg Score: -172.16 |\n",
"| Experiment: 2 | Episode: 132 | Score: -310.8 | Avg Score: -172.58 |\n",
"| Experiment: 2 | Episode: 133 | Score: -104.12 | Avg Score: -171.9 |\n",
"| Experiment: 2 | Episode: 134 | Score: -222.0 | Avg Score: -171.2 |\n",
"| Experiment: 2 | Episode: 135 | Score: -127.2 | Avg Score: -168.78 |\n",
"| Experiment: 2 | Episode: 136 | Score: -376.21 | Avg Score: -169.73 |\n",
"| Experiment: 2 | Episode: 137 | Score: -245.42 | Avg Score: -168.81 |\n",
"| Experiment: 2 | Episode: 138 | Score: -247.75 | Avg Score: -169.9 |\n",
"| Experiment: 2 | Episode: 139 | Score: -305.1 | Avg Score: -169.12 |\n",
"| Experiment: 2 | Episode: 140 | Score: -93.02 | Avg Score: -169.26 |\n",
"| Experiment: 2 | Episode: 141 | Score: -110.22 | Avg Score: -168.49 |\n",
"| Experiment: 2 | Episode: 142 | Score: -96.21 | Avg Score: -167.73 |\n",
"| Experiment: 2 | Episode: 143 | Score: -169.79 | Avg Score: -165.58 |\n",
"| Experiment: 2 | Episode: 144 | Score: -59.73 | Avg Score: -164.65 |\n",
"| Experiment: 2 | Episode: 145 | Score: -124.14 | Avg Score: -165.04 |\n",
"| Experiment: 2 | Episode: 146 | Score: -256.01 | Avg Score: -165.41 |\n",
"| Experiment: 2 | Episode: 147 | Score: -154.57 | Avg Score: -163.88 |\n",
"| Experiment: 2 | Episode: 148 | Score: -232.47 | Avg Score: -163.34 |\n",
"| Experiment: 2 | Episode: 149 | Score: -250.48 | Avg Score: -163.68 |\n",
"| Experiment: 2 | Episode: 150 | Score: -107.55 | Avg Score: -159.49 |\n",
"| Experiment: 2 | Episode: 151 | Score: -465.52 | Avg Score: -163.27 |\n",
"| Experiment: 2 | Episode: 152 | Score: -167.95 | Avg Score: -163.43 |\n",
"| Experiment: 2 | Episode: 153 | Score: -99.99 | Avg Score: -163.5 |\n",
"| Experiment: 2 | Episode: 154 | Score: 19.5 | Avg Score: -162.44 |\n",
"| Experiment: 2 | Episode: 155 | Score: -124.26 | Avg Score: -160.03 |\n",
"| Experiment: 2 | Episode: 156 | Score: -150.06 | Avg Score: -158.71 |\n",
"| Experiment: 2 | Episode: 157 | Score: -134.22 | Avg Score: -158.67 |\n",
"| Experiment: 2 | Episode: 158 | Score: -330.5 | Avg Score: -159.9 |\n",
"| Experiment: 2 | Episode: 159 | Score: -349.18 | Avg Score: -160.94 |\n",
"| Experiment: 2 | Episode: 160 | Score: -99.92 | Avg Score: -160.53 |\n",
"| Experiment: 2 | Episode: 161 | Score: -100.53 | Avg Score: -159.97 |\n",
"| Experiment: 2 | Episode: 162 | Score: -346.6 | Avg Score: -162.6 |\n",
"| Experiment: 2 | Episode: 163 | Score: -53.85 | Avg Score: -162.2 |\n",
"| Experiment: 2 | Episode: 164 | Score: -189.74 | Avg Score: -163.37 |\n",
"| Experiment: 2 | Episode: 165 | Score: -38.96 | Avg Score: -162.83 |\n",
"| Experiment: 2 | Episode: 166 | Score: -238.95 | Avg Score: -161.93 |\n",
"| Experiment: 2 | Episode: 167 | Score: -71.56 | Avg Score: -160.23 |\n",
"| Experiment: 2 | Episode: 168 | Score: -204.51 | Avg Score: -161.82 |\n",
"| Experiment: 2 | Episode: 169 | Score: -331.4 | Avg Score: -163.62 |\n",
"| Experiment: 2 | Episode: 170 | Score: -97.24 | Avg Score: -163.63 |\n",
"| Experiment: 2 | Episode: 171 | Score: -57.51 | Avg Score: -161.99 |\n",
"| Experiment: 2 | Episode: 172 | Score: -157.82 | Avg Score: -162.15 |\n",
"| Experiment: 2 | Episode: 173 | Score: -94.77 | Avg Score: -162.31 |\n",
"| Experiment: 2 | Episode: 174 | Score: -53.13 | Avg Score: -161.74 |\n",
"| Experiment: 2 | Episode: 175 | Score: -99.45 | Avg Score: -161.19 |\n",
"| Experiment: 2 | Episode: 176 | Score: -180.26 | Avg Score: -161.98 |\n",
"| Experiment: 2 | Episode: 177 | Score: -78.39 | Avg Score: -161.71 |\n",
"| Experiment: 2 | Episode: 178 | Score: -127.46 | Avg Score: -162.58 |\n",
"| Experiment: 2 | Episode: 179 | Score: -125.46 | Avg Score: -163.16 |\n",
"| Experiment: 2 | Episode: 180 | Score: -119.09 | Avg Score: -162.11 |\n",
"| Experiment: 2 | Episode: 181 | Score: -296.9 | Avg Score: -161.73 |\n",
"| Experiment: 2 | Episode: 182 | Score: -75.18 | Avg Score: -161.29 |\n",
"| Experiment: 2 | Episode: 183 | Score: -126.13 | Avg Score: -160.1 |\n",
"| Experiment: 2 | Episode: 184 | Score: -198.08 | Avg Score: -156.86 |\n",
"| Experiment: 2 | Episode: 185 | Score: -98.34 | Avg Score: -153.77 |\n",
"| Experiment: 2 | Episode: 186 | Score: -227.35 | Avg Score: -155.07 |\n",
"| Experiment: 2 | Episode: 187 | Score: -54.12 | Avg Score: -153.13 |\n",
"| Experiment: 2 | Episode: 188 | Score: -87.43 | Avg Score: -153.85 |\n",
"| Experiment: 2 | Episode: 189 | Score: -331.81 | Avg Score: -156.21 |\n",
"| Experiment: 2 | Episode: 190 | Score: -318.08 | Avg Score: -158.34 |\n",
"| Experiment: 2 | Episode: 191 | Score: -145.59 | Avg Score: -159.22 |\n",
"| Experiment: 2 | Episode: 192 | Score: -207.23 | Avg Score: -160.05 |\n",
"| Experiment: 2 | Episode: 193 | Score: -348.35 | Avg Score: -162.86 |\n",
"| Experiment: 2 | Episode: 194 | Score: -93.83 | Avg Score: -159.65 |\n",
"| Experiment: 2 | Episode: 195 | Score: -247.3 | Avg Score: -159.98 |\n",
"| Experiment: 2 | Episode: 196 | Score: -43.6 | Avg Score: -159.08 |\n",
"| Experiment: 2 | Episode: 197 | Score: -288.99 | Avg Score: -158.32 |\n",
"| Experiment: 2 | Episode: 198 | Score: -200.11 | Avg Score: -158.77 |\n",
"| Experiment: 2 | Episode: 199 | Score: -8.62 | Avg Score: -154.92 |\n",
"| Experiment: 2 | Episode: 200 | Score: -81.37 | Avg Score: -154.34 |\n",
"| Experiment: 2 | Episode: 201 | Score: -377.52 | Avg Score: -157.49 |\n",
"| Experiment: 2 | Episode: 202 | Score: -104.73 | Avg Score: -157.65 |\n",
"| Experiment: 2 | Episode: 203 | Score: -163.74 | Avg Score: -158.64 |\n",
"| Experiment: 2 | Episode: 204 | Score: -28.55 | Avg Score: -158.56 |\n",
"| Experiment: 2 | Episode: 205 | Score: -177.42 | Avg Score: -159.42 |\n",
"| Experiment: 2 | Episode: 206 | Score: -107.26 | Avg Score: -160.02 |\n",
"| Experiment: 2 | Episode: 207 | Score: -264.5 | Avg Score: -161.07 |\n",
"| Experiment: 2 | Episode: 208 | Score: -33.71 | Avg Score: -160.35 |\n",
"| Experiment: 2 | Episode: 209 | Score: -92.18 | Avg Score: -159.76 |\n",
"| Experiment: 2 | Episode: 210 | Score: -135.31 | Avg Score: -159.68 |\n",
"| Experiment: 2 | Episode: 211 | Score: -227.41 | Avg Score: -161.15 |\n",
"| Experiment: 2 | Episode: 212 | Score: -307.75 | Avg Score: -163.21 |\n",
"| Experiment: 2 | Episode: 213 | Score: -109.65 | Avg Score: -163.23 |\n",
"| Experiment: 2 | Episode: 214 | Score: -285.09 | Avg Score: -165.27 |\n",
"| Experiment: 2 | Episode: 215 | Score: -108.77 | Avg Score: -165.27 |\n",
"| Experiment: 2 | Episode: 216 | Score: -150.31 | Avg Score: -166.13 |\n",
"| Experiment: 2 | Episode: 217 | Score: -104.79 | Avg Score: -166.21 |\n",
"| Experiment: 2 | Episode: 218 | Score: -349.1 | Avg Score: -168.85 |\n",
"| Experiment: 2 | Episode: 219 | Score: -336.71 | Avg Score: -170.99 |\n",
"| Experiment: 2 | Episode: 220 | Score: -222.51 | Avg Score: -170.92 |\n",
"| Experiment: 2 | Episode: 221 | Score: -165.3 | Avg Score: -171.75 |\n",
"| Experiment: 2 | Episode: 222 | Score: -105.87 | Avg Score: -170.14 |\n",
"| Experiment: 2 | Episode: 223 | Score: -272.76 | Avg Score: -171.94 |\n",
"| Experiment: 2 | Episode: 224 | Score: -125.13 | Avg Score: -172.35 |\n",
"| Experiment: 2 | Episode: 225 | Score: -129.31 | Avg Score: -173.01 |\n",
"| Experiment: 2 | Episode: 226 | Score: -170.56 | Avg Score: -172.48 |\n",
"| Experiment: 2 | Episode: 227 | Score: -75.52 | Avg Score: -172.21 |\n",
"| Experiment: 2 | Episode: 228 | Score: -50.89 | Avg Score: -171.01 |\n",
"| Experiment: 2 | Episode: 229 | Score: -158.0 | Avg Score: -171.59 |\n",
"| Experiment: 2 | Episode: 230 | Score: -470.08 | Avg Score: -173.87 |\n",
"| Experiment: 2 | Episode: 231 | Score: -7.93 | Avg Score: -171.58 |\n",
"| Experiment: 2 | Episode: 232 | Score: -163.04 | Avg Score: -170.11 |\n",
"| Experiment: 2 | Episode: 233 | Score: -143.69 | Avg Score: -170.5 |\n",
"| Experiment: 2 | Episode: 234 | Score: -101.73 | Avg Score: -169.3 |\n",
"| Experiment: 2 | Episode: 235 | Score: -80.27 | Avg Score: -168.83 |\n",
"| Experiment: 2 | Episode: 236 | Score: -89.33 | Avg Score: -165.96 |\n",
"| Experiment: 2 | Episode: 237 | Score: -93.5 | Avg Score: -164.44 |\n",
"| Experiment: 2 | Episode: 238 | Score: -94.62 | Avg Score: -162.91 |\n",
"| Experiment: 2 | Episode: 239 | Score: -154.76 | Avg Score: -161.41 |\n",
"| Experiment: 2 | Episode: 240 | Score: -101.46 | Avg Score: -161.49 |\n",
"| Experiment: 2 | Episode: 241 | Score: -282.8 | Avg Score: -163.22 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 2 | Episode: 242 | Score: -75.67 | Avg Score: -163.01 |\n",
"| Experiment: 2 | Episode: 243 | Score: -133.56 | Avg Score: -162.65 |\n",
"| Experiment: 2 | Episode: 244 | Score: -251.87 | Avg Score: -164.57 |\n",
"| Experiment: 2 | Episode: 245 | Score: -44.79 | Avg Score: -163.78 |\n",
"| Experiment: 2 | Episode: 246 | Score: -293.29 | Avg Score: -164.15 |\n",
"| Experiment: 2 | Episode: 247 | Score: -97.39 | Avg Score: -163.58 |\n",
"| Experiment: 2 | Episode: 248 | Score: -270.85 | Avg Score: -163.96 |\n",
"| Experiment: 2 | Episode: 249 | Score: -82.35 | Avg Score: -162.28 |\n",
"| Experiment: 2 | Episode: 250 | Score: -217.19 | Avg Score: -163.38 |\n",
"| Experiment: 2 | Episode: 251 | Score: -168.85 | Avg Score: -160.41 |\n",
"| Experiment: 2 | Episode: 252 | Score: -100.07 | Avg Score: -159.73 |\n",
"| Experiment: 2 | Episode: 253 | Score: -90.37 | Avg Score: -159.64 |\n",
"| Experiment: 2 | Episode: 254 | Score: -44.19 | Avg Score: -160.27 |\n",
"| Experiment: 2 | Episode: 255 | Score: -138.9 | Avg Score: -160.42 |\n",
"| Experiment: 2 | Episode: 256 | Score: -114.54 | Avg Score: -160.06 |\n",
"| Experiment: 2 | Episode: 257 | Score: -54.95 | Avg Score: -159.27 |\n",
"| Experiment: 2 | Episode: 258 | Score: -372.86 | Avg Score: -159.69 |\n",
"| Experiment: 2 | Episode: 259 | Score: -90.73 | Avg Score: -157.11 |\n",
"| Experiment: 2 | Episode: 260 | Score: -252.41 | Avg Score: -158.63 |\n",
"| Experiment: 2 | Episode: 261 | Score: -114.71 | Avg Score: -158.78 |\n",
"| Experiment: 2 | Episode: 262 | Score: -278.91 | Avg Score: -158.1 |\n",
"| Experiment: 2 | Episode: 263 | Score: -11.67 | Avg Score: -157.68 |\n",
"| Experiment: 2 | Episode: 264 | Score: -154.68 | Avg Score: -157.33 |\n",
"| Experiment: 2 | Episode: 265 | Score: -151.01 | Avg Score: -158.45 |\n",
"| Experiment: 2 | Episode: 266 | Score: -41.24 | Avg Score: -156.47 |\n",
"| Experiment: 2 | Episode: 267 | Score: -101.48 | Avg Score: -156.77 |\n",
"| Experiment: 2 | Episode: 268 | Score: -174.7 | Avg Score: -156.47 |\n",
"| Experiment: 2 | Episode: 269 | Score: -79.42 | Avg Score: -153.95 |\n",
"| Experiment: 2 | Episode: 270 | Score: -65.39 | Avg Score: -153.63 |\n",
"| Experiment: 2 | Episode: 271 | Score: -99.09 | Avg Score: -154.05 |\n",
"| Experiment: 2 | Episode: 272 | Score: -299.59 | Avg Score: -155.47 |\n",
"| Experiment: 2 | Episode: 273 | Score: -72.19 | Avg Score: -155.24 |\n",
"| Experiment: 2 | Episode: 274 | Score: -78.79 | Avg Score: -155.5 |\n",
"| Experiment: 2 | Episode: 275 | Score: -124.97 | Avg Score: -155.75 |\n",
"| Experiment: 2 | Episode: 276 | Score: -149.05 | Avg Score: -155.44 |\n",
"| Experiment: 2 | Episode: 277 | Score: -148.46 | Avg Score: -156.14 |\n",
"| Experiment: 2 | Episode: 278 | Score: -339.11 | Avg Score: -158.26 |\n",
"| Experiment: 2 | Episode: 279 | Score: -196.52 | Avg Score: -158.97 |\n",
"| Experiment: 2 | Episode: 280 | Score: -75.81 | Avg Score: -158.54 |\n",
"| Experiment: 2 | Episode: 281 | Score: -400.09 | Avg Score: -159.57 |\n",
"| Experiment: 2 | Episode: 282 | Score: -157.0 | Avg Score: -160.39 |\n",
"| Experiment: 2 | Episode: 283 | Score: -290.08 | Avg Score: -162.03 |\n",
"| Experiment: 2 | Episode: 284 | Score: -78.44 | Avg Score: -160.83 |\n",
"| Experiment: 2 | Episode: 285 | Score: -207.88 | Avg Score: -161.92 |\n",
"| Experiment: 2 | Episode: 286 | Score: -121.56 | Avg Score: -160.87 |\n",
"| Experiment: 2 | Episode: 287 | Score: -434.67 | Avg Score: -164.67 |\n",
"| Experiment: 2 | Episode: 288 | Score: -115.28 | Avg Score: -164.95 |\n",
"| Experiment: 2 | Episode: 289 | Score: -79.64 | Avg Score: -162.43 |\n",
"| Experiment: 2 | Episode: 290 | Score: -51.47 | Avg Score: -159.76 |\n",
"| Experiment: 2 | Episode: 291 | Score: -127.41 | Avg Score: -159.58 |\n",
"| Experiment: 2 | Episode: 292 | Score: -162.7 | Avg Score: -159.14 |\n",
"| Experiment: 2 | Episode: 293 | Score: -234.05 | Avg Score: -157.99 |\n",
"| Experiment: 2 | Episode: 294 | Score: -216.79 | Avg Score: -159.22 |\n",
"| Experiment: 2 | Episode: 295 | Score: -424.86 | Avg Score: -161.0 |\n",
"| Experiment: 2 | Episode: 296 | Score: -48.07 | Avg Score: -161.04 |\n",
"| Experiment: 2 | Episode: 297 | Score: -43.47 | Avg Score: -158.59 |\n",
"| Experiment: 2 | Episode: 298 | Score: -161.07 | Avg Score: -158.2 |\n",
"| Experiment: 2 | Episode: 299 | Score: -10.89 | Avg Score: -158.22 |\n",
"| Experiment: 2 | Episode: 300 | Score: -42.36 | Avg Score: -157.83 |\n",
"| Experiment: 2 | Episode: 301 | Score: -84.31 | Avg Score: -154.9 |\n",
"| Experiment: 2 | Episode: 302 | Score: -93.79 | Avg Score: -154.79 |\n",
"| Experiment: 2 | Episode: 303 | Score: -62.02 | Avg Score: -153.77 |\n",
"| Experiment: 2 | Episode: 304 | Score: -85.92 | Avg Score: -154.34 |\n",
"| Experiment: 2 | Episode: 305 | Score: -76.09 | Avg Score: -153.33 |\n",
"| Experiment: 2 | Episode: 306 | Score: -61.63 | Avg Score: -152.87 |\n",
"| Experiment: 2 | Episode: 307 | Score: -156.54 | Avg Score: -151.8 |\n",
"| Experiment: 2 | Episode: 308 | Score: -81.86 | Avg Score: -152.28 |\n",
"| Experiment: 2 | Episode: 309 | Score: -332.67 | Avg Score: -154.68 |\n",
"| Experiment: 2 | Episode: 310 | Score: -72.71 | Avg Score: -154.06 |\n",
"| Experiment: 2 | Episode: 311 | Score: -48.29 | Avg Score: -152.26 |\n",
"| Experiment: 2 | Episode: 312 | Score: -95.29 | Avg Score: -150.14 |\n",
"| Experiment: 2 | Episode: 313 | Score: -61.73 | Avg Score: -149.66 |\n",
"| Experiment: 2 | Episode: 314 | Score: -212.95 | Avg Score: -148.94 |\n",
"| Experiment: 2 | Episode: 315 | Score: -141.76 | Avg Score: -149.27 |\n",
"| Experiment: 2 | Episode: 316 | Score: -124.06 | Avg Score: -149.01 |\n",
"| Experiment: 2 | Episode: 317 | Score: -243.44 | Avg Score: -150.39 |\n",
"| Experiment: 2 | Episode: 318 | Score: -76.93 | Avg Score: -147.67 |\n",
"| Experiment: 2 | Episode: 319 | Score: -320.41 | Avg Score: -147.51 |\n",
"| Experiment: 2 | Episode: 320 | Score: -25.66 | Avg Score: -145.54 |\n",
"| Experiment: 2 | Episode: 321 | Score: -52.76 | Avg Score: -144.41 |\n",
"| Experiment: 2 | Episode: 322 | Score: -92.65 | Avg Score: -144.28 |\n",
"| Experiment: 2 | Episode: 323 | Score: -52.21 | Avg Score: -142.08 |\n",
"| Experiment: 2 | Episode: 324 | Score: -128.76 | Avg Score: -142.11 |\n",
"| Experiment: 2 | Episode: 325 | Score: -96.18 | Avg Score: -141.78 |\n",
"| Experiment: 2 | Episode: 326 | Score: -67.73 | Avg Score: -140.75 |\n",
"| Experiment: 2 | Episode: 327 | Score: -73.84 | Avg Score: -140.74 |\n",
"| Experiment: 2 | Episode: 328 | Score: -170.69 | Avg Score: -141.93 |\n",
"| Experiment: 2 | Episode: 329 | Score: -62.23 | Avg Score: -140.98 |\n",
"| Experiment: 2 | Episode: 330 | Score: -65.93 | Avg Score: -136.94 |\n",
"| Experiment: 2 | Episode: 331 | Score: -56.92 | Avg Score: -137.43 |\n",
"| Experiment: 2 | Episode: 332 | Score: -78.21 | Avg Score: -136.58 |\n",
"| Experiment: 2 | Episode: 333 | Score: -97.86 | Avg Score: -136.12 |\n",
"| Experiment: 2 | Episode: 334 | Score: -217.51 | Avg Score: -137.28 |\n",
"| Experiment: 2 | Episode: 335 | Score: -314.35 | Avg Score: -139.62 |\n",
"| Experiment: 2 | Episode: 336 | Score: -81.09 | Avg Score: -139.53 |\n",
"| Experiment: 2 | Episode: 337 | Score: -117.11 | Avg Score: -139.77 |\n",
"| Experiment: 2 | Episode: 338 | Score: -70.59 | Avg Score: -139.53 |\n",
"| Experiment: 2 | Episode: 339 | Score: -202.7 | Avg Score: -140.01 |\n",
"| Experiment: 2 | Episode: 340 | Score: -91.26 | Avg Score: -139.91 |\n",
"| Experiment: 2 | Episode: 341 | Score: -88.0 | Avg Score: -137.96 |\n",
"| Experiment: 2 | Episode: 342 | Score: -123.85 | Avg Score: -138.44 |\n",
"| Experiment: 2 | Episode: 343 | Score: -52.5 | Avg Score: -137.63 |\n",
"| Experiment: 2 | Episode: 344 | Score: -236.56 | Avg Score: -137.48 |\n",
"| Experiment: 2 | Episode: 345 | Score: -101.74 | Avg Score: -138.05 |\n",
"| Experiment: 2 | Episode: 346 | Score: -218.96 | Avg Score: -137.3 |\n",
"| Experiment: 2 | Episode: 347 | Score: -227.1 | Avg Score: -138.6 |\n",
"| Experiment: 2 | Episode: 348 | Score: -306.54 | Avg Score: -138.96 |\n",
"| Experiment: 2 | Episode: 349 | Score: -162.55 | Avg Score: -139.76 |\n",
"| Experiment: 2 | Episode: 350 | Score: 11.79 | Avg Score: -137.47 |\n",
"| Experiment: 2 | Episode: 351 | Score: -125.77 | Avg Score: -137.04 |\n",
"| Experiment: 2 | Episode: 352 | Score: -48.92 | Avg Score: -136.53 |\n",
"| Experiment: 2 | Episode: 353 | Score: -85.8 | Avg Score: -136.48 |\n",
"| Experiment: 2 | Episode: 354 | Score: -42.02 | Avg Score: -136.46 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 2 | Episode: 355 | Score: 14.19 | Avg Score: -134.93 |\n",
"| Experiment: 2 | Episode: 356 | Score: 10.3 | Avg Score: -133.68 |\n",
"| Experiment: 2 | Episode: 357 | Score: -143.3 | Avg Score: -134.56 |\n",
"| Experiment: 2 | Episode: 358 | Score: -261.01 | Avg Score: -133.45 |\n",
"| Experiment: 2 | Episode: 359 | Score: -302.92 | Avg Score: -135.57 |\n",
"| Experiment: 2 | Episode: 360 | Score: -24.36 | Avg Score: -133.29 |\n",
"| Experiment: 2 | Episode: 361 | Score: -252.66 | Avg Score: -134.67 |\n",
"| Experiment: 2 | Episode: 362 | Score: -81.39 | Avg Score: -132.69 |\n",
"| Experiment: 2 | Episode: 363 | Score: -194.36 | Avg Score: -134.52 |\n",
"| Experiment: 2 | Episode: 364 | Score: -64.21 | Avg Score: -133.61 |\n",
"| Experiment: 2 | Episode: 365 | Score: -171.68 | Avg Score: -133.82 |\n",
"| Experiment: 2 | Episode: 366 | Score: -101.09 | Avg Score: -134.42 |\n",
"| Experiment: 2 | Episode: 367 | Score: -62.47 | Avg Score: -134.03 |\n",
"| Experiment: 2 | Episode: 368 | Score: -82.88 | Avg Score: -133.11 |\n",
"| Experiment: 2 | Episode: 369 | Score: -149.6 | Avg Score: -133.81 |\n",
"| Experiment: 2 | Episode: 370 | Score: -268.98 | Avg Score: -135.85 |\n",
"| Experiment: 2 | Episode: 371 | Score: -27.54 | Avg Score: -135.13 |\n",
"| Experiment: 2 | Episode: 372 | Score: -156.18 | Avg Score: -133.7 |\n",
"| Experiment: 2 | Episode: 373 | Score: -104.45 | Avg Score: -134.02 |\n",
"| Experiment: 2 | Episode: 374 | Score: -288.53 | Avg Score: -136.12 |\n",
"| Experiment: 2 | Episode: 375 | Score: -251.3 | Avg Score: -137.38 |\n",
"| Experiment: 2 | Episode: 376 | Score: -265.26 | Avg Score: -138.54 |\n",
"| Experiment: 2 | Episode: 377 | Score: -60.56 | Avg Score: -137.67 |\n",
"| Experiment: 2 | Episode: 378 | Score: -268.63 | Avg Score: -136.96 |\n",
"| Experiment: 2 | Episode: 379 | Score: -121.6 | Avg Score: -136.21 |\n",
"| Experiment: 2 | Episode: 380 | Score: -360.11 | Avg Score: -139.05 |\n",
"| Experiment: 2 | Episode: 381 | Score: -79.78 | Avg Score: -135.85 |\n",
"| Experiment: 2 | Episode: 382 | Score: -112.64 | Avg Score: -135.41 |\n",
"| Experiment: 2 | Episode: 383 | Score: -305.66 | Avg Score: -135.56 |\n",
"| Experiment: 2 | Episode: 384 | Score: -90.78 | Avg Score: -135.69 |\n",
"| Experiment: 2 | Episode: 385 | Score: -131.49 | Avg Score: -134.92 |\n",
"| Experiment: 2 | Episode: 386 | Score: 1.33 | Avg Score: -133.69 |\n",
"| Experiment: 2 | Episode: 387 | Score: -131.49 | Avg Score: -130.66 |\n",
"| Experiment: 2 | Episode: 388 | Score: -102.41 | Avg Score: -130.53 |\n",
"| Experiment: 2 | Episode: 389 | Score: -112.23 | Avg Score: -130.86 |\n",
"| Experiment: 2 | Episode: 390 | Score: -105.63 | Avg Score: -131.4 |\n",
"| Experiment: 2 | Episode: 391 | Score: -240.53 | Avg Score: -132.53 |\n",
"| Experiment: 2 | Episode: 392 | Score: -75.91 | Avg Score: -131.66 |\n",
"| Experiment: 2 | Episode: 393 | Score: -38.67 | Avg Score: -129.71 |\n",
"| Experiment: 2 | Episode: 394 | Score: -179.65 | Avg Score: -129.34 |\n",
"| Experiment: 2 | Episode: 395 | Score: -40.91 | Avg Score: -125.5 |\n",
"| Experiment: 2 | Episode: 396 | Score: -112.51 | Avg Score: -126.14 |\n",
"| Experiment: 2 | Episode: 397 | Score: -99.62 | Avg Score: -126.71 |\n",
"| Experiment: 2 | Episode: 398 | Score: -144.88 | Avg Score: -126.54 |\n",
"| Experiment: 2 | Episode: 399 | Score: -55.69 | Avg Score: -126.99 |\n",
"| Experiment: 2 | Episode: 400 | Score: -113.71 | Avg Score: -127.71 |\n",
"| Experiment: 2 | Episode: 401 | Score: -305.66 | Avg Score: -129.92 |\n",
"| Experiment: 2 | Episode: 402 | Score: -97.36 | Avg Score: -129.95 |\n",
"| Experiment: 2 | Episode: 403 | Score: -276.45 | Avg Score: -132.1 |\n",
"| Experiment: 2 | Episode: 404 | Score: -153.32 | Avg Score: -132.77 |\n",
"| Experiment: 2 | Episode: 405 | Score: -244.28 | Avg Score: -134.45 |\n",
"| Experiment: 2 | Episode: 406 | Score: -76.88 | Avg Score: -134.61 |\n",
"| Experiment: 2 | Episode: 407 | Score: -58.38 | Avg Score: -133.63 |\n",
"| Experiment: 2 | Episode: 408 | Score: -116.38 | Avg Score: -133.97 |\n",
"| Experiment: 2 | Episode: 409 | Score: -79.13 | Avg Score: -131.44 |\n",
"| Experiment: 2 | Episode: 410 | Score: -195.12 | Avg Score: -132.66 |\n",
"| Experiment: 2 | Episode: 411 | Score: -178.32 | Avg Score: -133.96 |\n",
"| Experiment: 2 | Episode: 412 | Score: 148.47 | Avg Score: -131.52 |\n",
"| Experiment: 2 | Episode: 413 | Score: -322.78 | Avg Score: -134.13 |\n",
"| Experiment: 2 | Episode: 414 | Score: -108.58 | Avg Score: -133.09 |\n",
"| Experiment: 2 | Episode: 415 | Score: -80.15 | Avg Score: -132.47 |\n",
"| Experiment: 2 | Episode: 416 | Score: -108.25 | Avg Score: -132.31 |\n",
"| Experiment: 2 | Episode: 417 | Score: -53.29 | Avg Score: -130.41 |\n",
"| Experiment: 2 | Episode: 418 | Score: -133.47 | Avg Score: -130.98 |\n",
"| Experiment: 2 | Episode: 419 | Score: -161.15 | Avg Score: -129.39 |\n",
"| Experiment: 2 | Episode: 420 | Score: -245.45 | Avg Score: -131.58 |\n",
"| Experiment: 2 | Episode: 421 | Score: -60.75 | Avg Score: -131.66 |\n",
"| Experiment: 2 | Episode: 422 | Score: 19.58 | Avg Score: -130.54 |\n",
"| Experiment: 2 | Episode: 423 | Score: -116.49 | Avg Score: -131.18 |\n",
"| Experiment: 2 | Episode: 424 | Score: -77.33 | Avg Score: -130.67 |\n",
"| Experiment: 2 | Episode: 425 | Score: -30.17 | Avg Score: -130.01 |\n",
"| Experiment: 2 | Episode: 426 | Score: -368.35 | Avg Score: -133.02 |\n",
"| Experiment: 2 | Episode: 427 | Score: -111.14 | Avg Score: -133.39 |\n",
"| Experiment: 2 | Episode: 428 | Score: -335.71 | Avg Score: -135.04 |\n",
"| Experiment: 2 | Episode: 429 | Score: -107.43 | Avg Score: -135.49 |\n",
"| Experiment: 2 | Episode: 430 | Score: -151.05 | Avg Score: -136.34 |\n",
"| Experiment: 2 | Episode: 431 | Score: -91.11 | Avg Score: -136.68 |\n",
"| Experiment: 2 | Episode: 432 | Score: -61.39 | Avg Score: -136.52 |\n",
"| Experiment: 2 | Episode: 433 | Score: -1.04 | Avg Score: -135.55 |\n",
"| Experiment: 2 | Episode: 434 | Score: -222.78 | Avg Score: -135.6 |\n",
"| Experiment: 2 | Episode: 435 | Score: -254.58 | Avg Score: -135.0 |\n",
"| Experiment: 2 | Episode: 436 | Score: -132.53 | Avg Score: -135.52 |\n",
"| Experiment: 2 | Episode: 437 | Score: -82.68 | Avg Score: -135.17 |\n",
"| Experiment: 2 | Episode: 438 | Score: -120.94 | Avg Score: -135.68 |\n",
"| Experiment: 2 | Episode: 439 | Score: -100.85 | Avg Score: -134.66 |\n",
"| Experiment: 2 | Episode: 440 | Score: -188.51 | Avg Score: -135.63 |\n",
"| Experiment: 2 | Episode: 441 | Score: -69.65 | Avg Score: -135.45 |\n",
"| Experiment: 2 | Episode: 442 | Score: -65.31 | Avg Score: -134.86 |\n",
"| Experiment: 2 | Episode: 443 | Score: -49.06 | Avg Score: -134.83 |\n",
"| Experiment: 2 | Episode: 444 | Score: -55.77 | Avg Score: -133.02 |\n",
"| Experiment: 2 | Episode: 445 | Score: -49.4 | Avg Score: -132.5 |\n",
"| Experiment: 2 | Episode: 446 | Score: -188.46 | Avg Score: -132.19 |\n",
"| Experiment: 2 | Episode: 447 | Score: -91.37 | Avg Score: -130.83 |\n",
"| Experiment: 2 | Episode: 448 | Score: -149.94 | Avg Score: -129.27 |\n",
"| Experiment: 2 | Episode: 449 | Score: -17.57 | Avg Score: -127.82 |\n",
"| Experiment: 2 | Episode: 450 | Score: -130.47 | Avg Score: -129.24 |\n",
"| Experiment: 2 | Episode: 451 | Score: -83.83 | Avg Score: -128.82 |\n",
"| Experiment: 2 | Episode: 452 | Score: -68.47 | Avg Score: -129.02 |\n",
"| Experiment: 2 | Episode: 453 | Score: -130.46 | Avg Score: -129.46 |\n",
"| Experiment: 2 | Episode: 454 | Score: -78.85 | Avg Score: -129.83 |\n",
"| Experiment: 2 | Episode: 455 | Score: -93.7 | Avg Score: -130.91 |\n",
"| Experiment: 2 | Episode: 456 | Score: -85.14 | Avg Score: -131.86 |\n",
"| Experiment: 2 | Episode: 457 | Score: -52.94 | Avg Score: -130.96 |\n",
"| Experiment: 2 | Episode: 458 | Score: -104.1 | Avg Score: -129.39 |\n",
"| Experiment: 2 | Episode: 459 | Score: -215.17 | Avg Score: -128.51 |\n",
"| Experiment: 2 | Episode: 460 | Score: -96.86 | Avg Score: -129.24 |\n",
"| Experiment: 2 | Episode: 461 | Score: -96.97 | Avg Score: -127.68 |\n",
"| Experiment: 2 | Episode: 462 | Score: -11.44 | Avg Score: -126.98 |\n",
"| Experiment: 2 | Episode: 463 | Score: -51.07 | Avg Score: -125.55 |\n",
"| Experiment: 2 | Episode: 464 | Score: -343.4 | Avg Score: -128.34 |\n",
"| Experiment: 2 | Episode: 465 | Score: -113.41 | Avg Score: -127.76 |\n",
"| Experiment: 2 | Episode: 466 | Score: -498.23 | Avg Score: -131.73 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 2 | Episode: 467 | Score: -117.44 | Avg Score: -132.28 |\n",
"| Experiment: 2 | Episode: 468 | Score: -162.69 | Avg Score: -133.08 |\n",
"| Experiment: 2 | Episode: 469 | Score: -129.84 | Avg Score: -132.88 |\n",
"| Experiment: 2 | Episode: 470 | Score: -53.09 | Avg Score: -130.72 |\n",
"| Experiment: 2 | Episode: 471 | Score: -218.14 | Avg Score: -132.63 |\n",
"| Experiment: 2 | Episode: 472 | Score: -97.67 | Avg Score: -132.04 |\n",
"| Experiment: 2 | Episode: 473 | Score: -336.61 | Avg Score: -134.36 |\n",
"| Experiment: 2 | Episode: 474 | Score: -169.81 | Avg Score: -133.18 |\n",
"| Experiment: 2 | Episode: 475 | Score: -116.71 | Avg Score: -131.83 |\n",
"| Experiment: 2 | Episode: 476 | Score: -169.35 | Avg Score: -130.87 |\n",
"| Experiment: 2 | Episode: 477 | Score: -330.34 | Avg Score: -133.57 |\n",
"| Experiment: 2 | Episode: 478 | Score: -118.66 | Avg Score: -132.07 |\n",
"| Experiment: 2 | Episode: 479 | Score: -127.09 | Avg Score: -132.13 |\n",
"| Experiment: 2 | Episode: 480 | Score: -116.48 | Avg Score: -129.69 |\n",
"| Experiment: 2 | Episode: 481 | Score: -36.42 | Avg Score: -129.26 |\n",
"| Experiment: 2 | Episode: 482 | Score: -52.65 | Avg Score: -128.66 |\n",
"| Experiment: 2 | Episode: 483 | Score: -93.78 | Avg Score: -126.54 |\n",
"| Experiment: 2 | Episode: 484 | Score: -84.6 | Avg Score: -126.48 |\n",
"| Experiment: 2 | Episode: 485 | Score: -87.49 | Avg Score: -126.04 |\n",
"| Experiment: 2 | Episode: 486 | Score: -29.97 | Avg Score: -126.35 |\n",
"| Experiment: 2 | Episode: 487 | Score: -189.35 | Avg Score: -126.93 |\n",
"| Experiment: 2 | Episode: 488 | Score: -118.92 | Avg Score: -127.09 |\n",
"| Experiment: 2 | Episode: 489 | Score: -255.21 | Avg Score: -128.52 |\n",
"| Experiment: 2 | Episode: 490 | Score: 21.48 | Avg Score: -127.25 |\n",
"| Experiment: 2 | Episode: 491 | Score: -306.36 | Avg Score: -127.91 |\n",
"| Experiment: 2 | Episode: 492 | Score: -258.52 | Avg Score: -129.74 |\n",
"| Experiment: 2 | Episode: 493 | Score: -159.04 | Avg Score: -130.94 |\n",
"| Experiment: 2 | Episode: 494 | Score: -93.9 | Avg Score: -130.08 |\n",
"| Experiment: 2 | Episode: 495 | Score: -77.21 | Avg Score: -130.44 |\n",
"| Experiment: 2 | Episode: 496 | Score: -166.41 | Avg Score: -130.98 |\n",
"| Experiment: 2 | Episode: 497 | Score: -161.7 | Avg Score: -131.6 |\n",
"| Experiment: 2 | Episode: 498 | Score: -92.12 | Avg Score: -131.08 |\n",
"| Experiment: 2 | Episode: 499 | Score: -73.73 | Avg Score: -131.26 |\n",
"| Experiment: 2 | Episode: 500 | Score: -73.94 | Avg Score: -130.86 |\n",
"| Experiment: 2 | Episode: 501 | Score: -138.66 | Avg Score: -129.19 |\n",
"| Experiment: 2 | Episode: 502 | Score: -60.63 | Avg Score: -128.82 |\n",
"| Experiment: 2 | Episode: 503 | Score: -196.05 | Avg Score: -128.02 |\n",
"| Experiment: 2 | Episode: 504 | Score: -92.99 | Avg Score: -127.41 |\n",
"| Experiment: 2 | Episode: 505 | Score: -57.84 | Avg Score: -125.55 |\n",
"| Experiment: 2 | Episode: 506 | Score: -168.27 | Avg Score: -126.46 |\n",
"| Experiment: 2 | Episode: 507 | Score: -48.77 | Avg Score: -126.37 |\n",
"| Experiment: 2 | Episode: 508 | Score: -67.12 | Avg Score: -125.88 |\n",
"| Experiment: 2 | Episode: 509 | Score: -357.26 | Avg Score: -128.66 |\n",
"| Experiment: 2 | Episode: 510 | Score: -255.8 | Avg Score: -129.26 |\n",
"| Experiment: 2 | Episode: 511 | Score: -219.48 | Avg Score: -129.68 |\n",
"| Experiment: 2 | Episode: 512 | Score: -161.91 | Avg Score: -132.78 |\n",
"| Experiment: 2 | Episode: 513 | Score: -64.14 | Avg Score: -130.19 |\n",
"| Experiment: 2 | Episode: 514 | Score: -87.61 | Avg Score: -129.98 |\n",
"| Experiment: 2 | Episode: 515 | Score: -100.32 | Avg Score: -130.18 |\n",
"| Experiment: 2 | Episode: 516 | Score: -92.77 | Avg Score: -130.03 |\n",
"| Experiment: 2 | Episode: 517 | Score: -101.2 | Avg Score: -130.51 |\n",
"| Experiment: 2 | Episode: 518 | Score: -84.21 | Avg Score: -130.02 |\n",
"| Experiment: 2 | Episode: 519 | Score: -0.54 | Avg Score: -128.41 |\n",
"| Experiment: 2 | Episode: 520 | Score: -310.49 | Avg Score: -129.06 |\n",
"| Experiment: 2 | Episode: 521 | Score: -68.73 | Avg Score: -129.14 |\n",
"| Experiment: 2 | Episode: 522 | Score: -59.14 | Avg Score: -129.93 |\n",
"| Experiment: 2 | Episode: 523 | Score: -311.45 | Avg Score: -131.88 |\n",
"| Experiment: 2 | Episode: 524 | Score: -162.02 | Avg Score: -132.72 |\n",
"| Experiment: 2 | Episode: 525 | Score: -48.18 | Avg Score: -132.9 |\n",
"| Experiment: 2 | Episode: 526 | Score: -146.97 | Avg Score: -130.69 |\n",
"| Experiment: 2 | Episode: 527 | Score: -220.98 | Avg Score: -131.79 |\n",
"| Experiment: 2 | Episode: 528 | Score: -48.99 | Avg Score: -128.92 |\n",
"| Experiment: 2 | Episode: 529 | Score: -140.23 | Avg Score: -129.25 |\n",
"| Experiment: 2 | Episode: 530 | Score: -101.78 | Avg Score: -128.76 |\n",
"| Experiment: 2 | Episode: 531 | Score: -79.98 | Avg Score: -128.65 |\n",
"| Experiment: 2 | Episode: 532 | Score: -152.01 | Avg Score: -129.55 |\n",
"| Experiment: 2 | Episode: 533 | Score: -244.19 | Avg Score: -131.98 |\n",
"| Experiment: 2 | Episode: 534 | Score: -99.1 | Avg Score: -130.75 |\n",
"| Experiment: 2 | Episode: 535 | Score: -102.83 | Avg Score: -129.23 |\n",
"| Experiment: 2 | Episode: 536 | Score: -64.18 | Avg Score: -128.55 |\n",
"| Experiment: 2 | Episode: 537 | Score: -176.86 | Avg Score: -129.49 |\n",
"| Experiment: 2 | Episode: 538 | Score: -256.28 | Avg Score: -130.84 |\n",
"| Experiment: 2 | Episode: 539 | Score: -185.32 | Avg Score: -131.69 |\n",
"| Experiment: 2 | Episode: 540 | Score: -59.39 | Avg Score: -130.39 |\n",
"| Experiment: 2 | Episode: 541 | Score: -185.74 | Avg Score: -131.56 |\n",
"| Experiment: 2 | Episode: 542 | Score: -59.19 | Avg Score: -131.49 |\n",
"| Experiment: 2 | Episode: 543 | Score: 77.34 | Avg Score: -130.23 |\n",
"| Experiment: 2 | Episode: 544 | Score: -127.26 | Avg Score: -130.95 |\n",
"| Experiment: 2 | Episode: 545 | Score: -129.32 | Avg Score: -131.74 |\n",
"| Experiment: 2 | Episode: 546 | Score: -35.94 | Avg Score: -130.22 |\n",
"| Experiment: 2 | Episode: 547 | Score: -78.82 | Avg Score: -130.09 |\n",
"| Experiment: 2 | Episode: 548 | Score: -53.49 | Avg Score: -129.13 |\n",
"| Experiment: 2 | Episode: 549 | Score: -123.9 | Avg Score: -130.19 |\n",
"| Experiment: 2 | Episode: 550 | Score: -30.98 | Avg Score: -129.2 |\n",
"| Experiment: 2 | Episode: 551 | Score: -193.58 | Avg Score: -130.29 |\n",
"| Experiment: 2 | Episode: 552 | Score: -180.36 | Avg Score: -131.41 |\n",
"| Experiment: 2 | Episode: 553 | Score: -112.54 | Avg Score: -131.23 |\n",
"| Experiment: 2 | Episode: 554 | Score: -106.21 | Avg Score: -131.51 |\n",
"| Experiment: 2 | Episode: 555 | Score: -63.1 | Avg Score: -131.2 |\n",
"| Experiment: 2 | Episode: 556 | Score: -102.25 | Avg Score: -131.37 |\n",
"| Experiment: 2 | Episode: 557 | Score: -124.57 | Avg Score: -132.09 |\n",
"| Experiment: 2 | Episode: 558 | Score: -45.1 | Avg Score: -131.5 |\n",
"| Experiment: 2 | Episode: 559 | Score: -87.25 | Avg Score: -130.22 |\n",
"| Experiment: 2 | Episode: 560 | Score: -77.4 | Avg Score: -130.03 |\n",
"| Experiment: 2 | Episode: 561 | Score: -74.17 | Avg Score: -129.8 |\n",
"| Experiment: 2 | Episode: 562 | Score: -141.51 | Avg Score: -131.1 |\n",
"| Experiment: 2 | Episode: 563 | Score: -29.67 | Avg Score: -130.88 |\n",
"| Experiment: 2 | Episode: 564 | Score: -322.13 | Avg Score: -130.67 |\n",
"| Experiment: 2 | Episode: 565 | Score: -91.62 | Avg Score: -130.45 |\n",
"| Experiment: 2 | Episode: 566 | Score: -94.79 | Avg Score: -126.42 |\n",
"| Experiment: 2 | Episode: 567 | Score: -246.03 | Avg Score: -127.71 |\n",
"| Experiment: 2 | Episode: 568 | Score: -229.36 | Avg Score: -128.37 |\n",
"| Experiment: 2 | Episode: 569 | Score: -121.76 | Avg Score: -128.29 |\n",
"| Experiment: 2 | Episode: 570 | Score: -230.28 | Avg Score: -130.06 |\n",
"| Experiment: 2 | Episode: 571 | Score: -55.04 | Avg Score: -128.43 |\n",
"| Experiment: 2 | Episode: 572 | Score: -99.39 | Avg Score: -128.45 |\n",
"| Experiment: 2 | Episode: 573 | Score: -121.61 | Avg Score: -126.3 |\n",
"| Experiment: 2 | Episode: 574 | Score: -171.45 | Avg Score: -126.32 |\n",
"| Experiment: 2 | Episode: 575 | Score: -56.56 | Avg Score: -125.71 |\n",
"| Experiment: 2 | Episode: 576 | Score: -165.46 | Avg Score: -125.68 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 2 | Episode: 577 | Score: -242.9 | Avg Score: -124.8 |\n",
"| Experiment: 2 | Episode: 578 | Score: -79.98 | Avg Score: -124.41 |\n",
"| Experiment: 2 | Episode: 579 | Score: -120.06 | Avg Score: -124.34 |\n",
"| Experiment: 2 | Episode: 580 | Score: -64.25 | Avg Score: -123.82 |\n",
"| Experiment: 2 | Episode: 581 | Score: -126.79 | Avg Score: -124.73 |\n",
"| Experiment: 2 | Episode: 582 | Score: -54.24 | Avg Score: -124.74 |\n",
"| Experiment: 2 | Episode: 583 | Score: -58.24 | Avg Score: -124.39 |\n",
"| Experiment: 2 | Episode: 584 | Score: -63.67 | Avg Score: -124.18 |\n",
"| Experiment: 2 | Episode: 585 | Score: -180.84 | Avg Score: -125.11 |\n",
"| Experiment: 2 | Episode: 586 | Score: -428.73 | Avg Score: -129.1 |\n",
"| Experiment: 2 | Episode: 587 | Score: -343.91 | Avg Score: -130.64 |\n",
"| Experiment: 2 | Episode: 588 | Score: -123.91 | Avg Score: -130.69 |\n",
"| Experiment: 2 | Episode: 589 | Score: -263.58 | Avg Score: -130.78 |\n",
"| Experiment: 2 | Episode: 590 | Score: -106.87 | Avg Score: -132.06 |\n",
"| Experiment: 2 | Episode: 591 | Score: -90.48 | Avg Score: -129.9 |\n",
"| Experiment: 2 | Episode: 592 | Score: -332.24 | Avg Score: -130.64 |\n",
"| Experiment: 2 | Episode: 593 | Score: -312.55 | Avg Score: -132.17 |\n",
"| Experiment: 2 | Episode: 594 | Score: -86.38 | Avg Score: -132.1 |\n",
"| Experiment: 2 | Episode: 595 | Score: -121.09 | Avg Score: -132.54 |\n",
"| Experiment: 2 | Episode: 596 | Score: -66.36 | Avg Score: -131.54 |\n",
"| Experiment: 2 | Episode: 597 | Score: -111.29 | Avg Score: -131.03 |\n",
"| Experiment: 2 | Episode: 598 | Score: -288.67 | Avg Score: -133.0 |\n",
"| Experiment: 2 | Episode: 599 | Score: -144.03 | Avg Score: -133.7 |\n",
"| Experiment: 2 | Episode: 600 | Score: -110.33 | Avg Score: -134.07 |\n",
"| Experiment: 2 | Episode: 601 | Score: -30.63 | Avg Score: -132.99 |\n",
"| Experiment: 2 | Episode: 602 | Score: -230.9 | Avg Score: -134.69 |\n",
"| Experiment: 2 | Episode: 603 | Score: -326.19 | Avg Score: -135.99 |\n",
"| Experiment: 2 | Episode: 604 | Score: -138.36 | Avg Score: -136.44 |\n",
"| Experiment: 2 | Episode: 605 | Score: -176.52 | Avg Score: -137.63 |\n",
"| Experiment: 2 | Episode: 606 | Score: -60.96 | Avg Score: -136.56 |\n",
"| Experiment: 2 | Episode: 607 | Score: -84.68 | Avg Score: -136.92 |\n",
"| Experiment: 2 | Episode: 608 | Score: -68.98 | Avg Score: -136.93 |\n",
"| Experiment: 2 | Episode: 609 | Score: -131.07 | Avg Score: -134.67 |\n",
"| Experiment: 2 | Episode: 610 | Score: -62.04 | Avg Score: -132.74 |\n",
"| Experiment: 2 | Episode: 611 | Score: -64.01 | Avg Score: -131.18 |\n",
"| Experiment: 2 | Episode: 612 | Score: -73.48 | Avg Score: -130.3 |\n",
"| Experiment: 2 | Episode: 613 | Score: -69.97 | Avg Score: -130.35 |\n",
"| Experiment: 2 | Episode: 614 | Score: -352.94 | Avg Score: -133.01 |\n",
"| Experiment: 2 | Episode: 615 | Score: -234.02 | Avg Score: -134.34 |\n",
"| Experiment: 2 | Episode: 616 | Score: -102.57 | Avg Score: -134.44 |\n",
"| Experiment: 2 | Episode: 617 | Score: -81.2 | Avg Score: -134.24 |\n",
"| Experiment: 2 | Episode: 618 | Score: -40.24 | Avg Score: -133.8 |\n",
"| Experiment: 2 | Episode: 619 | Score: -123.56 | Avg Score: -135.03 |\n",
"| Experiment: 2 | Episode: 620 | Score: -78.12 | Avg Score: -132.71 |\n",
"| Experiment: 2 | Episode: 621 | Score: -63.82 | Avg Score: -132.66 |\n",
"| Experiment: 2 | Episode: 622 | Score: -407.69 | Avg Score: -136.15 |\n",
"| Experiment: 2 | Episode: 623 | Score: -50.43 | Avg Score: -133.54 |\n",
"| Experiment: 2 | Episode: 624 | Score: -163.15 | Avg Score: -133.55 |\n",
"| Experiment: 2 | Episode: 625 | Score: -89.38 | Avg Score: -133.96 |\n",
"| Experiment: 2 | Episode: 626 | Score: -283.46 | Avg Score: -135.32 |\n",
"| Experiment: 2 | Episode: 627 | Score: -49.53 | Avg Score: -133.61 |\n",
"| Experiment: 2 | Episode: 628 | Score: -71.25 | Avg Score: -133.83 |\n",
"| Experiment: 2 | Episode: 629 | Score: -95.72 | Avg Score: -133.39 |\n",
"| Experiment: 2 | Episode: 630 | Score: -254.96 | Avg Score: -134.92 |\n",
"| Experiment: 2 | Episode: 631 | Score: -48.33 | Avg Score: -134.6 |\n",
"| Experiment: 2 | Episode: 632 | Score: -285.29 | Avg Score: -135.94 |\n",
"| Experiment: 2 | Episode: 633 | Score: -134.39 | Avg Score: -134.84 |\n",
"| Experiment: 2 | Episode: 634 | Score: -103.59 | Avg Score: -134.88 |\n",
"| Experiment: 2 | Episode: 635 | Score: -131.67 | Avg Score: -135.17 |\n",
"| Experiment: 2 | Episode: 636 | Score: -125.9 | Avg Score: -135.79 |\n",
"| Experiment: 2 | Episode: 637 | Score: -57.64 | Avg Score: -134.6 |\n",
"| Experiment: 2 | Episode: 638 | Score: -111.77 | Avg Score: -133.15 |\n",
"| Experiment: 2 | Episode: 639 | Score: -8.8 | Avg Score: -131.39 |\n",
"| Experiment: 2 | Episode: 640 | Score: -104.73 | Avg Score: -131.84 |\n",
"| Experiment: 2 | Episode: 641 | Score: -113.37 | Avg Score: -131.11 |\n",
"| Experiment: 2 | Episode: 642 | Score: -252.26 | Avg Score: -133.05 |\n",
"| Experiment: 2 | Episode: 643 | Score: -244.19 | Avg Score: -136.26 |\n",
"| Experiment: 2 | Episode: 644 | Score: -187.73 | Avg Score: -136.87 |\n",
"| Experiment: 2 | Episode: 645 | Score: -141.08 | Avg Score: -136.98 |\n",
"| Experiment: 2 | Episode: 646 | Score: -64.99 | Avg Score: -137.27 |\n",
"| Experiment: 2 | Episode: 647 | Score: -64.86 | Avg Score: -137.13 |\n",
"| Experiment: 2 | Episode: 648 | Score: -180.18 | Avg Score: -138.4 |\n",
"| Experiment: 2 | Episode: 649 | Score: -105.38 | Avg Score: -138.22 |\n",
"| Experiment: 2 | Episode: 650 | Score: -137.5 | Avg Score: -139.28 |\n",
"| Experiment: 2 | Episode: 651 | Score: -96.05 | Avg Score: -138.31 |\n",
"| Experiment: 2 | Episode: 652 | Score: -114.66 | Avg Score: -137.65 |\n",
"| Experiment: 2 | Episode: 653 | Score: -108.01 | Avg Score: -137.6 |\n",
"| Experiment: 2 | Episode: 654 | Score: -333.01 | Avg Score: -139.87 |\n",
"| Experiment: 2 | Episode: 655 | Score: -179.63 | Avg Score: -141.04 |\n",
"| Experiment: 2 | Episode: 656 | Score: -49.76 | Avg Score: -140.51 |\n",
"| Experiment: 2 | Episode: 657 | Score: -179.71 | Avg Score: -141.06 |\n",
"| Experiment: 2 | Episode: 658 | Score: -125.21 | Avg Score: -141.86 |\n",
"| Experiment: 2 | Episode: 659 | Score: -91.48 | Avg Score: -141.91 |\n",
"| Experiment: 2 | Episode: 660 | Score: -242.48 | Avg Score: -143.56 |\n",
"| Experiment: 2 | Episode: 661 | Score: -150.45 | Avg Score: -144.32 |\n",
"| Experiment: 2 | Episode: 662 | Score: -258.33 | Avg Score: -145.49 |\n",
"| Experiment: 2 | Episode: 663 | Score: -228.78 | Avg Score: -147.48 |\n",
"| Experiment: 2 | Episode: 664 | Score: -59.59 | Avg Score: -144.85 |\n",
"| Experiment: 2 | Episode: 665 | Score: -57.27 | Avg Score: -144.51 |\n",
"| Experiment: 2 | Episode: 666 | Score: -85.51 | Avg Score: -144.42 |\n",
"| Experiment: 2 | Episode: 667 | Score: -89.78 | Avg Score: -142.86 |\n",
"| Experiment: 2 | Episode: 668 | Score: -69.63 | Avg Score: -141.26 |\n",
"| Experiment: 2 | Episode: 669 | Score: -353.48 | Avg Score: -143.58 |\n",
"| Experiment: 2 | Episode: 670 | Score: -122.67 | Avg Score: -142.5 |\n",
"| Experiment: 2 | Episode: 671 | Score: -381.25 | Avg Score: -145.76 |\n",
"| Experiment: 2 | Episode: 672 | Score: -150.05 | Avg Score: -146.27 |\n",
"| Experiment: 2 | Episode: 673 | Score: -80.76 | Avg Score: -145.86 |\n",
"| Experiment: 2 | Episode: 674 | Score: -44.6 | Avg Score: -144.59 |\n",
"| Experiment: 2 | Episode: 675 | Score: -171.33 | Avg Score: -145.74 |\n",
"| Experiment: 2 | Episode: 676 | Score: -64.7 | Avg Score: -144.73 |\n",
"| Experiment: 2 | Episode: 677 | Score: -76.97 | Avg Score: -143.07 |\n",
"| Experiment: 2 | Episode: 678 | Score: -121.75 | Avg Score: -143.49 |\n",
"| Experiment: 2 | Episode: 679 | Score: -68.0 | Avg Score: -142.97 |\n",
"| Experiment: 2 | Episode: 680 | Score: -146.32 | Avg Score: -143.79 |\n",
"| Experiment: 2 | Episode: 681 | Score: -60.33 | Avg Score: -143.12 |\n",
"| Experiment: 2 | Episode: 682 | Score: -47.24 | Avg Score: -143.05 |\n",
"| Experiment: 2 | Episode: 683 | Score: -114.07 | Avg Score: -143.61 |\n",
"| Experiment: 2 | Episode: 684 | Score: -175.4 | Avg Score: -144.73 |\n",
"| Experiment: 2 | Episode: 685 | Score: -72.83 | Avg Score: -143.65 |\n",
"| Experiment: 2 | Episode: 686 | Score: -70.96 | Avg Score: -140.07 |\n",
"| Experiment: 2 | Episode: 687 | Score: -117.12 | Avg Score: -137.8 |\n",
"| Experiment: 2 | Episode: 688 | Score: -75.78 | Avg Score: -137.32 |\n",
"| Experiment: 2 | Episode: 689 | Score: -133.47 | Avg Score: -136.02 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 2 | Episode: 690 | Score: -136.17 | Avg Score: -136.32 |\n",
"| Experiment: 2 | Episode: 691 | Score: -50.04 | Avg Score: -135.91 |\n",
"| Experiment: 2 | Episode: 692 | Score: -90.54 | Avg Score: -133.49 |\n",
"| Experiment: 2 | Episode: 693 | Score: -51.72 | Avg Score: -130.89 |\n",
"| Experiment: 2 | Episode: 694 | Score: -203.3 | Avg Score: -132.05 |\n",
"| Experiment: 2 | Episode: 695 | Score: 8.21 | Avg Score: -130.76 |\n",
"| Experiment: 2 | Episode: 696 | Score: -106.93 | Avg Score: -131.17 |\n",
"| Experiment: 2 | Episode: 697 | Score: -204.14 | Avg Score: -132.1 |\n",
"| Experiment: 2 | Episode: 698 | Score: -122.76 | Avg Score: -130.44 |\n",
"| Experiment: 2 | Episode: 699 | Score: -128.95 | Avg Score: -130.29 |\n",
"| Experiment: 2 | Episode: 700 | Score: -111.23 | Avg Score: -130.29 |\n",
"| Experiment: 2 | Episode: 701 | Score: -155.6 | Avg Score: -131.54 |\n",
"| Experiment: 2 | Episode: 702 | Score: -190.52 | Avg Score: -131.14 |\n",
"| Experiment: 2 | Episode: 703 | Score: -103.1 | Avg Score: -128.91 |\n",
"| Experiment: 2 | Episode: 704 | Score: -319.36 | Avg Score: -130.72 |\n",
"| Experiment: 2 | Episode: 705 | Score: -89.84 | Avg Score: -129.85 |\n",
"| Experiment: 2 | Episode: 706 | Score: -173.07 | Avg Score: -130.97 |\n",
"| Experiment: 2 | Episode: 707 | Score: -161.3 | Avg Score: -131.74 |\n",
"| Experiment: 2 | Episode: 708 | Score: -122.62 | Avg Score: -132.28 |\n",
"| Experiment: 2 | Episode: 709 | Score: -89.62 | Avg Score: -131.86 |\n",
"| Experiment: 2 | Episode: 710 | Score: -108.0 | Avg Score: -132.32 |\n",
"| Experiment: 2 | Episode: 711 | Score: -305.06 | Avg Score: -134.73 |\n",
"| Experiment: 2 | Episode: 712 | Score: -179.48 | Avg Score: -135.79 |\n",
"| Experiment: 2 | Episode: 713 | Score: -111.35 | Avg Score: -136.21 |\n",
"| Experiment: 2 | Episode: 714 | Score: -135.42 | Avg Score: -134.03 |\n",
"| Experiment: 2 | Episode: 715 | Score: -68.21 | Avg Score: -132.37 |\n",
"| Experiment: 2 | Episode: 716 | Score: -82.22 | Avg Score: -132.17 |\n",
"| Experiment: 2 | Episode: 717 | Score: -134.68 | Avg Score: -132.7 |\n",
"| Experiment: 2 | Episode: 718 | Score: -66.45 | Avg Score: -132.97 |\n",
"| Experiment: 2 | Episode: 719 | Score: -87.68 | Avg Score: -132.61 |\n",
"| Experiment: 2 | Episode: 720 | Score: -132.74 | Avg Score: -133.15 |\n",
"| Experiment: 2 | Episode: 721 | Score: -46.52 | Avg Score: -132.98 |\n",
"| Experiment: 2 | Episode: 722 | Score: -50.91 | Avg Score: -129.41 |\n",
"| Experiment: 2 | Episode: 723 | Score: -80.81 | Avg Score: -129.72 |\n",
"| Experiment: 2 | Episode: 724 | Score: -199.41 | Avg Score: -130.08 |\n",
"| Experiment: 2 | Episode: 725 | Score: -117.29 | Avg Score: -130.36 |\n",
"| Experiment: 2 | Episode: 726 | Score: -88.35 | Avg Score: -128.41 |\n",
"| Experiment: 2 | Episode: 727 | Score: -100.61 | Avg Score: -128.92 |\n",
"| Experiment: 2 | Episode: 728 | Score: -44.7 | Avg Score: -128.65 |\n",
"| Experiment: 2 | Episode: 729 | Score: -81.94 | Avg Score: -128.51 |\n",
"| Experiment: 2 | Episode: 730 | Score: -244.33 | Avg Score: -128.41 |\n",
"| Experiment: 2 | Episode: 731 | Score: -61.63 | Avg Score: -128.54 |\n",
"| Experiment: 2 | Episode: 732 | Score: -156.79 | Avg Score: -127.26 |\n",
"| Experiment: 2 | Episode: 733 | Score: -210.12 | Avg Score: -128.01 |\n",
"| Experiment: 2 | Episode: 734 | Score: -56.27 | Avg Score: -127.54 |\n",
"| Experiment: 2 | Episode: 735 | Score: -197.01 | Avg Score: -128.19 |\n",
"| Experiment: 2 | Episode: 736 | Score: -85.43 | Avg Score: -127.79 |\n",
"| Experiment: 2 | Episode: 737 | Score: -78.79 | Avg Score: -128.0 |\n",
"| Experiment: 2 | Episode: 738 | Score: -195.41 | Avg Score: -128.84 |\n",
"| Experiment: 2 | Episode: 739 | Score: -63.55 | Avg Score: -129.38 |\n",
"| Experiment: 2 | Episode: 740 | Score: -87.37 | Avg Score: -129.21 |\n",
"| Experiment: 2 | Episode: 741 | Score: -159.73 | Avg Score: -129.67 |\n",
"| Experiment: 2 | Episode: 742 | Score: -125.21 | Avg Score: -128.4 |\n",
"| Experiment: 2 | Episode: 743 | Score: -106.35 | Avg Score: -127.03 |\n",
"| Experiment: 2 | Episode: 744 | Score: -64.28 | Avg Score: -125.79 |\n",
"| Experiment: 2 | Episode: 745 | Score: -67.94 | Avg Score: -125.06 |\n",
"| Experiment: 2 | Episode: 746 | Score: -194.34 | Avg Score: -126.35 |\n",
"| Experiment: 2 | Episode: 747 | Score: -371.46 | Avg Score: -129.42 |\n",
"| Experiment: 2 | Episode: 748 | Score: -98.17 | Avg Score: -128.6 |\n",
"| Experiment: 2 | Episode: 749 | Score: -159.41 | Avg Score: -129.14 |\n",
"| Experiment: 2 | Episode: 750 | Score: -110.4 | Avg Score: -128.87 |\n",
"| Experiment: 2 | Episode: 751 | Score: -59.03 | Avg Score: -128.5 |\n",
"| Experiment: 2 | Episode: 752 | Score: -140.4 | Avg Score: -128.76 |\n",
"| Experiment: 2 | Episode: 753 | Score: -107.61 | Avg Score: -128.75 |\n",
"| Experiment: 2 | Episode: 754 | Score: -75.25 | Avg Score: -126.17 |\n",
"| Experiment: 2 | Episode: 755 | Score: -97.32 | Avg Score: -125.35 |\n",
"| Experiment: 2 | Episode: 756 | Score: -89.77 | Avg Score: -125.75 |\n",
"| Experiment: 2 | Episode: 757 | Score: -118.28 | Avg Score: -125.14 |\n",
"| Experiment: 2 | Episode: 758 | Score: -138.3 | Avg Score: -125.27 |\n",
"| Experiment: 2 | Episode: 759 | Score: -220.5 | Avg Score: -126.56 |\n",
"| Experiment: 2 | Episode: 760 | Score: -309.44 | Avg Score: -127.23 |\n",
"| Experiment: 2 | Episode: 761 | Score: -496.63 | Avg Score: -130.69 |\n",
"| Experiment: 2 | Episode: 762 | Score: -171.43 | Avg Score: -129.82 |\n",
"| Experiment: 2 | Episode: 763 | Score: -122.19 | Avg Score: -128.75 |\n",
"| Experiment: 2 | Episode: 764 | Score: -134.19 | Avg Score: -129.5 |\n",
"| Experiment: 2 | Episode: 765 | Score: -166.03 | Avg Score: -130.59 |\n",
"| Experiment: 2 | Episode: 766 | Score: -340.56 | Avg Score: -133.14 |\n",
"| Experiment: 2 | Episode: 767 | Score: -111.38 | Avg Score: -133.35 |\n",
"| Experiment: 2 | Episode: 768 | Score: -269.96 | Avg Score: -135.36 |\n",
"| Experiment: 2 | Episode: 769 | Score: -297.9 | Avg Score: -134.8 |\n",
"| Experiment: 2 | Episode: 770 | Score: -124.52 | Avg Score: -134.82 |\n",
"| Experiment: 2 | Episode: 771 | Score: -42.05 | Avg Score: -131.43 |\n",
"| Experiment: 2 | Episode: 772 | Score: -152.26 | Avg Score: -131.45 |\n",
"| Experiment: 2 | Episode: 773 | Score: -106.08 | Avg Score: -131.7 |\n",
"| Experiment: 2 | Episode: 774 | Score: -87.68 | Avg Score: -132.13 |\n",
"| Experiment: 2 | Episode: 775 | Score: -104.24 | Avg Score: -131.46 |\n",
"| Experiment: 2 | Episode: 776 | Score: -103.47 | Avg Score: -131.85 |\n",
"| Experiment: 2 | Episode: 777 | Score: -76.53 | Avg Score: -131.85 |\n",
"| Experiment: 2 | Episode: 778 | Score: -219.95 | Avg Score: -132.83 |\n",
"| Experiment: 2 | Episode: 779 | Score: -302.48 | Avg Score: -135.17 |\n",
"| Experiment: 2 | Episode: 780 | Score: -84.32 | Avg Score: -134.55 |\n",
"| Experiment: 2 | Episode: 781 | Score: -328.84 | Avg Score: -137.24 |\n",
"| Experiment: 2 | Episode: 782 | Score: -133.8 | Avg Score: -138.1 |\n",
"| Experiment: 2 | Episode: 783 | Score: -309.11 | Avg Score: -140.05 |\n",
"| Experiment: 2 | Episode: 784 | Score: -124.55 | Avg Score: -139.55 |\n",
"| Experiment: 2 | Episode: 785 | Score: -329.46 | Avg Score: -142.11 |\n",
"| Experiment: 2 | Episode: 786 | Score: -123.81 | Avg Score: -142.64 |\n",
"| Experiment: 2 | Episode: 787 | Score: -188.65 | Avg Score: -143.36 |\n",
"| Experiment: 2 | Episode: 788 | Score: -257.3 | Avg Score: -145.17 |\n",
"| Experiment: 2 | Episode: 789 | Score: -110.59 | Avg Score: -144.94 |\n",
"| Experiment: 2 | Episode: 790 | Score: -175.23 | Avg Score: -145.33 |\n",
"| Experiment: 2 | Episode: 791 | Score: -118.87 | Avg Score: -146.02 |\n",
"| Experiment: 2 | Episode: 792 | Score: -156.09 | Avg Score: -146.68 |\n",
"| Experiment: 2 | Episode: 793 | Score: -144.15 | Avg Score: -147.6 |\n",
"| Experiment: 2 | Episode: 794 | Score: -112.67 | Avg Score: -146.69 |\n",
"| Experiment: 2 | Episode: 795 | Score: -73.67 | Avg Score: -147.51 |\n",
"| Experiment: 2 | Episode: 796 | Score: -90.64 | Avg Score: -147.35 |\n",
"| Experiment: 2 | Episode: 797 | Score: -182.76 | Avg Score: -147.14 |\n",
"| Experiment: 2 | Episode: 798 | Score: -226.84 | Avg Score: -148.18 |\n",
"| Experiment: 2 | Episode: 799 | Score: -55.83 | Avg Score: -147.45 |\n",
"| Experiment: 2 | Episode: 800 | Score: -101.56 | Avg Score: -147.35 |\n",
"| Experiment: 2 | Episode: 801 | Score: -78.98 | Avg Score: -146.58 |\n",
"| Experiment: 2 | Episode: 802 | Score: -37.44 | Avg Score: -145.05 |\n",
"| Experiment: 2 | Episode: 803 | Score: -197.58 | Avg Score: -146.0 |\n",
"| Experiment: 2 | Episode: 804 | Score: -139.8 | Avg Score: -144.2 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 2 | Episode: 805 | Score: -65.49 | Avg Score: -143.96 |\n",
"| Experiment: 2 | Episode: 806 | Score: -83.82 | Avg Score: -143.07 |\n",
"| Experiment: 2 | Episode: 807 | Score: -93.3 | Avg Score: -142.39 |\n",
"| Experiment: 2 | Episode: 808 | Score: -170.07 | Avg Score: -142.86 |\n",
"| Experiment: 2 | Episode: 809 | Score: -98.11 | Avg Score: -142.95 |\n",
"| Experiment: 2 | Episode: 810 | Score: -48.29 | Avg Score: -142.35 |\n",
"| Experiment: 2 | Episode: 811 | Score: -163.47 | Avg Score: -140.93 |\n",
"| Experiment: 2 | Episode: 812 | Score: -87.67 | Avg Score: -140.01 |\n",
"| Experiment: 2 | Episode: 813 | Score: -280.44 | Avg Score: -141.71 |\n",
"| Experiment: 2 | Episode: 814 | Score: -61.03 | Avg Score: -140.96 |\n",
"| Experiment: 2 | Episode: 815 | Score: -351.4 | Avg Score: -143.79 |\n",
"| Experiment: 2 | Episode: 816 | Score: -286.66 | Avg Score: -145.84 |\n",
"| Experiment: 2 | Episode: 817 | Score: -23.24 | Avg Score: -144.72 |\n",
"| Experiment: 2 | Episode: 818 | Score: -333.75 | Avg Score: -147.4 |\n",
"| Experiment: 2 | Episode: 819 | Score: -111.99 | Avg Score: -147.64 |\n",
"| Experiment: 2 | Episode: 820 | Score: -172.46 | Avg Score: -148.04 |\n",
"| Experiment: 2 | Episode: 821 | Score: -62.41 | Avg Score: -148.2 |\n",
"| Experiment: 2 | Episode: 822 | Score: -61.39 | Avg Score: -148.3 |\n",
"| Experiment: 2 | Episode: 823 | Score: -144.71 | Avg Score: -148.94 |\n",
"| Experiment: 2 | Episode: 824 | Score: -85.42 | Avg Score: -147.8 |\n",
"| Experiment: 2 | Episode: 825 | Score: -67.46 | Avg Score: -147.3 |\n",
"| Experiment: 2 | Episode: 826 | Score: -236.0 | Avg Score: -148.78 |\n",
"| Experiment: 2 | Episode: 827 | Score: -60.95 | Avg Score: -148.38 |\n",
"| Experiment: 2 | Episode: 828 | Score: -46.28 | Avg Score: -148.4 |\n",
"| Experiment: 2 | Episode: 829 | Score: -61.04 | Avg Score: -148.19 |\n",
"| Experiment: 2 | Episode: 830 | Score: -141.95 | Avg Score: -147.16 |\n",
"| Experiment: 2 | Episode: 831 | Score: -4.0 | Avg Score: -146.59 |\n",
"| Experiment: 2 | Episode: 832 | Score: -88.44 | Avg Score: -145.9 |\n",
"| Experiment: 2 | Episode: 833 | Score: -80.52 | Avg Score: -144.61 |\n",
"| Experiment: 2 | Episode: 834 | Score: -109.01 | Avg Score: -145.14 |\n",
"| Experiment: 2 | Episode: 835 | Score: -107.75 | Avg Score: -144.24 |\n",
"| Experiment: 2 | Episode: 836 | Score: -152.51 | Avg Score: -144.91 |\n",
"| Experiment: 2 | Episode: 837 | Score: -106.26 | Avg Score: -145.19 |\n",
"| Experiment: 2 | Episode: 838 | Score: -292.74 | Avg Score: -146.16 |\n",
"| Experiment: 2 | Episode: 839 | Score: -72.42 | Avg Score: -146.25 |\n",
"| Experiment: 2 | Episode: 840 | Score: -54.05 | Avg Score: -145.92 |\n",
"| Experiment: 2 | Episode: 841 | Score: -114.29 | Avg Score: -145.46 |\n",
"| Experiment: 2 | Episode: 842 | Score: -94.0 | Avg Score: -145.15 |\n",
"| Experiment: 2 | Episode: 843 | Score: -56.23 | Avg Score: -144.65 |\n",
"| Experiment: 2 | Episode: 844 | Score: -64.25 | Avg Score: -144.65 |\n",
"| Experiment: 2 | Episode: 845 | Score: -147.4 | Avg Score: -145.44 |\n",
"| Experiment: 2 | Episode: 846 | Score: -143.98 | Avg Score: -144.94 |\n",
"| Experiment: 2 | Episode: 847 | Score: -98.95 | Avg Score: -142.22 |\n",
"| Experiment: 2 | Episode: 848 | Score: -56.4 | Avg Score: -141.8 |\n",
"| Experiment: 2 | Episode: 849 | Score: -92.13 | Avg Score: -141.12 |\n",
"| Experiment: 2 | Episode: 850 | Score: -142.94 | Avg Score: -141.45 |\n",
"| Experiment: 2 | Episode: 851 | Score: -76.62 | Avg Score: -141.63 |\n",
"| Experiment: 2 | Episode: 852 | Score: -166.2 | Avg Score: -141.88 |\n",
"| Experiment: 2 | Episode: 853 | Score: -61.38 | Avg Score: -141.42 |\n",
"| Experiment: 2 | Episode: 854 | Score: -49.83 | Avg Score: -141.17 |\n",
"| Experiment: 2 | Episode: 855 | Score: -218.65 | Avg Score: -142.38 |\n",
"| Experiment: 2 | Episode: 856 | Score: -108.6 | Avg Score: -142.57 |\n",
"| Experiment: 2 | Episode: 857 | Score: -253.71 | Avg Score: -143.92 |\n",
"| Experiment: 2 | Episode: 858 | Score: -325.48 | Avg Score: -145.8 |\n",
"| Experiment: 2 | Episode: 859 | Score: -2.76 | Avg Score: -143.62 |\n",
"| Experiment: 2 | Episode: 860 | Score: -257.93 | Avg Score: -143.1 |\n",
"| Experiment: 2 | Episode: 861 | Score: -49.41 | Avg Score: -138.63 |\n",
"| Experiment: 2 | Episode: 862 | Score: -309.09 | Avg Score: -140.01 |\n",
"| Experiment: 2 | Episode: 863 | Score: -173.66 | Avg Score: -140.52 |\n",
"| Experiment: 2 | Episode: 864 | Score: -56.41 | Avg Score: -139.74 |\n",
"| Experiment: 2 | Episode: 865 | Score: -65.15 | Avg Score: -138.74 |\n",
"| Experiment: 2 | Episode: 866 | Score: -76.14 | Avg Score: -136.09 |\n",
"| Experiment: 2 | Episode: 867 | Score: -149.18 | Avg Score: -136.47 |\n",
"| Experiment: 2 | Episode: 868 | Score: -170.39 | Avg Score: -135.47 |\n",
"| Experiment: 2 | Episode: 869 | Score: -94.61 | Avg Score: -133.44 |\n",
"| Experiment: 2 | Episode: 870 | Score: -91.57 | Avg Score: -133.11 |\n",
"| Experiment: 2 | Episode: 871 | Score: -254.21 | Avg Score: -135.23 |\n",
"| Experiment: 2 | Episode: 872 | Score: -1.76 | Avg Score: -133.73 |\n",
"| Experiment: 2 | Episode: 873 | Score: -122.46 | Avg Score: -133.89 |\n",
"| Experiment: 2 | Episode: 874 | Score: -228.71 | Avg Score: -135.3 |\n",
"| Experiment: 2 | Episode: 875 | Score: -53.08 | Avg Score: -134.79 |\n",
"| Experiment: 2 | Episode: 876 | Score: -99.22 | Avg Score: -134.75 |\n",
"| Experiment: 2 | Episode: 877 | Score: 0.59 | Avg Score: -133.98 |\n",
"| Experiment: 2 | Episode: 878 | Score: -111.97 | Avg Score: -132.9 |\n",
"| Experiment: 2 | Episode: 879 | Score: -184.75 | Avg Score: -131.72 |\n",
"| Experiment: 2 | Episode: 880 | Score: -82.66 | Avg Score: -131.7 |\n",
"| Experiment: 2 | Episode: 881 | Score: -135.45 | Avg Score: -129.77 |\n",
"| Experiment: 2 | Episode: 882 | Score: -135.09 | Avg Score: -129.78 |\n",
"| Experiment: 2 | Episode: 883 | Score: -220.55 | Avg Score: -128.9 |\n",
"| Experiment: 2 | Episode: 884 | Score: -187.51 | Avg Score: -129.53 |\n",
"| Experiment: 2 | Episode: 885 | Score: -113.33 | Avg Score: -127.36 |\n",
"| Experiment: 2 | Episode: 886 | Score: -80.61 | Avg Score: -126.93 |\n",
"| Experiment: 2 | Episode: 887 | Score: -64.69 | Avg Score: -125.69 |\n",
"| Experiment: 2 | Episode: 888 | Score: -124.44 | Avg Score: -124.36 |\n",
"| Experiment: 2 | Episode: 889 | Score: -157.07 | Avg Score: -124.83 |\n",
"| Experiment: 2 | Episode: 890 | Score: -35.1 | Avg Score: -123.43 |\n",
"| Experiment: 2 | Episode: 891 | Score: -163.29 | Avg Score: -123.87 |\n",
"| Experiment: 2 | Episode: 892 | Score: -115.39 | Avg Score: -123.46 |\n",
"| Experiment: 2 | Episode: 893 | Score: -204.11 | Avg Score: -124.06 |\n",
"| Experiment: 2 | Episode: 894 | Score: -237.87 | Avg Score: -125.32 |\n",
"| Experiment: 2 | Episode: 895 | Score: -120.14 | Avg Score: -125.78 |\n",
"| Experiment: 2 | Episode: 896 | Score: -85.49 | Avg Score: -125.73 |\n",
"| Experiment: 2 | Episode: 897 | Score: -235.72 | Avg Score: -126.26 |\n",
"| Experiment: 2 | Episode: 898 | Score: -66.15 | Avg Score: -124.65 |\n",
"| Experiment: 2 | Episode: 899 | Score: -164.13 | Avg Score: -125.74 |\n",
"| Experiment: 2 | Episode: 900 | Score: -144.82 | Avg Score: -126.17 |\n",
"| Experiment: 2 | Episode: 901 | Score: -194.7 | Avg Score: -127.33 |\n",
"| Experiment: 2 | Episode: 902 | Score: -70.92 | Avg Score: -127.66 |\n",
"| Experiment: 2 | Episode: 903 | Score: -179.29 | Avg Score: -127.48 |\n",
"| Experiment: 2 | Episode: 904 | Score: -94.08 | Avg Score: -127.02 |\n",
"| Experiment: 2 | Episode: 905 | Score: -103.14 | Avg Score: -127.4 |\n",
"| Experiment: 2 | Episode: 906 | Score: -50.01 | Avg Score: -127.06 |\n",
"| Experiment: 2 | Episode: 907 | Score: -89.85 | Avg Score: -127.02 |\n",
"| Experiment: 2 | Episode: 908 | Score: -82.6 | Avg Score: -126.15 |\n",
"| Experiment: 2 | Episode: 909 | Score: -184.98 | Avg Score: -127.02 |\n",
"| Experiment: 2 | Episode: 910 | Score: -75.81 | Avg Score: -127.29 |\n",
"| Experiment: 2 | Episode: 911 | Score: -192.62 | Avg Score: -127.58 |\n",
"| Experiment: 2 | Episode: 912 | Score: -56.03 | Avg Score: -127.27 |\n",
"| Experiment: 2 | Episode: 913 | Score: -82.39 | Avg Score: -125.29 |\n",
"| Experiment: 2 | Episode: 914 | Score: -261.73 | Avg Score: -127.29 |\n",
"| Experiment: 2 | Episode: 915 | Score: -78.48 | Avg Score: -124.57 |\n",
"| Experiment: 2 | Episode: 916 | Score: -117.67 | Avg Score: -122.88 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 2 | Episode: 917 | Score: -101.58 | Avg Score: -123.66 |\n",
"| Experiment: 2 | Episode: 918 | Score: -189.09 | Avg Score: -122.21 |\n",
"| Experiment: 2 | Episode: 919 | Score: -126.53 | Avg Score: -122.36 |\n",
"| Experiment: 2 | Episode: 920 | Score: -135.49 | Avg Score: -121.99 |\n",
"| Experiment: 2 | Episode: 921 | Score: -45.08 | Avg Score: -121.82 |\n",
"| Experiment: 2 | Episode: 922 | Score: -50.87 | Avg Score: -121.71 |\n",
"| Experiment: 2 | Episode: 923 | Score: -117.31 | Avg Score: -121.44 |\n",
"| Experiment: 2 | Episode: 924 | Score: -85.61 | Avg Score: -121.44 |\n",
"| Experiment: 2 | Episode: 925 | Score: -97.3 | Avg Score: -121.74 |\n",
"| Experiment: 2 | Episode: 926 | Score: -82.71 | Avg Score: -120.2 |\n",
"| Experiment: 2 | Episode: 927 | Score: -90.53 | Avg Score: -120.5 |\n",
"| Experiment: 2 | Episode: 928 | Score: -41.9 | Avg Score: -120.46 |\n",
"| Experiment: 2 | Episode: 929 | Score: -112.7 | Avg Score: -120.97 |\n",
"| Experiment: 2 | Episode: 930 | Score: -87.93 | Avg Score: -120.43 |\n",
"| Experiment: 2 | Episode: 931 | Score: -79.95 | Avg Score: -121.19 |\n",
"| Experiment: 2 | Episode: 932 | Score: -181.02 | Avg Score: -122.12 |\n",
"| Experiment: 2 | Episode: 933 | Score: -88.84 | Avg Score: -122.2 |\n",
"| Experiment: 2 | Episode: 934 | Score: -39.7 | Avg Score: -121.51 |\n",
"| Experiment: 2 | Episode: 935 | Score: -133.77 | Avg Score: -121.77 |\n",
"| Experiment: 2 | Episode: 936 | Score: -133.4 | Avg Score: -121.58 |\n",
"| Experiment: 2 | Episode: 937 | Score: -136.55 | Avg Score: -121.88 |\n",
"| Experiment: 2 | Episode: 938 | Score: -83.15 | Avg Score: -119.78 |\n",
"| Experiment: 2 | Episode: 939 | Score: -233.01 | Avg Score: -121.39 |\n",
"| Experiment: 2 | Episode: 940 | Score: -118.18 | Avg Score: -122.03 |\n",
"| Experiment: 2 | Episode: 941 | Score: -148.77 | Avg Score: -122.37 |\n",
"| Experiment: 2 | Episode: 942 | Score: -127.82 | Avg Score: -122.71 |\n",
"| Experiment: 2 | Episode: 943 | Score: -80.7 | Avg Score: -122.96 |\n",
"| Experiment: 2 | Episode: 944 | Score: -81.97 | Avg Score: -123.13 |\n",
"| Experiment: 2 | Episode: 945 | Score: -112.92 | Avg Score: -122.79 |\n",
"| Experiment: 2 | Episode: 946 | Score: -153.07 | Avg Score: -122.88 |\n",
"| Experiment: 2 | Episode: 947 | Score: -139.05 | Avg Score: -123.28 |\n",
"| Experiment: 2 | Episode: 948 | Score: -94.05 | Avg Score: -123.66 |\n",
"| Experiment: 2 | Episode: 949 | Score: -97.35 | Avg Score: -123.71 |\n",
"| Experiment: 2 | Episode: 950 | Score: -118.08 | Avg Score: -123.46 |\n",
"| Experiment: 2 | Episode: 951 | Score: -100.6 | Avg Score: -123.7 |\n",
"| Experiment: 2 | Episode: 952 | Score: -26.11 | Avg Score: -122.3 |\n",
"| Experiment: 2 | Episode: 953 | Score: -107.54 | Avg Score: -122.76 |\n",
"| Experiment: 2 | Episode: 954 | Score: 14.16 | Avg Score: -122.12 |\n",
"| Experiment: 2 | Episode: 955 | Score: 0.47 | Avg Score: -119.93 |\n",
"| Experiment: 2 | Episode: 956 | Score: 24.36 | Avg Score: -118.6 |\n",
"| Experiment: 2 | Episode: 957 | Score: -384.23 | Avg Score: -119.91 |\n",
"| Experiment: 2 | Episode: 958 | Score: -433.55 | Avg Score: -120.99 |\n",
"| Experiment: 2 | Episode: 959 | Score: -73.15 | Avg Score: -121.69 |\n",
"| Experiment: 2 | Episode: 960 | Score: -79.91 | Avg Score: -119.91 |\n",
"| Experiment: 2 | Episode: 961 | Score: -307.05 | Avg Score: -122.49 |\n",
"| Experiment: 2 | Episode: 962 | Score: -52.62 | Avg Score: -119.92 |\n",
"| Experiment: 2 | Episode: 963 | Score: -130.2 | Avg Score: -119.49 |\n",
"| Experiment: 2 | Episode: 964 | Score: -54.3 | Avg Score: -119.47 |\n",
"| Experiment: 2 | Episode: 965 | Score: -52.4 | Avg Score: -119.34 |\n",
"| Experiment: 2 | Episode: 966 | Score: -69.29 | Avg Score: -119.27 |\n",
"| Experiment: 2 | Episode: 967 | Score: -30.2 | Avg Score: -118.08 |\n",
"| Experiment: 2 | Episode: 968 | Score: -143.22 | Avg Score: -117.81 |\n",
"| Experiment: 2 | Episode: 969 | Score: -70.24 | Avg Score: -117.57 |\n",
"| Experiment: 2 | Episode: 970 | Score: -222.62 | Avg Score: -118.88 |\n",
"| Experiment: 2 | Episode: 971 | Score: -106.58 | Avg Score: -117.4 |\n",
"| Experiment: 2 | Episode: 972 | Score: -163.01 | Avg Score: -119.01 |\n",
"| Experiment: 2 | Episode: 973 | Score: -166.82 | Avg Score: -119.46 |\n",
"| Experiment: 2 | Episode: 974 | Score: -89.54 | Avg Score: -118.07 |\n",
"| Experiment: 2 | Episode: 975 | Score: -90.33 | Avg Score: -118.44 |\n",
"| Experiment: 2 | Episode: 976 | Score: -40.8 | Avg Score: -117.85 |\n",
"| Experiment: 2 | Episode: 977 | Score: -47.43 | Avg Score: -118.33 |\n",
"| Experiment: 2 | Episode: 978 | Score: -118.73 | Avg Score: -118.4 |\n",
"| Experiment: 2 | Episode: 979 | Score: -108.61 | Avg Score: -117.64 |\n",
"| Experiment: 2 | Episode: 980 | Score: -97.28 | Avg Score: -117.79 |\n",
"| Experiment: 2 | Episode: 981 | Score: -77.03 | Avg Score: -117.2 |\n",
"| Experiment: 2 | Episode: 982 | Score: -60.96 | Avg Score: -116.46 |\n",
"| Experiment: 2 | Episode: 983 | Score: -123.98 | Avg Score: -115.49 |\n",
"| Experiment: 2 | Episode: 984 | Score: -104.34 | Avg Score: -114.66 |\n",
"| Experiment: 2 | Episode: 985 | Score: -51.73 | Avg Score: -114.05 |\n",
"| Experiment: 2 | Episode: 986 | Score: -127.62 | Avg Score: -114.52 |\n",
"| Experiment: 2 | Episode: 987 | Score: -46.17 | Avg Score: -114.33 |\n",
"| Experiment: 2 | Episode: 988 | Score: -103.55 | Avg Score: -114.12 |\n",
"| Experiment: 2 | Episode: 989 | Score: -268.72 | Avg Score: -115.24 |\n",
"| Experiment: 2 | Episode: 990 | Score: -261.15 | Avg Score: -117.5 |\n",
"| Experiment: 2 | Episode: 991 | Score: -74.54 | Avg Score: -116.61 |\n",
"| Experiment: 2 | Episode: 992 | Score: -81.87 | Avg Score: -116.28 |\n",
"| Experiment: 2 | Episode: 993 | Score: -91.27 | Avg Score: -115.15 |\n",
"| Experiment: 2 | Episode: 994 | Score: -71.44 | Avg Score: -113.49 |\n",
"| Experiment: 2 | Episode: 995 | Score: -83.39 | Avg Score: -113.12 |\n",
"| Experiment: 2 | Episode: 996 | Score: -141.91 | Avg Score: -113.68 |\n",
"| Experiment: 2 | Episode: 997 | Score: -137.68 | Avg Score: -112.7 |\n",
"| Experiment: 2 | Episode: 998 | Score: -56.44 | Avg Score: -112.6 |\n",
"| Experiment: 2 | Episode: 999 | Score: -116.6 | Avg Score: -112.13 |\n",
"| Experiment: 2 | Episode: 1000 | Score: -88.48 | Avg Score: -111.57 |\n",
"| Experiment: 2 | Episode: 1001 | Score: -19.57 | Avg Score: -109.81 |\n",
"| Experiment: 2 | Episode: 1002 | Score: -54.77 | Avg Score: -109.65 |\n",
"| Experiment: 2 | Episode: 1003 | Score: -140.85 | Avg Score: -109.27 |\n",
"| Experiment: 2 | Episode: 1004 | Score: -282.36 | Avg Score: -111.15 |\n",
"| Experiment: 2 | Episode: 1005 | Score: -110.75 | Avg Score: -111.23 |\n",
"| Experiment: 2 | Episode: 1006 | Score: -60.51 | Avg Score: -111.33 |\n",
"| Experiment: 2 | Episode: 1007 | Score: -78.03 | Avg Score: -111.21 |\n",
"| Experiment: 2 | Episode: 1008 | Score: -110.97 | Avg Score: -111.5 |\n",
"| Experiment: 2 | Episode: 1009 | Score: -87.08 | Avg Score: -110.52 |\n",
"| Experiment: 2 | Episode: 1010 | Score: -170.23 | Avg Score: -111.46 |\n",
"| Experiment: 2 | Episode: 1011 | Score: -234.34 | Avg Score: -111.88 |\n",
"| Experiment: 2 | Episode: 1012 | Score: -58.23 | Avg Score: -111.9 |\n",
"| Experiment: 2 | Episode: 1013 | Score: -65.04 | Avg Score: -111.73 |\n",
"| Experiment: 2 | Episode: 1014 | Score: -51.03 | Avg Score: -109.62 |\n",
"| Experiment: 2 | Episode: 1015 | Score: -110.61 | Avg Score: -109.94 |\n",
"| Experiment: 2 | Episode: 1016 | Score: -162.21 | Avg Score: -110.39 |\n",
"| Experiment: 2 | Episode: 1017 | Score: -140.03 | Avg Score: -110.77 |\n",
"| Experiment: 2 | Episode: 1018 | Score: -308.94 | Avg Score: -111.97 |\n",
"| Experiment: 2 | Episode: 1019 | Score: -201.96 | Avg Score: -112.73 |\n",
"| Experiment: 2 | Episode: 1020 | Score: -63.8 | Avg Score: -112.01 |\n",
"| Experiment: 2 | Episode: 1021 | Score: -53.16 | Avg Score: -112.09 |\n",
"| Experiment: 2 | Episode: 1022 | Score: -137.81 | Avg Score: -112.96 |\n",
"| Experiment: 2 | Episode: 1023 | Score: -117.78 | Avg Score: -112.96 |\n",
"| Experiment: 2 | Episode: 1024 | Score: -92.07 | Avg Score: -113.03 |\n",
"| Experiment: 2 | Episode: 1025 | Score: -65.54 | Avg Score: -112.71 |\n",
"| Experiment: 2 | Episode: 1026 | Score: -160.51 | Avg Score: -113.49 |\n",
"| Experiment: 2 | Episode: 1027 | Score: -47.65 | Avg Score: -113.06 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 2 | Episode: 1028 | Score: -113.66 | Avg Score: -113.78 |\n",
"| Experiment: 2 | Episode: 1029 | Score: -116.52 | Avg Score: -113.82 |\n",
"| Experiment: 2 | Episode: 1030 | Score: -231.62 | Avg Score: -115.25 |\n",
"| Experiment: 2 | Episode: 1031 | Score: -87.04 | Avg Score: -115.32 |\n",
"| Experiment: 2 | Episode: 1032 | Score: -144.87 | Avg Score: -114.96 |\n",
"| Experiment: 2 | Episode: 1033 | Score: -143.2 | Avg Score: -115.51 |\n",
"| Experiment: 2 | Episode: 1034 | Score: -354.79 | Avg Score: -118.66 |\n",
"| Experiment: 2 | Episode: 1035 | Score: -176.85 | Avg Score: -119.09 |\n",
"| Experiment: 2 | Episode: 1036 | Score: -92.96 | Avg Score: -118.68 |\n",
"| Experiment: 2 | Episode: 1037 | Score: -82.06 | Avg Score: -118.14 |\n",
"| Experiment: 2 | Episode: 1038 | Score: -85.8 | Avg Score: -118.16 |\n",
"| Experiment: 2 | Episode: 1039 | Score: -61.33 | Avg Score: -116.45 |\n",
"| Experiment: 2 | Episode: 1040 | Score: -9.65 | Avg Score: -115.36 |\n",
"| Experiment: 2 | Episode: 1041 | Score: -228.56 | Avg Score: -116.16 |\n",
"| Experiment: 2 | Episode: 1042 | Score: -73.5 | Avg Score: -115.62 |\n",
"| Experiment: 2 | Episode: 1043 | Score: -199.85 | Avg Score: -116.81 |\n",
"| Experiment: 2 | Episode: 1044 | Score: -80.68 | Avg Score: -116.8 |\n",
"| Experiment: 2 | Episode: 1045 | Score: -243.63 | Avg Score: -118.1 |\n",
"| Experiment: 2 | Episode: 1046 | Score: -166.8 | Avg Score: -118.24 |\n",
"| Experiment: 2 | Episode: 1047 | Score: -73.84 | Avg Score: -117.59 |\n",
"| Experiment: 2 | Episode: 1048 | Score: -57.2 | Avg Score: -117.22 |\n",
"| Experiment: 2 | Episode: 1049 | Score: -21.15 | Avg Score: -116.46 |\n",
"| Experiment: 2 | Episode: 1050 | Score: -152.46 | Avg Score: -116.8 |\n",
"| Experiment: 2 | Episode: 1051 | Score: -107.68 | Avg Score: -116.87 |\n",
"| Experiment: 2 | Episode: 1052 | Score: -97.22 | Avg Score: -117.58 |\n",
"| Experiment: 2 | Episode: 1053 | Score: -22.07 | Avg Score: -116.73 |\n",
"| Experiment: 2 | Episode: 1054 | Score: -70.68 | Avg Score: -117.58 |\n",
"| Experiment: 2 | Episode: 1055 | Score: -155.3 | Avg Score: -119.13 |\n",
"| Experiment: 2 | Episode: 1056 | Score: -68.91 | Avg Score: -120.07 |\n",
"| Experiment: 2 | Episode: 1057 | Score: -45.65 | Avg Score: -116.68 |\n",
"| Experiment: 2 | Episode: 1058 | Score: -112.7 | Avg Score: -113.47 |\n",
"| Experiment: 2 | Episode: 1059 | Score: -53.44 | Avg Score: -113.28 |\n",
"| Experiment: 2 | Episode: 1060 | Score: -85.54 | Avg Score: -113.33 |\n",
"| Experiment: 2 | Episode: 1061 | Score: -86.4 | Avg Score: -111.13 |\n",
"| Experiment: 2 | Episode: 1062 | Score: -172.75 | Avg Score: -112.33 |\n",
"| Experiment: 2 | Episode: 1063 | Score: -430.36 | Avg Score: -115.33 |\n",
"| Experiment: 2 | Episode: 1064 | Score: -164.35 | Avg Score: -116.43 |\n",
"| Experiment: 2 | Episode: 1065 | Score: -106.43 | Avg Score: -116.97 |\n",
"| Experiment: 2 | Episode: 1066 | Score: -145.4 | Avg Score: -117.73 |\n",
"| Experiment: 2 | Episode: 1067 | Score: -92.38 | Avg Score: -118.35 |\n",
"| Experiment: 2 | Episode: 1068 | Score: -59.84 | Avg Score: -117.52 |\n",
"| Experiment: 2 | Episode: 1069 | Score: -72.7 | Avg Score: -117.54 |\n",
"| Experiment: 2 | Episode: 1070 | Score: -224.13 | Avg Score: -117.56 |\n",
"| Experiment: 2 | Episode: 1071 | Score: -151.11 | Avg Score: -118.0 |\n",
"| Experiment: 2 | Episode: 1072 | Score: -168.59 | Avg Score: -118.06 |\n",
"| Experiment: 2 | Episode: 1073 | Score: -46.46 | Avg Score: -116.86 |\n",
"| Experiment: 2 | Episode: 1074 | Score: -52.61 | Avg Score: -116.49 |\n",
"| Experiment: 2 | Episode: 1075 | Score: -107.61 | Avg Score: -116.66 |\n",
"| Experiment: 2 | Episode: 1076 | Score: -86.04 | Avg Score: -117.11 |\n",
"| Experiment: 2 | Episode: 1077 | Score: -17.24 | Avg Score: -116.81 |\n",
"| Experiment: 2 | Episode: 1078 | Score: -159.58 | Avg Score: -117.22 |\n",
"| Experiment: 2 | Episode: 1079 | Score: -225.03 | Avg Score: -118.38 |\n",
"| Experiment: 2 | Episode: 1080 | Score: -111.0 | Avg Score: -118.52 |\n",
"| Experiment: 2 | Episode: 1081 | Score: -91.45 | Avg Score: -118.66 |\n",
"| Experiment: 2 | Episode: 1082 | Score: -355.74 | Avg Score: -121.61 |\n",
"| Experiment: 2 | Episode: 1083 | Score: -172.48 | Avg Score: -122.1 |\n",
"| Experiment: 2 | Episode: 1084 | Score: -34.69 | Avg Score: -121.4 |\n",
"| Experiment: 2 | Episode: 1085 | Score: -66.79 | Avg Score: -121.55 |\n",
"| Experiment: 2 | Episode: 1086 | Score: -155.39 | Avg Score: -121.83 |\n",
"| Experiment: 2 | Episode: 1087 | Score: -127.21 | Avg Score: -122.64 |\n",
"| Experiment: 2 | Episode: 1088 | Score: -84.77 | Avg Score: -122.45 |\n",
"| Experiment: 2 | Episode: 1089 | Score: -67.98 | Avg Score: -120.44 |\n",
"| Experiment: 2 | Episode: 1090 | Score: -55.7 | Avg Score: -118.39 |\n",
"| Experiment: 2 | Episode: 1091 | Score: -142.7 | Avg Score: -119.07 |\n",
"| Experiment: 2 | Episode: 1092 | Score: -180.52 | Avg Score: -120.06 |\n",
"| Experiment: 2 | Episode: 1093 | Score: 78.99 | Avg Score: -118.35 |\n",
"| Experiment: 2 | Episode: 1094 | Score: -40.86 | Avg Score: -118.05 |\n",
"| Experiment: 2 | Episode: 1095 | Score: -25.69 | Avg Score: -117.47 |\n",
"| Experiment: 2 | Episode: 1096 | Score: -63.98 | Avg Score: -116.69 |\n",
"| Experiment: 2 | Episode: 1097 | Score: -157.46 | Avg Score: -116.89 |\n",
"| Experiment: 2 | Episode: 1098 | Score: -259.15 | Avg Score: -118.92 |\n",
"| Experiment: 2 | Episode: 1099 | Score: -33.49 | Avg Score: -118.09 |\n",
"| Experiment: 2 | Episode: 1100 | Score: -223.45 | Avg Score: -119.44 |\n",
"| Experiment: 2 | Episode: 1101 | Score: -63.41 | Avg Score: -119.87 |\n",
"| Experiment: 2 | Episode: 1102 | Score: -71.07 | Avg Score: -120.04 |\n",
"| Experiment: 2 | Episode: 1103 | Score: -181.32 | Avg Score: -120.44 |\n",
"| Experiment: 2 | Episode: 1104 | Score: -72.68 | Avg Score: -118.34 |\n",
"| Experiment: 2 | Episode: 1105 | Score: -117.43 | Avg Score: -118.41 |\n",
"| Experiment: 2 | Episode: 1106 | Score: -31.33 | Avg Score: -118.12 |\n",
"| Experiment: 2 | Episode: 1107 | Score: -174.93 | Avg Score: -119.09 |\n",
"| Experiment: 2 | Episode: 1108 | Score: -69.34 | Avg Score: -118.67 |\n",
"| Experiment: 2 | Episode: 1109 | Score: -220.4 | Avg Score: -120.01 |\n",
"| Experiment: 2 | Episode: 1110 | Score: -131.72 | Avg Score: -119.62 |\n",
"| Experiment: 2 | Episode: 1111 | Score: -476.17 | Avg Score: -122.04 |\n",
"| Experiment: 2 | Episode: 1112 | Score: -188.42 | Avg Score: -123.34 |\n",
"| Experiment: 2 | Episode: 1113 | Score: -99.95 | Avg Score: -123.69 |\n",
"| Experiment: 2 | Episode: 1114 | Score: 4.49 | Avg Score: -123.13 |\n",
"| Experiment: 2 | Episode: 1115 | Score: -88.02 | Avg Score: -122.91 |\n",
"| Experiment: 2 | Episode: 1116 | Score: -92.68 | Avg Score: -122.21 |\n",
"| Experiment: 2 | Episode: 1117 | Score: -210.36 | Avg Score: -122.92 |\n",
"| Experiment: 2 | Episode: 1118 | Score: -62.31 | Avg Score: -120.45 |\n",
"| Experiment: 2 | Episode: 1119 | Score: -331.99 | Avg Score: -121.75 |\n",
"| Experiment: 2 | Episode: 1120 | Score: -17.85 | Avg Score: -121.29 |\n",
"| Experiment: 2 | Episode: 1121 | Score: -209.88 | Avg Score: -122.86 |\n",
"| Experiment: 2 | Episode: 1122 | Score: -25.84 | Avg Score: -121.74 |\n",
"| Experiment: 2 | Episode: 1123 | Score: -95.74 | Avg Score: -121.52 |\n",
"| Experiment: 2 | Episode: 1124 | Score: -211.03 | Avg Score: -122.71 |\n",
"| Experiment: 2 | Episode: 1125 | Score: -86.51 | Avg Score: -122.92 |\n",
"| Experiment: 2 | Episode: 1126 | Score: -194.22 | Avg Score: -123.25 |\n",
"| Experiment: 2 | Episode: 1127 | Score: -72.65 | Avg Score: -123.5 |\n",
"| Experiment: 2 | Episode: 1128 | Score: -128.6 | Avg Score: -123.65 |\n",
"| Experiment: 2 | Episode: 1129 | Score: -130.07 | Avg Score: -123.79 |\n",
"| Experiment: 2 | Episode: 1130 | Score: -210.31 | Avg Score: -123.58 |\n",
"| Experiment: 2 | Episode: 1131 | Score: -62.35 | Avg Score: -123.33 |\n",
"| Experiment: 2 | Episode: 1132 | Score: -111.99 | Avg Score: -123.0 |\n",
"| Experiment: 2 | Episode: 1133 | Score: -188.43 | Avg Score: -123.45 |\n",
"| Experiment: 2 | Episode: 1134 | Score: -119.12 | Avg Score: -121.1 |\n",
"| Experiment: 2 | Episode: 1135 | Score: -243.68 | Avg Score: -121.76 |\n",
"| Experiment: 2 | Episode: 1136 | Score: -115.8 | Avg Score: -121.99 |\n",
"| Experiment: 2 | Episode: 1137 | Score: -117.39 | Avg Score: -122.35 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 2 | Episode: 1138 | Score: -221.13 | Avg Score: -123.7 |\n",
"| Experiment: 2 | Episode: 1139 | Score: -99.6 | Avg Score: -124.08 |\n",
"| Experiment: 2 | Episode: 1140 | Score: -218.77 | Avg Score: -126.17 |\n",
"| Experiment: 2 | Episode: 1141 | Score: -273.65 | Avg Score: -126.62 |\n",
"| Experiment: 2 | Episode: 1142 | Score: -62.37 | Avg Score: -126.51 |\n",
"| Experiment: 2 | Episode: 1143 | Score: -139.25 | Avg Score: -125.91 |\n",
"| Experiment: 2 | Episode: 1144 | Score: -96.44 | Avg Score: -126.06 |\n",
"| Experiment: 2 | Episode: 1145 | Score: -91.99 | Avg Score: -124.55 |\n",
"| Experiment: 2 | Episode: 1146 | Score: -229.96 | Avg Score: -125.18 |\n",
"| Experiment: 2 | Episode: 1147 | Score: -88.35 | Avg Score: -125.33 |\n",
"| Experiment: 2 | Episode: 1148 | Score: -30.91 | Avg Score: -125.06 |\n",
"| Experiment: 2 | Episode: 1149 | Score: -44.92 | Avg Score: -125.3 |\n",
"| Experiment: 2 | Episode: 1150 | Score: -133.29 | Avg Score: -125.11 |\n",
"| Experiment: 2 | Episode: 1151 | Score: -139.68 | Avg Score: -125.43 |\n",
"| Experiment: 2 | Episode: 1152 | Score: -115.29 | Avg Score: -125.61 |\n",
"| Experiment: 2 | Episode: 1153 | Score: -33.17 | Avg Score: -125.72 |\n",
"| Experiment: 2 | Episode: 1154 | Score: -126.85 | Avg Score: -126.28 |\n",
"| Experiment: 2 | Episode: 1155 | Score: -58.14 | Avg Score: -125.31 |\n",
"| Experiment: 2 | Episode: 1156 | Score: -108.27 | Avg Score: -125.7 |\n",
"| Experiment: 2 | Episode: 1157 | Score: -102.15 | Avg Score: -126.27 |\n",
"| Experiment: 2 | Episode: 1158 | Score: -142.35 | Avg Score: -126.57 |\n",
"| Experiment: 2 | Episode: 1159 | Score: -61.61 | Avg Score: -126.65 |\n",
"| Experiment: 2 | Episode: 1160 | Score: -410.61 | Avg Score: -129.9 |\n",
"| Experiment: 2 | Episode: 1161 | Score: -84.73 | Avg Score: -129.88 |\n",
"| Experiment: 2 | Episode: 1162 | Score: -108.63 | Avg Score: -129.24 |\n",
"| Experiment: 2 | Episode: 1163 | Score: -64.14 | Avg Score: -125.58 |\n",
"| Experiment: 2 | Episode: 1164 | Score: 1.33 | Avg Score: -123.92 |\n",
"| Experiment: 2 | Episode: 1165 | Score: -13.46 | Avg Score: -122.99 |\n",
"| Experiment: 2 | Episode: 1166 | Score: -140.42 | Avg Score: -122.94 |\n",
"| Experiment: 2 | Episode: 1167 | Score: -181.85 | Avg Score: -123.84 |\n",
"| Experiment: 2 | Episode: 1168 | Score: -34.52 | Avg Score: -123.58 |\n",
"| Experiment: 2 | Episode: 1169 | Score: -33.96 | Avg Score: -123.2 |\n",
"| Experiment: 2 | Episode: 1170 | Score: 19.8 | Avg Score: -120.76 |\n",
"| Experiment: 2 | Episode: 1171 | Score: -40.57 | Avg Score: -119.65 |\n",
"| Experiment: 2 | Episode: 1172 | Score: -113.08 | Avg Score: -119.1 |\n",
"| Experiment: 2 | Episode: 1173 | Score: -356.14 | Avg Score: -122.19 |\n",
"| Experiment: 2 | Episode: 1174 | Score: -51.99 | Avg Score: -122.19 |\n",
"| Experiment: 2 | Episode: 1175 | Score: -96.5 | Avg Score: -122.08 |\n",
"| Experiment: 2 | Episode: 1176 | Score: -70.58 | Avg Score: -121.92 |\n",
"| Experiment: 2 | Episode: 1177 | Score: 64.33 | Avg Score: -121.1 |\n",
"| Experiment: 2 | Episode: 1178 | Score: -147.89 | Avg Score: -120.99 |\n",
"| Experiment: 2 | Episode: 1179 | Score: -2.19 | Avg Score: -118.76 |\n",
"| Experiment: 2 | Episode: 1180 | Score: -189.88 | Avg Score: -119.55 |\n",
"| Experiment: 2 | Episode: 1181 | Score: 26.93 | Avg Score: -118.36 |\n",
"| Experiment: 2 | Episode: 1182 | Score: -51.87 | Avg Score: -115.33 |\n",
"| Experiment: 2 | Episode: 1183 | Score: -115.33 | Avg Score: -114.75 |\n",
"| Experiment: 2 | Episode: 1184 | Score: -153.09 | Avg Score: -115.94 |\n",
"| Experiment: 2 | Episode: 1185 | Score: -134.1 | Avg Score: -116.61 |\n",
"| Experiment: 2 | Episode: 1186 | Score: -176.82 | Avg Score: -116.83 |\n",
"| Experiment: 2 | Episode: 1187 | Score: -179.63 | Avg Score: -117.35 |\n",
"| Experiment: 2 | Episode: 1188 | Score: -284.74 | Avg Score: -119.35 |\n",
"| Experiment: 2 | Episode: 1189 | Score: -111.66 | Avg Score: -119.79 |\n",
"| Experiment: 2 | Episode: 1190 | Score: -93.32 | Avg Score: -120.16 |\n",
"| Experiment: 2 | Episode: 1191 | Score: -240.19 | Avg Score: -121.14 |\n",
"| Experiment: 2 | Episode: 1192 | Score: -93.56 | Avg Score: -120.27 |\n",
"| Experiment: 2 | Episode: 1193 | Score: -282.47 | Avg Score: -123.88 |\n",
"| Experiment: 2 | Episode: 1194 | Score: -72.83 | Avg Score: -124.2 |\n",
"| Experiment: 2 | Episode: 1195 | Score: -237.42 | Avg Score: -126.32 |\n",
"| Experiment: 2 | Episode: 1196 | Score: 42.98 | Avg Score: -125.25 |\n",
"| Experiment: 2 | Episode: 1197 | Score: -86.55 | Avg Score: -124.54 |\n",
"| Experiment: 2 | Episode: 1198 | Score: -80.54 | Avg Score: -122.75 |\n",
"| Experiment: 2 | Episode: 1199 | Score: -99.67 | Avg Score: -123.42 |\n",
"| Experiment: 2 | Episode: 1200 | Score: -80.23 | Avg Score: -121.98 |\n",
"| Experiment: 2 | Episode: 1201 | Score: -62.0 | Avg Score: -121.97 |\n",
"| Experiment: 2 | Episode: 1202 | Score: -178.71 | Avg Score: -123.05 |\n",
"| Experiment: 2 | Episode: 1203 | Score: -63.58 | Avg Score: -121.87 |\n",
"| Experiment: 2 | Episode: 1204 | Score: -74.3 | Avg Score: -121.89 |\n",
"| Experiment: 2 | Episode: 1205 | Score: -62.65 | Avg Score: -121.34 |\n",
"| Experiment: 2 | Episode: 1206 | Score: -269.09 | Avg Score: -123.72 |\n",
"| Experiment: 2 | Episode: 1207 | Score: -257.3 | Avg Score: -124.54 |\n",
"| Experiment: 2 | Episode: 1208 | Score: -72.46 | Avg Score: -124.57 |\n",
"| Experiment: 2 | Episode: 1209 | Score: 8.55 | Avg Score: -122.28 |\n",
"| Experiment: 2 | Episode: 1210 | Score: -136.83 | Avg Score: -122.33 |\n",
"| Experiment: 2 | Episode: 1211 | Score: -122.26 | Avg Score: -118.79 |\n",
"| Experiment: 2 | Episode: 1212 | Score: -29.16 | Avg Score: -117.2 |\n",
"| Experiment: 2 | Episode: 1213 | Score: -61.91 | Avg Score: -116.82 |\n",
"| Experiment: 2 | Episode: 1214 | Score: -77.04 | Avg Score: -117.63 |\n",
"| Experiment: 2 | Episode: 1215 | Score: -84.04 | Avg Score: -117.59 |\n",
"| Experiment: 2 | Episode: 1216 | Score: -79.2 | Avg Score: -117.46 |\n",
"| Experiment: 2 | Episode: 1217 | Score: -52.01 | Avg Score: -115.88 |\n",
"| Experiment: 2 | Episode: 1218 | Score: -54.26 | Avg Score: -115.8 |\n",
"| Experiment: 2 | Episode: 1219 | Score: -304.64 | Avg Score: -115.52 |\n",
"| Experiment: 2 | Episode: 1220 | Score: -46.0 | Avg Score: -115.8 |\n",
"| Experiment: 2 | Episode: 1221 | Score: -70.33 | Avg Score: -114.41 |\n",
"| Experiment: 2 | Episode: 1222 | Score: -35.54 | Avg Score: -114.51 |\n",
"| Experiment: 2 | Episode: 1223 | Score: -69.71 | Avg Score: -114.25 |\n",
"| Experiment: 2 | Episode: 1224 | Score: -170.38 | Avg Score: -113.84 |\n",
"| Experiment: 2 | Episode: 1225 | Score: -108.75 | Avg Score: -114.06 |\n",
"| Experiment: 2 | Episode: 1226 | Score: -112.19 | Avg Score: -113.24 |\n",
"| Experiment: 2 | Episode: 1227 | Score: -156.01 | Avg Score: -114.07 |\n",
"| Experiment: 2 | Episode: 1228 | Score: -67.34 | Avg Score: -113.46 |\n",
"| Experiment: 2 | Episode: 1229 | Score: -43.76 | Avg Score: -112.6 |\n",
"| Experiment: 2 | Episode: 1230 | Score: -76.19 | Avg Score: -111.26 |\n",
"| Experiment: 2 | Episode: 1231 | Score: -115.25 | Avg Score: -111.79 |\n",
"| Experiment: 2 | Episode: 1232 | Score: -68.91 | Avg Score: -111.36 |\n",
"| Experiment: 2 | Episode: 1233 | Score: -86.9 | Avg Score: -110.34 |\n",
"| Experiment: 2 | Episode: 1234 | Score: -42.55 | Avg Score: -109.58 |\n",
"| Experiment: 2 | Episode: 1235 | Score: -179.06 | Avg Score: -108.93 |\n",
"| Experiment: 2 | Episode: 1236 | Score: -147.65 | Avg Score: -109.25 |\n",
"| Experiment: 2 | Episode: 1237 | Score: -116.65 | Avg Score: -109.24 |\n",
"| Experiment: 2 | Episode: 1238 | Score: -188.35 | Avg Score: -108.91 |\n",
"| Experiment: 2 | Episode: 1239 | Score: -57.7 | Avg Score: -108.49 |\n",
"| Experiment: 2 | Episode: 1240 | Score: -75.93 | Avg Score: -107.06 |\n",
"| Experiment: 2 | Episode: 1241 | Score: -62.29 | Avg Score: -104.95 |\n",
"| Experiment: 2 | Episode: 1242 | Score: -24.76 | Avg Score: -104.57 |\n",
"| Experiment: 2 | Episode: 1243 | Score: -59.9 | Avg Score: -103.78 |\n",
"| Experiment: 2 | Episode: 1244 | Score: 25.4 | Avg Score: -102.56 |\n",
"| Experiment: 2 | Episode: 1245 | Score: -118.58 | Avg Score: -102.83 |\n",
"| Experiment: 2 | Episode: 1246 | Score: -93.71 | Avg Score: -101.47 |\n",
"| Experiment: 2 | Episode: 1247 | Score: -36.46 | Avg Score: -100.95 |\n",
"| Experiment: 2 | Episode: 1248 | Score: -12.31 | Avg Score: -100.76 |\n",
"| Experiment: 2 | Episode: 1249 | Score: -5.24 | Avg Score: -100.36 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 2 | Episode: 1250 | Score: -108.11 | Avg Score: -100.11 |\n",
"| Experiment: 2 | Episode: 1251 | Score: -490.51 | Avg Score: -103.62 |\n",
"| Experiment: 2 | Episode: 1252 | Score: -343.95 | Avg Score: -105.91 |\n",
"| Experiment: 2 | Episode: 1253 | Score: -105.41 | Avg Score: -106.63 |\n",
"| Experiment: 2 | Episode: 1254 | Score: -100.29 | Avg Score: -106.36 |\n",
"| Experiment: 2 | Episode: 1255 | Score: -32.76 | Avg Score: -106.11 |\n",
"| Experiment: 2 | Episode: 1256 | Score: -288.53 | Avg Score: -107.91 |\n",
"| Experiment: 2 | Episode: 1257 | Score: -10.35 | Avg Score: -107.0 |\n",
"| Experiment: 2 | Episode: 1258 | Score: -43.61 | Avg Score: -106.01 |\n",
"| Experiment: 2 | Episode: 1259 | Score: -199.18 | Avg Score: -107.38 |\n",
"| Experiment: 2 | Episode: 1260 | Score: -103.79 | Avg Score: -104.32 |\n",
"| Experiment: 2 | Episode: 1261 | Score: -86.03 | Avg Score: -104.33 |\n",
"| Experiment: 2 | Episode: 1262 | Score: -81.05 | Avg Score: -104.05 |\n",
"| Experiment: 2 | Episode: 1263 | Score: -45.92 | Avg Score: -103.87 |\n",
"| Experiment: 2 | Episode: 1264 | Score: -37.57 | Avg Score: -104.26 |\n",
"| Experiment: 2 | Episode: 1265 | Score: -5.27 | Avg Score: -104.18 |\n",
"| Experiment: 2 | Episode: 1266 | Score: -218.16 | Avg Score: -104.96 |\n",
"| Experiment: 2 | Episode: 1267 | Score: -7.2 | Avg Score: -103.21 |\n",
"| Experiment: 2 | Episode: 1268 | Score: -91.39 | Avg Score: -103.78 |\n",
"| Experiment: 2 | Episode: 1269 | Score: -74.64 | Avg Score: -104.18 |\n",
"| Experiment: 2 | Episode: 1270 | Score: -8.19 | Avg Score: -104.46 |\n",
"| Experiment: 2 | Episode: 1271 | Score: -65.46 | Avg Score: -104.71 |\n",
"| Experiment: 2 | Episode: 1272 | Score: -125.08 | Avg Score: -104.83 |\n",
"| Experiment: 2 | Episode: 1273 | Score: -155.33 | Avg Score: -102.82 |\n",
"| Experiment: 2 | Episode: 1274 | Score: -57.26 | Avg Score: -102.88 |\n",
"| Experiment: 2 | Episode: 1275 | Score: -54.45 | Avg Score: -102.46 |\n",
"| Experiment: 2 | Episode: 1276 | Score: -135.49 | Avg Score: -103.11 |\n",
"| Experiment: 2 | Episode: 1277 | Score: -77.56 | Avg Score: -104.53 |\n",
"| Experiment: 2 | Episode: 1278 | Score: -103.28 | Avg Score: -104.08 |\n",
"| Experiment: 2 | Episode: 1279 | Score: -208.82 | Avg Score: -106.15 |\n",
"| Experiment: 2 | Episode: 1280 | Score: -107.51 | Avg Score: -105.32 |\n",
"| Experiment: 2 | Episode: 1281 | Score: -32.33 | Avg Score: -105.91 |\n",
"| Experiment: 2 | Episode: 1282 | Score: -55.0 | Avg Score: -105.95 |\n",
"| Experiment: 2 | Episode: 1283 | Score: -126.41 | Avg Score: -106.06 |\n",
"| Experiment: 2 | Episode: 1284 | Score: -305.2 | Avg Score: -107.58 |\n",
"| Experiment: 2 | Episode: 1285 | Score: -105.43 | Avg Score: -107.29 |\n",
"| Experiment: 2 | Episode: 1286 | Score: -118.06 | Avg Score: -106.7 |\n",
"| Experiment: 2 | Episode: 1287 | Score: -83.6 | Avg Score: -105.74 |\n",
"| Experiment: 2 | Episode: 1288 | Score: -80.45 | Avg Score: -103.7 |\n",
"| Experiment: 2 | Episode: 1289 | Score: -134.29 | Avg Score: -103.93 |\n",
"| Experiment: 2 | Episode: 1290 | Score: -114.37 | Avg Score: -104.14 |\n",
"| Experiment: 2 | Episode: 1291 | Score: -94.14 | Avg Score: -102.68 |\n",
"| Experiment: 2 | Episode: 1292 | Score: -75.7 | Avg Score: -102.5 |\n",
"| Experiment: 2 | Episode: 1293 | Score: -92.37 | Avg Score: -100.6 |\n",
"| Experiment: 2 | Episode: 1294 | Score: -46.71 | Avg Score: -100.34 |\n",
"| Experiment: 2 | Episode: 1295 | Score: -45.31 | Avg Score: -98.41 |\n",
"| Experiment: 2 | Episode: 1296 | Score: -64.66 | Avg Score: -99.49 |\n",
"| Experiment: 2 | Episode: 1297 | Score: -46.42 | Avg Score: -99.09 |\n",
"| Experiment: 2 | Episode: 1298 | Score: -324.94 | Avg Score: -101.53 |\n",
"| Experiment: 2 | Episode: 1299 | Score: -122.65 | Avg Score: -101.76 |\n",
"| Experiment: 2 | Episode: 1300 | Score: -107.42 | Avg Score: -102.04 |\n",
"| Experiment: 2 | Episode: 1301 | Score: -57.61 | Avg Score: -101.99 |\n",
"| Experiment: 2 | Episode: 1302 | Score: -18.87 | Avg Score: -100.39 |\n",
"| Experiment: 2 | Episode: 1303 | Score: -95.62 | Avg Score: -100.71 |\n",
"| Experiment: 2 | Episode: 1304 | Score: -52.53 | Avg Score: -100.5 |\n",
"| Experiment: 2 | Episode: 1305 | Score: -193.62 | Avg Score: -101.81 |\n",
"| Experiment: 2 | Episode: 1306 | Score: -165.07 | Avg Score: -100.76 |\n",
"| Experiment: 2 | Episode: 1307 | Score: -63.52 | Avg Score: -98.83 |\n",
"| Experiment: 2 | Episode: 1308 | Score: -97.5 | Avg Score: -99.08 |\n",
"| Experiment: 2 | Episode: 1309 | Score: -88.18 | Avg Score: -100.04 |\n",
"| Experiment: 2 | Episode: 1310 | Score: -103.06 | Avg Score: -99.71 |\n",
"| Experiment: 2 | Episode: 1311 | Score: -46.82 | Avg Score: -98.95 |\n",
"| Experiment: 2 | Episode: 1312 | Score: -86.6 | Avg Score: -99.53 |\n",
"| Experiment: 2 | Episode: 1313 | Score: -105.77 | Avg Score: -99.97 |\n",
"| Experiment: 2 | Episode: 1314 | Score: -74.05 | Avg Score: -99.94 |\n",
"| Experiment: 2 | Episode: 1315 | Score: -51.16 | Avg Score: -99.61 |\n",
"| Experiment: 2 | Episode: 1316 | Score: -167.09 | Avg Score: -100.49 |\n",
"| Experiment: 2 | Episode: 1317 | Score: -33.97 | Avg Score: -100.31 |\n",
"| Experiment: 2 | Episode: 1318 | Score: -123.26 | Avg Score: -101.0 |\n",
"| Experiment: 2 | Episode: 1319 | Score: 24.85 | Avg Score: -97.7 |\n",
"| Experiment: 2 | Episode: 1320 | Score: -99.44 | Avg Score: -98.24 |\n",
"| Experiment: 2 | Episode: 1321 | Score: -43.84 | Avg Score: -97.97 |\n",
"| Experiment: 2 | Episode: 1322 | Score: -170.76 | Avg Score: -99.32 |\n",
"| Experiment: 2 | Episode: 1323 | Score: -77.19 | Avg Score: -99.4 |\n",
"| Experiment: 2 | Episode: 1324 | Score: -69.37 | Avg Score: -98.39 |\n",
"| Experiment: 2 | Episode: 1325 | Score: -62.87 | Avg Score: -97.93 |\n",
"| Experiment: 2 | Episode: 1326 | Score: -123.91 | Avg Score: -98.05 |\n",
"| Experiment: 2 | Episode: 1327 | Score: -229.79 | Avg Score: -98.78 |\n",
"| Experiment: 2 | Episode: 1328 | Score: -9.15 | Avg Score: -98.2 |\n",
"| Experiment: 2 | Episode: 1329 | Score: -112.59 | Avg Score: -98.89 |\n",
"| Experiment: 2 | Episode: 1330 | Score: 67.47 | Avg Score: -97.45 |\n",
"| Experiment: 2 | Episode: 1331 | Score: -91.49 | Avg Score: -97.22 |\n",
"| Experiment: 2 | Episode: 1332 | Score: -112.59 | Avg Score: -97.65 |\n",
"| Experiment: 2 | Episode: 1333 | Score: -158.97 | Avg Score: -98.37 |\n",
"| Experiment: 2 | Episode: 1334 | Score: -83.96 | Avg Score: -98.79 |\n",
"| Experiment: 2 | Episode: 1335 | Score: -50.13 | Avg Score: -97.5 |\n",
"| Experiment: 2 | Episode: 1336 | Score: -51.82 | Avg Score: -96.54 |\n",
"| Experiment: 2 | Episode: 1337 | Score: -144.66 | Avg Score: -96.82 |\n",
"| Experiment: 2 | Episode: 1338 | Score: -153.46 | Avg Score: -96.47 |\n",
"| Experiment: 2 | Episode: 1339 | Score: -185.95 | Avg Score: -97.75 |\n",
"| Experiment: 2 | Episode: 1340 | Score: -59.35 | Avg Score: -97.59 |\n",
"| Experiment: 2 | Episode: 1341 | Score: -52.61 | Avg Score: -97.49 |\n",
"| Experiment: 2 | Episode: 1342 | Score: -87.14 | Avg Score: -98.11 |\n",
"| Experiment: 2 | Episode: 1343 | Score: -288.4 | Avg Score: -100.4 |\n",
"| Experiment: 2 | Episode: 1344 | Score: -141.04 | Avg Score: -102.06 |\n",
"| Experiment: 2 | Episode: 1345 | Score: -8.34 | Avg Score: -100.96 |\n",
"| Experiment: 2 | Episode: 1346 | Score: -120.05 | Avg Score: -101.22 |\n",
"| Experiment: 2 | Episode: 1347 | Score: -95.48 | Avg Score: -101.81 |\n",
"| Experiment: 2 | Episode: 1348 | Score: -55.75 | Avg Score: -102.25 |\n",
"| Experiment: 2 | Episode: 1349 | Score: -25.4 | Avg Score: -102.45 |\n",
"| Experiment: 2 | Episode: 1350 | Score: -144.64 | Avg Score: -102.82 |\n",
"| Experiment: 2 | Episode: 1351 | Score: -82.79 | Avg Score: -98.74 |\n",
"| Experiment: 2 | Episode: 1352 | Score: -71.92 | Avg Score: -96.02 |\n",
"| Experiment: 2 | Episode: 1353 | Score: -174.95 | Avg Score: -96.71 |\n",
"| Experiment: 2 | Episode: 1354 | Score: -126.54 | Avg Score: -96.98 |\n",
"| Experiment: 2 | Episode: 1355 | Score: -73.65 | Avg Score: -97.39 |\n",
"| Experiment: 2 | Episode: 1356 | Score: -79.68 | Avg Score: -95.3 |\n",
"| Experiment: 2 | Episode: 1357 | Score: -84.52 | Avg Score: -96.04 |\n",
"| Experiment: 2 | Episode: 1358 | Score: -74.48 | Avg Score: -96.35 |\n",
"| Experiment: 2 | Episode: 1359 | Score: -58.48 | Avg Score: -94.94 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 2 | Episode: 1360 | Score: -106.48 | Avg Score: -94.97 |\n",
"| Experiment: 2 | Episode: 1361 | Score: -95.65 | Avg Score: -95.06 |\n",
"| Experiment: 2 | Episode: 1362 | Score: -37.23 | Avg Score: -94.63 |\n",
"| Experiment: 2 | Episode: 1363 | Score: -42.28 | Avg Score: -94.59 |\n",
"| Experiment: 2 | Episode: 1364 | Score: -22.36 | Avg Score: -94.44 |\n",
"| Experiment: 2 | Episode: 1365 | Score: -91.72 | Avg Score: -95.3 |\n",
"| Experiment: 2 | Episode: 1366 | Score: -147.2 | Avg Score: -94.59 |\n",
"| Experiment: 2 | Episode: 1367 | Score: -232.2 | Avg Score: -96.84 |\n",
"| Experiment: 2 | Episode: 1368 | Score: -68.15 | Avg Score: -96.61 |\n",
"| Experiment: 2 | Episode: 1369 | Score: -50.3 | Avg Score: -96.37 |\n",
"| Experiment: 2 | Episode: 1370 | Score: -106.09 | Avg Score: -97.34 |\n",
"| Experiment: 2 | Episode: 1371 | Score: -66.89 | Avg Score: -97.36 |\n",
"| Experiment: 2 | Episode: 1372 | Score: -46.85 | Avg Score: -96.58 |\n",
"| Experiment: 2 | Episode: 1373 | Score: -55.91 | Avg Score: -95.58 |\n",
"| Experiment: 2 | Episode: 1374 | Score: -162.21 | Avg Score: -96.63 |\n",
"| Experiment: 2 | Episode: 1375 | Score: -105.26 | Avg Score: -97.14 |\n",
"| Experiment: 2 | Episode: 1376 | Score: -84.13 | Avg Score: -96.63 |\n",
"| Experiment: 2 | Episode: 1377 | Score: -20.53 | Avg Score: -96.06 |\n",
"| Experiment: 2 | Episode: 1378 | Score: -12.99 | Avg Score: -95.15 |\n",
"| Experiment: 2 | Episode: 1379 | Score: -68.59 | Avg Score: -93.75 |\n",
"| Experiment: 2 | Episode: 1380 | Score: -104.03 | Avg Score: -93.72 |\n",
"| Experiment: 2 | Episode: 1381 | Score: -6.08 | Avg Score: -93.45 |\n",
"| Experiment: 2 | Episode: 1382 | Score: -309.8 | Avg Score: -96.0 |\n",
"| Experiment: 2 | Episode: 1383 | Score: -88.96 | Avg Score: -95.63 |\n",
"| Experiment: 2 | Episode: 1384 | Score: -73.11 | Avg Score: -93.31 |\n",
"| Experiment: 2 | Episode: 1385 | Score: -230.75 | Avg Score: -94.56 |\n",
"| Experiment: 2 | Episode: 1386 | Score: -73.43 | Avg Score: -94.11 |\n",
"| Experiment: 2 | Episode: 1387 | Score: -60.99 | Avg Score: -93.89 |\n",
"| Experiment: 2 | Episode: 1388 | Score: -133.11 | Avg Score: -94.41 |\n",
"| Experiment: 2 | Episode: 1389 | Score: -65.88 | Avg Score: -93.73 |\n",
"| Experiment: 2 | Episode: 1390 | Score: -61.69 | Avg Score: -93.2 |\n",
"| Experiment: 2 | Episode: 1391 | Score: -86.47 | Avg Score: -93.13 |\n",
"| Experiment: 2 | Episode: 1392 | Score: -109.05 | Avg Score: -93.46 |\n",
"| Experiment: 2 | Episode: 1393 | Score: -57.16 | Avg Score: -93.11 |\n",
"| Experiment: 2 | Episode: 1394 | Score: -48.53 | Avg Score: -93.13 |\n",
"| Experiment: 2 | Episode: 1395 | Score: -40.84 | Avg Score: -93.08 |\n",
"| Experiment: 2 | Episode: 1396 | Score: -102.88 | Avg Score: -93.46 |\n",
"| Experiment: 2 | Episode: 1397 | Score: -103.83 | Avg Score: -94.04 |\n",
"| Experiment: 2 | Episode: 1398 | Score: 3.8 | Avg Score: -90.75 |\n",
"| Experiment: 2 | Episode: 1399 | Score: -133.49 | Avg Score: -90.86 |\n",
"| Experiment: 2 | Episode: 1400 | Score: -102.77 | Avg Score: -90.81 |\n",
"| Experiment: 2 | Episode: 1401 | Score: -248.94 | Avg Score: -92.72 |\n",
"| Experiment: 2 | Episode: 1402 | Score: -97.76 | Avg Score: -93.51 |\n",
"| Experiment: 2 | Episode: 1403 | Score: -38.79 | Avg Score: -92.95 |\n",
"| Experiment: 2 | Episode: 1404 | Score: -49.4 | Avg Score: -92.91 |\n",
"| Experiment: 2 | Episode: 1405 | Score: -41.88 | Avg Score: -91.4 |\n",
"| Experiment: 2 | Episode: 1406 | Score: -102.69 | Avg Score: -90.77 |\n",
"| Experiment: 2 | Episode: 1407 | Score: -88.87 | Avg Score: -91.03 |\n",
"| Experiment: 2 | Episode: 1408 | Score: -209.76 | Avg Score: -92.15 |\n",
"| Experiment: 2 | Episode: 1409 | Score: -58.34 | Avg Score: -91.85 |\n",
"| Experiment: 2 | Episode: 1410 | Score: -130.9 | Avg Score: -92.13 |\n",
"| Experiment: 2 | Episode: 1411 | Score: -20.0 | Avg Score: -91.86 |\n",
"| Experiment: 2 | Episode: 1412 | Score: -52.48 | Avg Score: -91.52 |\n",
"| Experiment: 2 | Episode: 1413 | Score: 3.91 | Avg Score: -90.42 |\n",
"| Experiment: 2 | Episode: 1414 | Score: -70.99 | Avg Score: -90.39 |\n",
"| Experiment: 2 | Episode: 1415 | Score: -67.45 | Avg Score: -90.56 |\n",
"| Experiment: 2 | Episode: 1416 | Score: -59.29 | Avg Score: -89.48 |\n",
"| Experiment: 2 | Episode: 1417 | Score: -87.23 | Avg Score: -90.01 |\n",
"| Experiment: 2 | Episode: 1418 | Score: -70.74 | Avg Score: -89.48 |\n",
"| Experiment: 2 | Episode: 1419 | Score: -97.71 | Avg Score: -90.71 |\n",
"| Experiment: 2 | Episode: 1420 | Score: -75.15 | Avg Score: -90.47 |\n",
"| Experiment: 2 | Episode: 1421 | Score: -155.19 | Avg Score: -91.58 |\n",
"| Experiment: 2 | Episode: 1422 | Score: -17.57 | Avg Score: -90.05 |\n",
"| Experiment: 2 | Episode: 1423 | Score: -41.97 | Avg Score: -89.7 |\n",
"| Experiment: 2 | Episode: 1424 | Score: -85.83 | Avg Score: -89.86 |\n",
"| Experiment: 2 | Episode: 1425 | Score: -71.48 | Avg Score: -89.95 |\n",
"| Experiment: 2 | Episode: 1426 | Score: -61.61 | Avg Score: -89.32 |\n",
"| Experiment: 2 | Episode: 1427 | Score: -121.14 | Avg Score: -88.24 |\n",
"| Experiment: 2 | Episode: 1428 | Score: -56.76 | Avg Score: -88.71 |\n",
"| Experiment: 2 | Episode: 1429 | Score: -89.08 | Avg Score: -88.48 |\n",
"| Experiment: 2 | Episode: 1430 | Score: -148.7 | Avg Score: -90.64 |\n",
"| Experiment: 2 | Episode: 1431 | Score: -95.31 | Avg Score: -90.68 |\n",
"| Experiment: 2 | Episode: 1432 | Score: -169.46 | Avg Score: -91.25 |\n",
"| Experiment: 2 | Episode: 1433 | Score: -65.13 | Avg Score: -90.31 |\n",
"| Experiment: 2 | Episode: 1434 | Score: -174.28 | Avg Score: -91.21 |\n",
"| Experiment: 2 | Episode: 1435 | Score: -123.55 | Avg Score: -91.95 |\n",
"| Experiment: 2 | Episode: 1436 | Score: -77.95 | Avg Score: -92.21 |\n",
"| Experiment: 2 | Episode: 1437 | Score: -53.65 | Avg Score: -91.3 |\n",
"| Experiment: 2 | Episode: 1438 | Score: -50.72 | Avg Score: -90.27 |\n",
"| Experiment: 2 | Episode: 1439 | Score: -42.52 | Avg Score: -88.84 |\n",
"| Experiment: 2 | Episode: 1440 | Score: -184.28 | Avg Score: -90.09 |\n",
"| Experiment: 2 | Episode: 1441 | Score: -202.13 | Avg Score: -91.58 |\n",
"| Experiment: 2 | Episode: 1442 | Score: -53.28 | Avg Score: -91.24 |\n",
"| Experiment: 2 | Episode: 1443 | Score: -79.63 | Avg Score: -89.15 |\n",
"| Experiment: 2 | Episode: 1444 | Score: -33.19 | Avg Score: -88.08 |\n",
"| Experiment: 2 | Episode: 1445 | Score: -75.27 | Avg Score: -88.75 |\n",
"| Experiment: 2 | Episode: 1446 | Score: -51.95 | Avg Score: -88.06 |\n",
"| Experiment: 2 | Episode: 1447 | Score: -32.54 | Avg Score: -87.43 |\n",
"| Experiment: 2 | Episode: 1448 | Score: -189.62 | Avg Score: -88.77 |\n",
"| Experiment: 2 | Episode: 1449 | Score: -132.01 | Avg Score: -89.84 |\n",
"| Experiment: 2 | Episode: 1450 | Score: -45.8 | Avg Score: -88.85 |\n",
"| Experiment: 2 | Episode: 1451 | Score: -58.09 | Avg Score: -88.6 |\n",
"| Experiment: 2 | Episode: 1452 | Score: -46.37 | Avg Score: -88.35 |\n",
"| Experiment: 2 | Episode: 1453 | Score: -37.83 | Avg Score: -86.98 |\n",
"| Experiment: 2 | Episode: 1454 | Score: -81.9 | Avg Score: -86.53 |\n",
"| Experiment: 2 | Episode: 1455 | Score: -76.18 | Avg Score: -86.56 |\n",
"| Experiment: 2 | Episode: 1456 | Score: -66.2 | Avg Score: -86.42 |\n",
"| Experiment: 2 | Episode: 1457 | Score: -66.47 | Avg Score: -86.24 |\n",
"| Experiment: 2 | Episode: 1458 | Score: -145.57 | Avg Score: -86.95 |\n",
"| Experiment: 2 | Episode: 1459 | Score: -120.55 | Avg Score: -87.57 |\n",
"| Experiment: 2 | Episode: 1460 | Score: -130.81 | Avg Score: -87.82 |\n",
"| Experiment: 2 | Episode: 1461 | Score: -78.31 | Avg Score: -87.64 |\n",
"| Experiment: 2 | Episode: 1462 | Score: -89.34 | Avg Score: -88.16 |\n",
"| Experiment: 2 | Episode: 1463 | Score: -97.42 | Avg Score: -88.72 |\n",
"| Experiment: 2 | Episode: 1464 | Score: -73.43 | Avg Score: -89.23 |\n",
"| Experiment: 2 | Episode: 1465 | Score: -35.83 | Avg Score: -88.67 |\n",
"| Experiment: 2 | Episode: 1466 | Score: -48.88 | Avg Score: -87.68 |\n",
"| Experiment: 2 | Episode: 1467 | Score: -84.31 | Avg Score: -86.2 |\n",
"| Experiment: 2 | Episode: 1468 | Score: -34.09 | Avg Score: -85.86 |\n",
"| Experiment: 2 | Episode: 1469 | Score: -40.79 | Avg Score: -85.77 |\n",
"| Experiment: 2 | Episode: 1470 | Score: -92.33 | Avg Score: -85.63 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 2 | Episode: 1471 | Score: -83.94 | Avg Score: -85.8 |\n",
"| Experiment: 2 | Episode: 1472 | Score: -73.13 | Avg Score: -86.06 |\n",
"| Experiment: 2 | Episode: 1473 | Score: -45.1 | Avg Score: -85.96 |\n",
"| Experiment: 2 | Episode: 1474 | Score: -63.53 | Avg Score: -84.97 |\n",
"| Experiment: 2 | Episode: 1475 | Score: -101.42 | Avg Score: -84.93 |\n",
"| Experiment: 2 | Episode: 1476 | Score: -111.41 | Avg Score: -85.2 |\n",
"| Experiment: 2 | Episode: 1477 | Score: -19.38 | Avg Score: -85.19 |\n",
"| Experiment: 2 | Episode: 1478 | Score: -46.06 | Avg Score: -85.52 |\n",
"| Experiment: 2 | Episode: 1479 | Score: -111.88 | Avg Score: -85.96 |\n",
"| Experiment: 2 | Episode: 1480 | Score: -83.74 | Avg Score: -85.75 |\n",
"| Experiment: 2 | Episode: 1481 | Score: -41.09 | Avg Score: -86.1 |\n",
"| Experiment: 2 | Episode: 1482 | Score: -85.19 | Avg Score: -83.86 |\n",
"| Experiment: 2 | Episode: 1483 | Score: -60.15 | Avg Score: -83.57 |\n",
"| Experiment: 2 | Episode: 1484 | Score: -64.74 | Avg Score: -83.49 |\n",
"| Experiment: 2 | Episode: 1485 | Score: -84.84 | Avg Score: -82.03 |\n",
"| Experiment: 2 | Episode: 1486 | Score: -87.79 | Avg Score: -82.17 |\n",
"| Experiment: 2 | Episode: 1487 | Score: -83.14 | Avg Score: -82.39 |\n",
"| Experiment: 2 | Episode: 1488 | Score: -95.95 | Avg Score: -82.02 |\n",
"| Experiment: 2 | Episode: 1489 | Score: -89.21 | Avg Score: -82.25 |\n",
"| Experiment: 2 | Episode: 1490 | Score: -44.29 | Avg Score: -82.08 |\n",
"| Experiment: 2 | Episode: 1491 | Score: -52.77 | Avg Score: -81.74 |\n",
"| Experiment: 2 | Episode: 1492 | Score: -13.0 | Avg Score: -80.78 |\n",
"| Experiment: 2 | Episode: 1493 | Score: -28.3 | Avg Score: -80.49 |\n",
"| Experiment: 2 | Episode: 1494 | Score: -113.06 | Avg Score: -81.14 |\n",
"| Experiment: 2 | Episode: 1495 | Score: -77.3 | Avg Score: -81.5 |\n",
"| Experiment: 2 | Episode: 1496 | Score: -34.67 | Avg Score: -80.82 |\n",
"| Experiment: 2 | Episode: 1497 | Score: -104.13 | Avg Score: -80.82 |\n",
"| Experiment: 2 | Episode: 1498 | Score: -229.89 | Avg Score: -83.16 |\n",
"| Experiment: 2 | Episode: 1499 | Score: -71.86 | Avg Score: -82.54 |\n",
"| Experiment: 2 | Episode: 1500 | Score: -59.96 | Avg Score: -82.12 |\n",
"| Experiment: 2 | Episode: 1501 | Score: -108.8 | Avg Score: -80.72 |\n",
"| Experiment: 2 | Episode: 1502 | Score: -64.41 | Avg Score: -80.38 |\n",
"| Experiment: 2 | Episode: 1503 | Score: -35.73 | Avg Score: -80.35 |\n",
"| Experiment: 2 | Episode: 1504 | Score: -199.4 | Avg Score: -81.85 |\n",
"| Experiment: 2 | Episode: 1505 | Score: -46.11 | Avg Score: -81.89 |\n",
"| Experiment: 2 | Episode: 1506 | Score: -60.13 | Avg Score: -81.47 |\n",
"| Experiment: 2 | Episode: 1507 | Score: -67.16 | Avg Score: -81.25 |\n",
"| Experiment: 2 | Episode: 1508 | Score: -270.24 | Avg Score: -81.86 |\n",
"| Experiment: 2 | Episode: 1509 | Score: -190.4 | Avg Score: -83.18 |\n",
"| Experiment: 2 | Episode: 1510 | Score: -21.99 | Avg Score: -82.09 |\n",
"| Experiment: 2 | Episode: 1511 | Score: -65.62 | Avg Score: -82.54 |\n",
"| Experiment: 2 | Episode: 1512 | Score: -64.59 | Avg Score: -82.66 |\n",
"| Experiment: 2 | Episode: 1513 | Score: -11.64 | Avg Score: -82.82 |\n",
"| Experiment: 2 | Episode: 1514 | Score: -94.42 | Avg Score: -83.05 |\n",
"| Experiment: 2 | Episode: 1515 | Score: -74.99 | Avg Score: -83.13 |\n",
"| Experiment: 2 | Episode: 1516 | Score: -43.73 | Avg Score: -82.97 |\n",
"| Experiment: 2 | Episode: 1517 | Score: -49.51 | Avg Score: -82.6 |\n",
"| Experiment: 2 | Episode: 1518 | Score: -25.01 | Avg Score: -82.14 |\n",
"| Experiment: 2 | Episode: 1519 | Score: -56.29 | Avg Score: -81.73 |\n",
"| Experiment: 2 | Episode: 1520 | Score: -62.13 | Avg Score: -81.6 |\n",
"| Experiment: 2 | Episode: 1521 | Score: -19.45 | Avg Score: -80.24 |\n",
"| Experiment: 2 | Episode: 1522 | Score: -121.18 | Avg Score: -81.27 |\n",
"| Experiment: 2 | Episode: 1523 | Score: -56.77 | Avg Score: -81.42 |\n",
"| Experiment: 2 | Episode: 1524 | Score: -278.0 | Avg Score: -83.34 |\n",
"| Experiment: 2 | Episode: 1525 | Score: -215.79 | Avg Score: -84.79 |\n",
"| Experiment: 2 | Episode: 1526 | Score: -40.82 | Avg Score: -84.58 |\n",
"| Experiment: 2 | Episode: 1527 | Score: -485.12 | Avg Score: -88.22 |\n",
"| Experiment: 2 | Episode: 1528 | Score: -105.36 | Avg Score: -88.7 |\n",
"| Experiment: 2 | Episode: 1529 | Score: -70.88 | Avg Score: -88.52 |\n",
"| Experiment: 2 | Episode: 1530 | Score: -37.7 | Avg Score: -87.41 |\n",
"| Experiment: 2 | Episode: 1531 | Score: -23.41 | Avg Score: -86.69 |\n",
"| Experiment: 2 | Episode: 1532 | Score: -64.37 | Avg Score: -85.64 |\n",
"| Experiment: 2 | Episode: 1533 | Score: -34.75 | Avg Score: -85.34 |\n",
"| Experiment: 2 | Episode: 1534 | Score: -101.34 | Avg Score: -84.61 |\n",
"| Experiment: 2 | Episode: 1535 | Score: -77.96 | Avg Score: -84.15 |\n",
"| Experiment: 2 | Episode: 1536 | Score: -309.9 | Avg Score: -86.47 |\n",
"| Experiment: 2 | Episode: 1537 | Score: -72.94 | Avg Score: -86.67 |\n",
"| Experiment: 2 | Episode: 1538 | Score: -62.54 | Avg Score: -86.78 |\n",
"| Experiment: 2 | Episode: 1539 | Score: -87.47 | Avg Score: -87.23 |\n",
"| Experiment: 2 | Episode: 1540 | Score: -73.67 | Avg Score: -86.13 |\n",
"| Experiment: 2 | Episode: 1541 | Score: -182.41 | Avg Score: -85.93 |\n",
"| Experiment: 2 | Episode: 1542 | Score: -104.33 | Avg Score: -86.44 |\n",
"| Experiment: 2 | Episode: 1543 | Score: -111.4 | Avg Score: -86.76 |\n",
"| Experiment: 2 | Episode: 1544 | Score: -103.12 | Avg Score: -87.46 |\n",
"| Experiment: 2 | Episode: 1545 | Score: -44.94 | Avg Score: -87.15 |\n",
"| Experiment: 2 | Episode: 1546 | Score: -49.63 | Avg Score: -87.13 |\n",
"| Experiment: 2 | Episode: 1547 | Score: -26.82 | Avg Score: -87.07 |\n",
"| Experiment: 2 | Episode: 1548 | Score: -77.36 | Avg Score: -85.95 |\n",
"| Experiment: 2 | Episode: 1549 | Score: -26.36 | Avg Score: -84.89 |\n",
"| Experiment: 2 | Episode: 1550 | Score: -56.73 | Avg Score: -85.0 |\n",
"| Experiment: 2 | Episode: 1551 | Score: -37.56 | Avg Score: -84.8 |\n",
"| Experiment: 2 | Episode: 1552 | Score: -58.83 | Avg Score: -84.92 |\n",
"| Experiment: 2 | Episode: 1553 | Score: -61.09 | Avg Score: -85.16 |\n",
"| Experiment: 2 | Episode: 1554 | Score: -45.83 | Avg Score: -84.8 |\n",
"| Experiment: 2 | Episode: 1555 | Score: -66.09 | Avg Score: -84.69 |\n",
"| Experiment: 2 | Episode: 1556 | Score: -105.23 | Avg Score: -85.09 |\n",
"| Experiment: 2 | Episode: 1557 | Score: -68.88 | Avg Score: -85.11 |\n",
"| Experiment: 2 | Episode: 1558 | Score: -53.59 | Avg Score: -84.19 |\n",
"| Experiment: 2 | Episode: 1559 | Score: -71.3 | Avg Score: -83.7 |\n",
"| Experiment: 2 | Episode: 1560 | Score: 49.03 | Avg Score: -81.9 |\n",
"| Experiment: 2 | Episode: 1561 | Score: -102.32 | Avg Score: -82.14 |\n",
"| Experiment: 2 | Episode: 1562 | Score: -108.28 | Avg Score: -82.33 |\n",
"| Experiment: 2 | Episode: 1563 | Score: -85.58 | Avg Score: -82.21 |\n",
"| Experiment: 2 | Episode: 1564 | Score: -33.31 | Avg Score: -81.81 |\n",
"| Experiment: 2 | Episode: 1565 | Score: -124.73 | Avg Score: -82.7 |\n",
"| Experiment: 2 | Episode: 1566 | Score: -66.74 | Avg Score: -82.88 |\n",
"| Experiment: 2 | Episode: 1567 | Score: -57.1 | Avg Score: -82.6 |\n",
"| Experiment: 2 | Episode: 1568 | Score: -263.83 | Avg Score: -84.9 |\n",
"| Experiment: 2 | Episode: 1569 | Score: -34.18 | Avg Score: -84.83 |\n",
"| Experiment: 2 | Episode: 1570 | Score: -90.93 | Avg Score: -84.82 |\n",
"| Experiment: 2 | Episode: 1571 | Score: -78.9 | Avg Score: -84.77 |\n",
"| Experiment: 2 | Episode: 1572 | Score: -116.55 | Avg Score: -85.2 |\n",
"| Experiment: 2 | Episode: 1573 | Score: -64.62 | Avg Score: -85.4 |\n",
"| Experiment: 2 | Episode: 1574 | Score: -90.46 | Avg Score: -85.67 |\n",
"| Experiment: 2 | Episode: 1575 | Score: -88.1 | Avg Score: -85.54 |\n",
"| Experiment: 2 | Episode: 1576 | Score: -79.86 | Avg Score: -85.22 |\n",
"| Experiment: 2 | Episode: 1577 | Score: -62.94 | Avg Score: -85.66 |\n",
"| Experiment: 2 | Episode: 1578 | Score: -44.86 | Avg Score: -85.64 |\n",
"| Experiment: 2 | Episode: 1579 | Score: -39.11 | Avg Score: -84.92 |\n",
"| Experiment: 2 | Episode: 1580 | Score: -105.87 | Avg Score: -85.14 |\n",
"| Experiment: 2 | Episode: 1581 | Score: -74.86 | Avg Score: -85.48 |\n",
"| Experiment: 2 | Episode: 1582 | Score: -70.74 | Avg Score: -85.33 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 2 | Episode: 1583 | Score: -62.15 | Avg Score: -85.35 |\n",
"| Experiment: 2 | Episode: 1584 | Score: -84.9 | Avg Score: -85.55 |\n",
"| Experiment: 2 | Episode: 1585 | Score: -50.37 | Avg Score: -85.21 |\n",
"| Experiment: 2 | Episode: 1586 | Score: -41.22 | Avg Score: -84.74 |\n",
"| Experiment: 2 | Episode: 1587 | Score: -54.18 | Avg Score: -84.45 |\n",
"| Experiment: 2 | Episode: 1588 | Score: -68.91 | Avg Score: -84.18 |\n",
"| Experiment: 2 | Episode: 1589 | Score: -73.36 | Avg Score: -84.02 |\n",
"| Experiment: 2 | Episode: 1590 | Score: -103.67 | Avg Score: -84.62 |\n",
"| Experiment: 2 | Episode: 1591 | Score: -72.26 | Avg Score: -84.81 |\n",
"| Experiment: 2 | Episode: 1592 | Score: -111.68 | Avg Score: -85.8 |\n",
"| Experiment: 2 | Episode: 1593 | Score: -113.1 | Avg Score: -86.65 |\n",
"| Experiment: 2 | Episode: 1594 | Score: -41.06 | Avg Score: -85.93 |\n",
"| Experiment: 2 | Episode: 1595 | Score: -58.36 | Avg Score: -85.74 |\n",
"| Experiment: 2 | Episode: 1596 | Score: -54.41 | Avg Score: -85.94 |\n",
"| Experiment: 2 | Episode: 1597 | Score: -31.13 | Avg Score: -85.21 |\n",
"| Experiment: 2 | Episode: 1598 | Score: -72.26 | Avg Score: -83.63 |\n",
"| Experiment: 2 | Episode: 1599 | Score: -47.99 | Avg Score: -83.39 |\n",
"| Experiment: 2 | Episode: 1600 | Score: -94.91 | Avg Score: -83.74 |\n",
"| Experiment: 2 | Episode: 1601 | Score: -49.02 | Avg Score: -83.14 |\n",
"| Experiment: 2 | Episode: 1602 | Score: -65.27 | Avg Score: -83.15 |\n",
"| Experiment: 2 | Episode: 1603 | Score: -172.73 | Avg Score: -84.52 |\n",
"| Experiment: 2 | Episode: 1604 | Score: -68.43 | Avg Score: -83.21 |\n",
"| Experiment: 2 | Episode: 1605 | Score: -70.7 | Avg Score: -83.46 |\n",
"| Experiment: 2 | Episode: 1606 | Score: -73.17 | Avg Score: -83.59 |\n",
"| Experiment: 2 | Episode: 1607 | Score: -53.54 | Avg Score: -83.45 |\n",
"| Experiment: 2 | Episode: 1608 | Score: -78.12 | Avg Score: -81.53 |\n",
"| Experiment: 2 | Episode: 1609 | Score: -49.41 | Avg Score: -80.12 |\n",
"| Experiment: 2 | Episode: 1610 | Score: -112.8 | Avg Score: -81.03 |\n",
"| Experiment: 2 | Episode: 1611 | Score: -61.32 | Avg Score: -80.98 |\n",
"| Experiment: 2 | Episode: 1612 | Score: -75.72 | Avg Score: -81.1 |\n",
"| Experiment: 2 | Episode: 1613 | Score: -7.75 | Avg Score: -81.06 |\n",
"| Experiment: 2 | Episode: 1614 | Score: 12.17 | Avg Score: -79.99 |\n",
"| Experiment: 2 | Episode: 1615 | Score: -111.61 | Avg Score: -80.36 |\n",
"| Experiment: 2 | Episode: 1616 | Score: -73.86 | Avg Score: -80.66 |\n",
"| Experiment: 2 | Episode: 1617 | Score: -66.16 | Avg Score: -80.83 |\n",
"| Experiment: 2 | Episode: 1618 | Score: -34.48 | Avg Score: -80.92 |\n",
"| Experiment: 2 | Episode: 1619 | Score: -51.93 | Avg Score: -80.88 |\n",
"| Experiment: 2 | Episode: 1620 | Score: -65.29 | Avg Score: -80.91 |\n",
"| Experiment: 2 | Episode: 1621 | Score: -105.97 | Avg Score: -81.77 |\n",
"| Experiment: 2 | Episode: 1622 | Score: -5.61 | Avg Score: -80.62 |\n",
"| Experiment: 2 | Episode: 1623 | Score: -78.97 | Avg Score: -80.84 |\n",
"| Experiment: 2 | Episode: 1624 | Score: -54.79 | Avg Score: -78.61 |\n",
"| Experiment: 2 | Episode: 1625 | Score: -48.16 | Avg Score: -76.93 |\n",
"| Experiment: 2 | Episode: 1626 | Score: -27.96 | Avg Score: -76.8 |\n",
"| Experiment: 2 | Episode: 1627 | Score: -71.87 | Avg Score: -72.67 |\n",
"| Experiment: 2 | Episode: 1628 | Score: -4.69 | Avg Score: -71.66 |\n",
"| Experiment: 2 | Episode: 1629 | Score: -44.94 | Avg Score: -71.4 |\n",
"| Experiment: 2 | Episode: 1630 | Score: -113.75 | Avg Score: -72.16 |\n",
"| Experiment: 2 | Episode: 1631 | Score: -65.56 | Avg Score: -72.59 |\n",
"| Experiment: 2 | Episode: 1632 | Score: -83.54 | Avg Score: -72.78 |\n",
"| Experiment: 2 | Episode: 1633 | Score: -69.46 | Avg Score: -73.12 |\n",
"| Experiment: 2 | Episode: 1634 | Score: -90.5 | Avg Score: -73.02 |\n",
"| Experiment: 2 | Episode: 1635 | Score: -124.29 | Avg Score: -73.48 |\n",
"| Experiment: 2 | Episode: 1636 | Score: -77.63 | Avg Score: -71.16 |\n",
"| Experiment: 2 | Episode: 1637 | Score: -69.1 | Avg Score: -71.12 |\n",
"| Experiment: 2 | Episode: 1638 | Score: -146.68 | Avg Score: -71.96 |\n",
"| Experiment: 2 | Episode: 1639 | Score: -223.27 | Avg Score: -73.32 |\n",
"| Experiment: 2 | Episode: 1640 | Score: -73.71 | Avg Score: -73.32 |\n",
"| Experiment: 2 | Episode: 1641 | Score: -56.74 | Avg Score: -72.06 |\n",
"| Experiment: 2 | Episode: 1642 | Score: -32.97 | Avg Score: -71.35 |\n",
"| Experiment: 2 | Episode: 1643 | Score: -54.82 | Avg Score: -70.78 |\n",
"| Experiment: 2 | Episode: 1644 | Score: 24.78 | Avg Score: -69.5 |\n",
"| Experiment: 2 | Episode: 1645 | Score: -135.18 | Avg Score: -70.41 |\n",
"| Experiment: 2 | Episode: 1646 | Score: -35.78 | Avg Score: -70.27 |\n",
"| Experiment: 2 | Episode: 1647 | Score: -94.46 | Avg Score: -70.94 |\n",
"| Experiment: 2 | Episode: 1648 | Score: -110.81 | Avg Score: -71.28 |\n",
"| Experiment: 2 | Episode: 1649 | Score: -41.54 | Avg Score: -71.43 |\n",
"| Experiment: 2 | Episode: 1650 | Score: -78.58 | Avg Score: -71.65 |\n",
"| Experiment: 2 | Episode: 1651 | Score: -53.36 | Avg Score: -71.81 |\n",
"| Experiment: 2 | Episode: 1652 | Score: -59.12 | Avg Score: -71.81 |\n",
"| Experiment: 2 | Episode: 1653 | Score: -46.65 | Avg Score: -71.67 |\n",
"| Experiment: 2 | Episode: 1654 | Score: -43.39 | Avg Score: -71.64 |\n",
"| Experiment: 2 | Episode: 1655 | Score: -62.02 | Avg Score: -71.6 |\n",
"| Experiment: 2 | Episode: 1656 | Score: -27.39 | Avg Score: -70.82 |\n",
"| Experiment: 2 | Episode: 1657 | Score: -46.35 | Avg Score: -70.6 |\n",
"| Experiment: 2 | Episode: 1658 | Score: -23.87 | Avg Score: -70.3 |\n",
"| Experiment: 2 | Episode: 1659 | Score: -44.0 | Avg Score: -70.03 |\n",
"| Experiment: 2 | Episode: 1660 | Score: -49.08 | Avg Score: -71.01 |\n",
"| Experiment: 2 | Episode: 1661 | Score: -61.3 | Avg Score: -70.6 |\n",
"| Experiment: 2 | Episode: 1662 | Score: -86.02 | Avg Score: -70.37 |\n",
"| Experiment: 2 | Episode: 1663 | Score: -58.57 | Avg Score: -70.1 |\n",
"| Experiment: 2 | Episode: 1664 | Score: -81.01 | Avg Score: -70.58 |\n",
"| Experiment: 2 | Episode: 1665 | Score: -15.21 | Avg Score: -69.49 |\n",
"| Experiment: 2 | Episode: 1666 | Score: 30.37 | Avg Score: -68.52 |\n",
"| Experiment: 2 | Episode: 1667 | Score: -49.04 | Avg Score: -68.43 |\n",
"| Experiment: 2 | Episode: 1668 | Score: -52.96 | Avg Score: -66.33 |\n",
"| Experiment: 2 | Episode: 1669 | Score: -3.2 | Avg Score: -66.02 |\n",
"| Experiment: 2 | Episode: 1670 | Score: -67.61 | Avg Score: -65.78 |\n",
"| Experiment: 2 | Episode: 1671 | Score: -78.26 | Avg Score: -65.78 |\n",
"| Experiment: 2 | Episode: 1672 | Score: -124.85 | Avg Score: -65.86 |\n",
"| Experiment: 2 | Episode: 1673 | Score: -74.93 | Avg Score: -65.96 |\n",
"| Experiment: 2 | Episode: 1674 | Score: -166.75 | Avg Score: -66.73 |\n",
"| Experiment: 2 | Episode: 1675 | Score: -8.97 | Avg Score: -65.93 |\n",
"| Experiment: 2 | Episode: 1676 | Score: -164.29 | Avg Score: -66.78 |\n",
"| Experiment: 2 | Episode: 1677 | Score: -26.62 | Avg Score: -66.42 |\n",
"| Experiment: 2 | Episode: 1678 | Score: -77.0 | Avg Score: -66.74 |\n",
"| Experiment: 2 | Episode: 1679 | Score: -53.42 | Avg Score: -66.88 |\n",
"| Experiment: 2 | Episode: 1680 | Score: -43.66 | Avg Score: -66.26 |\n",
"| Experiment: 2 | Episode: 1681 | Score: -45.58 | Avg Score: -65.97 |\n",
"| Experiment: 2 | Episode: 1682 | Score: -25.36 | Avg Score: -65.51 |\n",
"| Experiment: 2 | Episode: 1683 | Score: -123.45 | Avg Score: -66.12 |\n",
"| Experiment: 2 | Episode: 1684 | Score: -68.36 | Avg Score: -65.96 |\n",
"| Experiment: 2 | Episode: 1685 | Score: -235.96 | Avg Score: -67.82 |\n",
"| Experiment: 2 | Episode: 1686 | Score: -87.6 | Avg Score: -68.28 |\n",
"| Experiment: 2 | Episode: 1687 | Score: -83.86 | Avg Score: -68.58 |\n",
"| Experiment: 2 | Episode: 1688 | Score: -23.12 | Avg Score: -68.12 |\n",
"| Experiment: 2 | Episode: 1689 | Score: -48.29 | Avg Score: -67.87 |\n",
"| Experiment: 2 | Episode: 1690 | Score: -32.9 | Avg Score: -67.16 |\n",
"| Experiment: 2 | Episode: 1691 | Score: -56.75 | Avg Score: -67.0 |\n",
"| Experiment: 2 | Episode: 1692 | Score: -28.46 | Avg Score: -66.17 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 2 | Episode: 1693 | Score: -115.99 | Avg Score: -66.2 |\n",
"| Experiment: 2 | Episode: 1694 | Score: -63.3 | Avg Score: -66.42 |\n",
"| Experiment: 2 | Episode: 1695 | Score: -88.56 | Avg Score: -66.73 |\n",
"| Experiment: 2 | Episode: 1696 | Score: -125.43 | Avg Score: -67.44 |\n",
"| Experiment: 2 | Episode: 1697 | Score: -30.12 | Avg Score: -67.43 |\n",
"| Experiment: 2 | Episode: 1698 | Score: -60.05 | Avg Score: -67.3 |\n",
"| Experiment: 2 | Episode: 1699 | Score: -60.22 | Avg Score: -67.43 |\n",
"| Experiment: 2 | Episode: 1700 | Score: -52.99 | Avg Score: -67.01 |\n",
"| Experiment: 2 | Episode: 1701 | Score: -104.54 | Avg Score: -67.56 |\n",
"| Experiment: 2 | Episode: 1702 | Score: -72.03 | Avg Score: -67.63 |\n",
"| Experiment: 2 | Episode: 1703 | Score: -77.72 | Avg Score: -66.68 |\n",
"| Experiment: 2 | Episode: 1704 | Score: -79.85 | Avg Score: -66.79 |\n",
"| Experiment: 2 | Episode: 1705 | Score: -62.39 | Avg Score: -66.71 |\n",
"| Experiment: 2 | Episode: 1706 | Score: -66.97 | Avg Score: -66.65 |\n",
"| Experiment: 2 | Episode: 1707 | Score: -128.8 | Avg Score: -67.4 |\n",
"| Experiment: 2 | Episode: 1708 | Score: -31.66 | Avg Score: -66.94 |\n",
"| Experiment: 2 | Episode: 1709 | Score: -28.6 | Avg Score: -66.73 |\n",
"| Experiment: 2 | Episode: 1710 | Score: -110.0 | Avg Score: -66.7 |\n",
"| Experiment: 2 | Episode: 1711 | Score: -27.83 | Avg Score: -66.37 |\n",
"| Experiment: 2 | Episode: 1712 | Score: -86.19 | Avg Score: -66.47 |\n",
"| Experiment: 2 | Episode: 1713 | Score: -52.09 | Avg Score: -66.91 |\n",
"| Experiment: 2 | Episode: 1714 | Score: -16.28 | Avg Score: -67.2 |\n",
"| Experiment: 2 | Episode: 1715 | Score: -89.47 | Avg Score: -66.98 |\n",
"| Experiment: 2 | Episode: 1716 | Score: -195.95 | Avg Score: -68.2 |\n",
"| Experiment: 2 | Episode: 1717 | Score: -44.07 | Avg Score: -67.98 |\n",
"| Experiment: 2 | Episode: 1718 | Score: -53.96 | Avg Score: -68.17 |\n",
"| Experiment: 2 | Episode: 1719 | Score: 1.65 | Avg Score: -67.64 |\n",
"| Experiment: 2 | Episode: 1720 | Score: -16.16 | Avg Score: -67.14 |\n",
"| Experiment: 2 | Episode: 1721 | Score: -41.36 | Avg Score: -66.5 |\n",
"| Experiment: 2 | Episode: 1722 | Score: -56.15 | Avg Score: -67.0 |\n",
"| Experiment: 2 | Episode: 1723 | Score: -73.43 | Avg Score: -66.95 |\n",
"| Experiment: 2 | Episode: 1724 | Score: -91.23 | Avg Score: -67.31 |\n",
"| Experiment: 2 | Episode: 1725 | Score: -78.41 | Avg Score: -67.61 |\n",
"| Experiment: 2 | Episode: 1726 | Score: -88.59 | Avg Score: -68.22 |\n",
"| Experiment: 2 | Episode: 1727 | Score: -88.03 | Avg Score: -68.38 |\n",
"| Experiment: 2 | Episode: 1728 | Score: -44.75 | Avg Score: -68.78 |\n",
"| Experiment: 2 | Episode: 1729 | Score: -323.49 | Avg Score: -71.57 |\n",
"| Experiment: 2 | Episode: 1730 | Score: -32.15 | Avg Score: -70.75 |\n",
"| Experiment: 2 | Episode: 1731 | Score: -55.7 | Avg Score: -70.65 |\n",
"| Experiment: 2 | Episode: 1732 | Score: -64.78 | Avg Score: -70.47 |\n",
"| Experiment: 2 | Episode: 1733 | Score: 11.67 | Avg Score: -69.66 |\n",
"| Experiment: 2 | Episode: 1734 | Score: -50.98 | Avg Score: -69.26 |\n",
"| Experiment: 2 | Episode: 1735 | Score: -102.71 | Avg Score: -69.04 |\n",
"| Experiment: 2 | Episode: 1736 | Score: 156.59 | Avg Score: -66.7 |\n",
"| Experiment: 2 | Episode: 1737 | Score: -64.34 | Avg Score: -66.65 |\n",
"| Experiment: 2 | Episode: 1738 | Score: -68.65 | Avg Score: -65.87 |\n",
"| Experiment: 2 | Episode: 1739 | Score: -75.9 | Avg Score: -64.4 |\n",
"| Experiment: 2 | Episode: 1740 | Score: -74.65 | Avg Score: -64.41 |\n",
"| Experiment: 2 | Episode: 1741 | Score: -7.31 | Avg Score: -63.92 |\n",
"| Experiment: 2 | Episode: 1742 | Score: -65.28 | Avg Score: -64.24 |\n",
"| Experiment: 2 | Episode: 1743 | Score: -14.88 | Avg Score: -63.84 |\n",
"| Experiment: 2 | Episode: 1744 | Score: -70.91 | Avg Score: -64.8 |\n",
"| Experiment: 2 | Episode: 1745 | Score: -152.02 | Avg Score: -64.96 |\n",
"| Experiment: 2 | Episode: 1746 | Score: -57.71 | Avg Score: -65.18 |\n",
"| Experiment: 2 | Episode: 1747 | Score: 3.81 | Avg Score: -64.2 |\n",
"| Experiment: 2 | Episode: 1748 | Score: -80.81 | Avg Score: -63.9 |\n",
"| Experiment: 2 | Episode: 1749 | Score: -110.51 | Avg Score: -64.59 |\n",
"| Experiment: 2 | Episode: 1750 | Score: 26.48 | Avg Score: -63.54 |\n",
"| Experiment: 2 | Episode: 1751 | Score: -2.07 | Avg Score: -63.03 |\n",
"| Experiment: 2 | Episode: 1752 | Score: -58.47 | Avg Score: -63.02 |\n",
"| Experiment: 2 | Episode: 1753 | Score: -62.61 | Avg Score: -63.18 |\n",
"| Experiment: 2 | Episode: 1754 | Score: -66.98 | Avg Score: -63.42 |\n",
"| Experiment: 2 | Episode: 1755 | Score: -56.34 | Avg Score: -63.36 |\n",
"| Experiment: 2 | Episode: 1756 | Score: -11.54 | Avg Score: -63.2 |\n",
"| Experiment: 2 | Episode: 1757 | Score: -53.33 | Avg Score: -63.27 |\n",
"| Experiment: 2 | Episode: 1758 | Score: -27.02 | Avg Score: -63.3 |\n",
"| Experiment: 2 | Episode: 1759 | Score: -78.33 | Avg Score: -63.65 |\n",
"| Experiment: 2 | Episode: 1760 | Score: 18.3 | Avg Score: -62.97 |\n",
"| Experiment: 2 | Episode: 1761 | Score: -38.3 | Avg Score: -62.74 |\n",
"| Experiment: 2 | Episode: 1762 | Score: -28.58 | Avg Score: -62.17 |\n",
"| Experiment: 2 | Episode: 1763 | Score: -94.37 | Avg Score: -62.52 |\n",
"| Experiment: 2 | Episode: 1764 | Score: -21.54 | Avg Score: -61.93 |\n",
"| Experiment: 2 | Episode: 1765 | Score: -47.01 | Avg Score: -62.25 |\n",
"| Experiment: 2 | Episode: 1766 | Score: -43.73 | Avg Score: -62.99 |\n",
"| Experiment: 2 | Episode: 1767 | Score: -113.27 | Avg Score: -63.63 |\n",
"| Experiment: 2 | Episode: 1768 | Score: -28.34 | Avg Score: -63.39 |\n",
"| Experiment: 2 | Episode: 1769 | Score: -121.79 | Avg Score: -64.57 |\n",
"| Experiment: 2 | Episode: 1770 | Score: -69.13 | Avg Score: -64.59 |\n",
"| Experiment: 2 | Episode: 1771 | Score: -64.46 | Avg Score: -64.45 |\n",
"| Experiment: 2 | Episode: 1772 | Score: -51.88 | Avg Score: -63.72 |\n",
"| Experiment: 2 | Episode: 1773 | Score: -37.54 | Avg Score: -63.34 |\n",
"| Experiment: 2 | Episode: 1774 | Score: -41.94 | Avg Score: -62.1 |\n",
"| Experiment: 2 | Episode: 1775 | Score: -120.61 | Avg Score: -63.21 |\n",
"| Experiment: 2 | Episode: 1776 | Score: -125.17 | Avg Score: -62.82 |\n",
"| Experiment: 2 | Episode: 1777 | Score: -40.6 | Avg Score: -62.96 |\n",
"| Experiment: 2 | Episode: 1778 | Score: -31.96 | Avg Score: -62.51 |\n",
"| Experiment: 2 | Episode: 1779 | Score: -17.88 | Avg Score: -62.16 |\n",
"| Experiment: 2 | Episode: 1780 | Score: -41.65 | Avg Score: -62.14 |\n",
"| Experiment: 2 | Episode: 1781 | Score: -71.9 | Avg Score: -62.4 |\n",
"| Experiment: 2 | Episode: 1782 | Score: 18.5 | Avg Score: -61.96 |\n",
"| Experiment: 2 | Episode: 1783 | Score: -18.88 | Avg Score: -60.91 |\n",
"| Experiment: 2 | Episode: 1784 | Score: -87.39 | Avg Score: -61.1 |\n",
"| Experiment: 2 | Episode: 1785 | Score: -67.81 | Avg Score: -59.42 |\n",
"| Experiment: 2 | Episode: 1786 | Score: -5.73 | Avg Score: -58.6 |\n",
"| Experiment: 2 | Episode: 1787 | Score: -37.92 | Avg Score: -58.14 |\n",
"| Experiment: 2 | Episode: 1788 | Score: -76.95 | Avg Score: -58.68 |\n",
"| Experiment: 2 | Episode: 1789 | Score: -50.8 | Avg Score: -58.71 |\n",
"| Experiment: 2 | Episode: 1790 | Score: -83.56 | Avg Score: -59.21 |\n",
"| Experiment: 2 | Episode: 1791 | Score: -125.41 | Avg Score: -59.9 |\n",
"| Experiment: 2 | Episode: 1792 | Score: 2.29 | Avg Score: -59.59 |\n",
"| Experiment: 2 | Episode: 1793 | Score: -33.42 | Avg Score: -58.77 |\n",
"| Experiment: 2 | Episode: 1794 | Score: -85.58 | Avg Score: -58.99 |\n",
"| Experiment: 2 | Episode: 1795 | Score: -112.87 | Avg Score: -59.23 |\n",
"| Experiment: 2 | Episode: 1796 | Score: -79.95 | Avg Score: -58.78 |\n",
"| Experiment: 2 | Episode: 1797 | Score: 17.68 | Avg Score: -58.3 |\n",
"| Experiment: 2 | Episode: 1798 | Score: -23.46 | Avg Score: -57.94 |\n",
"| Experiment: 2 | Episode: 1799 | Score: -48.21 | Avg Score: -57.82 |\n",
"| Experiment: 2 | Episode: 1800 | Score: -101.16 | Avg Score: -58.3 |\n",
"| Experiment: 2 | Episode: 1801 | Score: -79.48 | Avg Score: -58.05 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 2 | Episode: 1802 | Score: 1.75 | Avg Score: -57.31 |\n",
"| Experiment: 2 | Episode: 1803 | Score: -82.29 | Avg Score: -57.35 |\n",
"| Experiment: 2 | Episode: 1804 | Score: -72.11 | Avg Score: -57.28 |\n",
"| Experiment: 2 | Episode: 1805 | Score: -21.25 | Avg Score: -56.87 |\n",
"| Experiment: 2 | Episode: 1806 | Score: -13.12 | Avg Score: -56.33 |\n",
"| Experiment: 2 | Episode: 1807 | Score: -49.01 | Avg Score: -55.53 |\n",
"| Experiment: 2 | Episode: 1808 | Score: -75.07 | Avg Score: -55.96 |\n",
"| Experiment: 2 | Episode: 1809 | Score: 96.76 | Avg Score: -54.71 |\n",
"| Experiment: 2 | Episode: 1810 | Score: -180.58 | Avg Score: -55.42 |\n",
"| Experiment: 2 | Episode: 1811 | Score: -169.67 | Avg Score: -56.83 |\n",
"| Experiment: 2 | Episode: 1812 | Score: -43.68 | Avg Score: -56.41 |\n",
"| Experiment: 2 | Episode: 1813 | Score: -36.7 | Avg Score: -56.25 |\n",
"| Experiment: 2 | Episode: 1814 | Score: -25.79 | Avg Score: -56.35 |\n",
"| Experiment: 2 | Episode: 1815 | Score: -101.29 | Avg Score: -56.47 |\n",
"| Experiment: 2 | Episode: 1816 | Score: -70.82 | Avg Score: -55.22 |\n",
"| Experiment: 2 | Episode: 1817 | Score: -3.11 | Avg Score: -54.81 |\n",
"| Experiment: 2 | Episode: 1818 | Score: -39.11 | Avg Score: -54.66 |\n",
"| Experiment: 2 | Episode: 1819 | Score: -52.41 | Avg Score: -55.2 |\n",
"| Experiment: 2 | Episode: 1820 | Score: -60.69 | Avg Score: -55.64 |\n",
"| Experiment: 2 | Episode: 1821 | Score: -44.25 | Avg Score: -55.67 |\n",
"| Experiment: 2 | Episode: 1822 | Score: -26.55 | Avg Score: -55.38 |\n",
"| Experiment: 2 | Episode: 1823 | Score: -73.95 | Avg Score: -55.38 |\n",
"| Experiment: 2 | Episode: 1824 | Score: -68.14 | Avg Score: -55.15 |\n",
"| Experiment: 2 | Episode: 1825 | Score: -58.11 | Avg Score: -54.95 |\n",
"| Experiment: 2 | Episode: 1826 | Score: -61.18 | Avg Score: -54.67 |\n",
"| Experiment: 2 | Episode: 1827 | Score: -54.16 | Avg Score: -54.34 |\n",
"| Experiment: 2 | Episode: 1828 | Score: -35.65 | Avg Score: -54.25 |\n",
"| Experiment: 2 | Episode: 1829 | Score: -83.06 | Avg Score: -51.84 |\n",
"| Experiment: 2 | Episode: 1830 | Score: -72.28 | Avg Score: -52.24 |\n",
"| Experiment: 2 | Episode: 1831 | Score: -88.65 | Avg Score: -52.57 |\n",
"| Experiment: 2 | Episode: 1832 | Score: -47.93 | Avg Score: -52.4 |\n",
"| Experiment: 2 | Episode: 1833 | Score: -42.91 | Avg Score: -52.95 |\n",
"| Experiment: 2 | Episode: 1834 | Score: -35.86 | Avg Score: -52.8 |\n",
"| Experiment: 2 | Episode: 1835 | Score: -67.85 | Avg Score: -52.45 |\n",
"| Experiment: 2 | Episode: 1836 | Score: -145.31 | Avg Score: -55.47 |\n",
"| Experiment: 2 | Episode: 1837 | Score: -36.37 | Avg Score: -55.19 |\n",
"| Experiment: 2 | Episode: 1838 | Score: 114.4 | Avg Score: -53.36 |\n",
"| Experiment: 2 | Episode: 1839 | Score: -67.61 | Avg Score: -53.27 |\n",
"| Experiment: 2 | Episode: 1840 | Score: -27.53 | Avg Score: -52.8 |\n",
"| Experiment: 2 | Episode: 1841 | Score: -33.1 | Avg Score: -53.06 |\n",
"| Experiment: 2 | Episode: 1842 | Score: -21.65 | Avg Score: -52.63 |\n",
"| Experiment: 2 | Episode: 1843 | Score: -52.49 | Avg Score: -53.0 |\n",
"| Experiment: 2 | Episode: 1844 | Score: -95.54 | Avg Score: -53.25 |\n",
"| Experiment: 2 | Episode: 1845 | Score: -112.19 | Avg Score: -52.85 |\n",
"| Experiment: 2 | Episode: 1846 | Score: -133.97 | Avg Score: -53.61 |\n",
"| Experiment: 2 | Episode: 1847 | Score: -66.62 | Avg Score: -54.32 |\n",
"| Experiment: 2 | Episode: 1848 | Score: -96.0 | Avg Score: -54.47 |\n",
"| Experiment: 2 | Episode: 1849 | Score: -29.3 | Avg Score: -53.66 |\n",
"| Experiment: 2 | Episode: 1850 | Score: -56.19 | Avg Score: -54.48 |\n",
"| Experiment: 2 | Episode: 1851 | Score: -25.39 | Avg Score: -54.72 |\n",
"| Experiment: 2 | Episode: 1852 | Score: -164.84 | Avg Score: -55.78 |\n",
"| Experiment: 2 | Episode: 1853 | Score: -60.63 | Avg Score: -55.76 |\n",
"| Experiment: 2 | Episode: 1854 | Score: -99.95 | Avg Score: -56.09 |\n",
"| Experiment: 2 | Episode: 1855 | Score: -45.85 | Avg Score: -55.98 |\n",
"| Experiment: 2 | Episode: 1856 | Score: -42.92 | Avg Score: -56.3 |\n",
"| Experiment: 2 | Episode: 1857 | Score: -44.49 | Avg Score: -56.21 |\n",
"| Experiment: 2 | Episode: 1858 | Score: -90.83 | Avg Score: -56.85 |\n",
"| Experiment: 2 | Episode: 1859 | Score: -22.73 | Avg Score: -56.29 |\n",
"| Experiment: 2 | Episode: 1860 | Score: -32.76 | Avg Score: -56.8 |\n",
"| Experiment: 2 | Episode: 1861 | Score: -19.72 | Avg Score: -56.62 |\n",
"| Experiment: 2 | Episode: 1862 | Score: -288.98 | Avg Score: -59.22 |\n",
"| Experiment: 2 | Episode: 1863 | Score: -63.24 | Avg Score: -58.91 |\n",
"| Experiment: 2 | Episode: 1864 | Score: 21.78 | Avg Score: -58.48 |\n",
"| Experiment: 2 | Episode: 1865 | Score: -180.82 | Avg Score: -59.81 |\n",
"| Experiment: 2 | Episode: 1866 | Score: -146.69 | Avg Score: -60.84 |\n",
"| Experiment: 2 | Episode: 1867 | Score: -15.28 | Avg Score: -59.86 |\n",
"| Experiment: 2 | Episode: 1868 | Score: -80.33 | Avg Score: -60.38 |\n",
"| Experiment: 2 | Episode: 1869 | Score: -134.08 | Avg Score: -60.51 |\n",
"| Experiment: 2 | Episode: 1870 | Score: -47.62 | Avg Score: -60.29 |\n",
"| Experiment: 2 | Episode: 1871 | Score: -104.24 | Avg Score: -60.69 |\n",
"| Experiment: 2 | Episode: 1872 | Score: -127.8 | Avg Score: -61.45 |\n",
"| Experiment: 2 | Episode: 1873 | Score: -91.87 | Avg Score: -61.99 |\n",
"| Experiment: 2 | Episode: 1874 | Score: -66.86 | Avg Score: -62.24 |\n",
"| Experiment: 2 | Episode: 1875 | Score: -44.44 | Avg Score: -61.48 |\n",
"| Experiment: 2 | Episode: 1876 | Score: -109.52 | Avg Score: -61.32 |\n",
"| Experiment: 2 | Episode: 1877 | Score: -59.2 | Avg Score: -61.51 |\n",
"| Experiment: 2 | Episode: 1878 | Score: -277.33 | Avg Score: -63.96 |\n",
"| Experiment: 2 | Episode: 1879 | Score: -76.61 | Avg Score: -64.55 |\n",
"| Experiment: 2 | Episode: 1880 | Score: -4.23 | Avg Score: -64.18 |\n",
"| Experiment: 2 | Episode: 1881 | Score: -33.61 | Avg Score: -63.79 |\n",
"| Experiment: 2 | Episode: 1882 | Score: -212.63 | Avg Score: -66.1 |\n",
"| Experiment: 2 | Episode: 1883 | Score: -50.49 | Avg Score: -66.42 |\n",
"| Experiment: 2 | Episode: 1884 | Score: -36.65 | Avg Score: -65.91 |\n",
"| Experiment: 2 | Episode: 1885 | Score: -53.89 | Avg Score: -65.77 |\n",
"| Experiment: 2 | Episode: 1886 | Score: -165.17 | Avg Score: -67.37 |\n",
"| Experiment: 2 | Episode: 1887 | Score: -245.0 | Avg Score: -69.44 |\n",
"| Experiment: 2 | Episode: 1888 | Score: -11.87 | Avg Score: -68.79 |\n",
"| Experiment: 2 | Episode: 1889 | Score: -59.75 | Avg Score: -68.88 |\n",
"| Experiment: 2 | Episode: 1890 | Score: -58.03 | Avg Score: -68.62 |\n",
"| Experiment: 2 | Episode: 1891 | Score: -53.2 | Avg Score: -67.9 |\n",
"| Experiment: 2 | Episode: 1892 | Score: -1.19 | Avg Score: -67.94 |\n",
"| Experiment: 2 | Episode: 1893 | Score: 30.29 | Avg Score: -67.3 |\n",
"| Experiment: 2 | Episode: 1894 | Score: -7.4 | Avg Score: -66.52 |\n",
"| Experiment: 2 | Episode: 1895 | Score: -48.99 | Avg Score: -65.88 |\n",
"| Experiment: 2 | Episode: 1896 | Score: -30.84 | Avg Score: -65.39 |\n",
"| Experiment: 2 | Episode: 1897 | Score: -36.87 | Avg Score: -65.93 |\n",
"| Experiment: 2 | Episode: 1898 | Score: -42.72 | Avg Score: -66.12 |\n",
"| Experiment: 2 | Episode: 1899 | Score: 13.17 | Avg Score: -65.51 |\n",
"| Experiment: 2 | Episode: 1900 | Score: -98.22 | Avg Score: -65.48 |\n",
"| Experiment: 2 | Episode: 1901 | Score: -67.32 | Avg Score: -65.36 |\n",
"| Experiment: 2 | Episode: 1902 | Score: -237.7 | Avg Score: -67.75 |\n",
"| Experiment: 2 | Episode: 1903 | Score: -50.9 | Avg Score: -67.44 |\n",
"| Experiment: 2 | Episode: 1904 | Score: -78.5 | Avg Score: -67.5 |\n",
"| Experiment: 2 | Episode: 1905 | Score: -153.41 | Avg Score: -68.83 |\n",
"| Experiment: 2 | Episode: 1906 | Score: -63.7 | Avg Score: -69.33 |\n",
"| Experiment: 2 | Episode: 1907 | Score: -2.33 | Avg Score: -68.87 |\n",
"| Experiment: 2 | Episode: 1908 | Score: -46.73 | Avg Score: -68.58 |\n",
"| Experiment: 2 | Episode: 1909 | Score: -74.18 | Avg Score: -70.29 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 2 | Episode: 1910 | Score: -114.02 | Avg Score: -69.63 |\n",
"| Experiment: 2 | Episode: 1911 | Score: -37.36 | Avg Score: -68.3 |\n",
"| Experiment: 2 | Episode: 1912 | Score: -81.73 | Avg Score: -68.68 |\n",
"| Experiment: 2 | Episode: 1913 | Score: -39.09 | Avg Score: -68.71 |\n",
"| Experiment: 2 | Episode: 1914 | Score: -26.82 | Avg Score: -68.72 |\n",
"| Experiment: 2 | Episode: 1915 | Score: -9.77 | Avg Score: -67.8 |\n",
"| Experiment: 2 | Episode: 1916 | Score: -2.0 | Avg Score: -67.11 |\n",
"| Experiment: 2 | Episode: 1917 | Score: -67.95 | Avg Score: -67.76 |\n",
"| Experiment: 2 | Episode: 1918 | Score: -2.52 | Avg Score: -67.4 |\n",
"| Experiment: 2 | Episode: 1919 | Score: -50.81 | Avg Score: -67.38 |\n",
"| Experiment: 2 | Episode: 1920 | Score: 150.76 | Avg Score: -65.27 |\n",
"| Experiment: 2 | Episode: 1921 | Score: -55.82 | Avg Score: -65.38 |\n",
"| Experiment: 2 | Episode: 1922 | Score: -24.66 | Avg Score: -65.36 |\n",
"| Experiment: 2 | Episode: 1923 | Score: -67.55 | Avg Score: -65.3 |\n",
"| Experiment: 2 | Episode: 1924 | Score: -0.31 | Avg Score: -64.62 |\n",
"| Experiment: 2 | Episode: 1925 | Score: -147.32 | Avg Score: -65.51 |\n",
"| Experiment: 2 | Episode: 1926 | Score: -38.16 | Avg Score: -65.28 |\n",
"| Experiment: 2 | Episode: 1927 | Score: -21.15 | Avg Score: -64.95 |\n",
"| Experiment: 2 | Episode: 1928 | Score: -22.36 | Avg Score: -64.82 |\n",
"| Experiment: 2 | Episode: 1929 | Score: -78.3 | Avg Score: -64.77 |\n",
"| Experiment: 2 | Episode: 1930 | Score: -15.62 | Avg Score: -64.21 |\n",
"| Experiment: 2 | Episode: 1931 | Score: -25.78 | Avg Score: -63.58 |\n",
"| Experiment: 2 | Episode: 1932 | Score: -36.07 | Avg Score: -63.46 |\n",
"| Experiment: 2 | Episode: 1933 | Score: -24.24 | Avg Score: -63.27 |\n",
"| Experiment: 2 | Episode: 1934 | Score: -53.21 | Avg Score: -63.44 |\n",
"| Experiment: 2 | Episode: 1935 | Score: -75.84 | Avg Score: -63.52 |\n",
"| Experiment: 2 | Episode: 1936 | Score: -80.92 | Avg Score: -62.88 |\n",
"| Experiment: 2 | Episode: 1937 | Score: -37.7 | Avg Score: -62.89 |\n",
"| Experiment: 2 | Episode: 1938 | Score: -17.29 | Avg Score: -64.21 |\n",
"| Experiment: 2 | Episode: 1939 | Score: -13.51 | Avg Score: -63.67 |\n",
"| Experiment: 2 | Episode: 1940 | Score: -41.87 | Avg Score: -63.81 |\n",
"| Experiment: 2 | Episode: 1941 | Score: 22.64 | Avg Score: -63.26 |\n",
"| Experiment: 2 | Episode: 1942 | Score: -27.73 | Avg Score: -63.32 |\n",
"| Experiment: 2 | Episode: 1943 | Score: -22.89 | Avg Score: -63.02 |\n",
"| Experiment: 2 | Episode: 1944 | Score: -44.86 | Avg Score: -62.51 |\n",
"| Experiment: 2 | Episode: 1945 | Score: -123.61 | Avg Score: -62.63 |\n",
"| Experiment: 2 | Episode: 1946 | Score: -89.86 | Avg Score: -62.19 |\n",
"| Experiment: 2 | Episode: 1947 | Score: -44.71 | Avg Score: -61.97 |\n",
"| Experiment: 2 | Episode: 1948 | Score: -22.89 | Avg Score: -61.24 |\n",
"| Experiment: 2 | Episode: 1949 | Score: -113.53 | Avg Score: -62.08 |\n",
"| Experiment: 2 | Episode: 1950 | Score: -21.82 | Avg Score: -61.74 |\n",
"| Experiment: 2 | Episode: 1951 | Score: -42.36 | Avg Score: -61.9 |\n",
"| Experiment: 2 | Episode: 1952 | Score: -48.43 | Avg Score: -60.74 |\n",
"| Experiment: 2 | Episode: 1953 | Score: -258.53 | Avg Score: -62.72 |\n",
"| Experiment: 2 | Episode: 1954 | Score: -72.87 | Avg Score: -62.45 |\n",
"| Experiment: 2 | Episode: 1955 | Score: -121.9 | Avg Score: -63.21 |\n",
"| Experiment: 2 | Episode: 1956 | Score: -47.69 | Avg Score: -63.26 |\n",
"| Experiment: 2 | Episode: 1957 | Score: -57.56 | Avg Score: -63.39 |\n",
"| Experiment: 2 | Episode: 1958 | Score: -163.84 | Avg Score: -64.12 |\n",
"| Experiment: 2 | Episode: 1959 | Score: -91.96 | Avg Score: -64.81 |\n",
"| Experiment: 2 | Episode: 1960 | Score: -10.03 | Avg Score: -64.58 |\n",
"| Experiment: 2 | Episode: 1961 | Score: 13.91 | Avg Score: -64.25 |\n",
"| Experiment: 2 | Episode: 1962 | Score: -8.52 | Avg Score: -61.44 |\n",
"| Experiment: 2 | Episode: 1963 | Score: -20.4 | Avg Score: -61.01 |\n",
"| Experiment: 2 | Episode: 1964 | Score: -4.32 | Avg Score: -61.28 |\n",
"| Experiment: 2 | Episode: 1965 | Score: 60.25 | Avg Score: -58.86 |\n",
"| Experiment: 2 | Episode: 1966 | Score: -63.6 | Avg Score: -58.03 |\n",
"| Experiment: 2 | Episode: 1967 | Score: -29.87 | Avg Score: -58.18 |\n",
"| Experiment: 2 | Episode: 1968 | Score: -26.57 | Avg Score: -57.64 |\n",
"| Experiment: 2 | Episode: 1969 | Score: -48.99 | Avg Score: -56.79 |\n",
"| Experiment: 2 | Episode: 1970 | Score: -45.77 | Avg Score: -56.77 |\n",
"| Experiment: 2 | Episode: 1971 | Score: -23.7 | Avg Score: -55.97 |\n",
"| Experiment: 2 | Episode: 1972 | Score: -63.28 | Avg Score: -55.32 |\n",
"| Experiment: 2 | Episode: 1973 | Score: -150.35 | Avg Score: -55.91 |\n",
"| Experiment: 2 | Episode: 1974 | Score: -67.44 | Avg Score: -55.91 |\n",
"| Experiment: 2 | Episode: 1975 | Score: -43.25 | Avg Score: -55.9 |\n",
"| Experiment: 2 | Episode: 1976 | Score: 14.59 | Avg Score: -54.66 |\n",
"| Experiment: 2 | Episode: 1977 | Score: 19.54 | Avg Score: -53.87 |\n",
"| Experiment: 2 | Episode: 1978 | Score: 37.68 | Avg Score: -50.72 |\n",
"| Experiment: 2 | Episode: 1979 | Score: -104.69 | Avg Score: -51.0 |\n",
"| Experiment: 2 | Episode: 1980 | Score: -55.08 | Avg Score: -51.51 |\n",
"| Experiment: 2 | Episode: 1981 | Score: -0.97 | Avg Score: -51.18 |\n",
"| Experiment: 2 | Episode: 1982 | Score: -235.48 | Avg Score: -51.41 |\n",
"| Experiment: 2 | Episode: 1983 | Score: -83.99 | Avg Score: -51.75 |\n",
"| Experiment: 2 | Episode: 1984 | Score: 34.92 | Avg Score: -51.03 |\n",
"| Experiment: 2 | Episode: 1985 | Score: -70.92 | Avg Score: -51.2 |\n",
"| Experiment: 2 | Episode: 1986 | Score: -42.27 | Avg Score: -49.97 |\n",
"| Experiment: 2 | Episode: 1987 | Score: 137.52 | Avg Score: -46.15 |\n",
"| Experiment: 2 | Episode: 1988 | Score: -89.14 | Avg Score: -46.92 |\n",
"| Experiment: 2 | Episode: 1989 | Score: 12.9 | Avg Score: -46.2 |\n",
"| Experiment: 2 | Episode: 1990 | Score: 2.1 | Avg Score: -45.59 |\n",
"| Experiment: 2 | Episode: 1991 | Score: -17.31 | Avg Score: -45.23 |\n",
"| Experiment: 2 | Episode: 1992 | Score: -302.05 | Avg Score: -48.24 |\n",
"| Experiment: 2 | Episode: 1993 | Score: -11.7 | Avg Score: -48.66 |\n",
"| Experiment: 2 | Episode: 1994 | Score: 18.24 | Avg Score: -48.41 |\n",
"| Experiment: 2 | Episode: 1995 | Score: 16.49 | Avg Score: -47.75 |\n",
"| Experiment: 2 | Episode: 1996 | Score: -89.03 | Avg Score: -48.33 |\n",
"| Experiment: 2 | Episode: 1997 | Score: -40.61 | Avg Score: -48.37 |\n",
"| Experiment: 2 | Episode: 1998 | Score: 37.7 | Avg Score: -47.57 |\n",
"| Experiment: 2 | Episode: 1999 | Score: -94.1 | Avg Score: -48.64 |\n",
"| Experiment: 3 | Episode: 0 | Score: -104.02 | Avg Score: -104.02 |\n",
"| Experiment: 3 | Episode: 1 | Score: -151.51 | Avg Score: -127.77 |\n",
"| Experiment: 3 | Episode: 2 | Score: -99.91 | Avg Score: -118.48 |\n",
"| Experiment: 3 | Episode: 3 | Score: -172.67 | Avg Score: -132.03 |\n",
"| Experiment: 3 | Episode: 4 | Score: -151.69 | Avg Score: -135.96 |\n",
"| Experiment: 3 | Episode: 5 | Score: -128.88 | Avg Score: -134.78 |\n",
"| Experiment: 3 | Episode: 6 | Score: -91.17 | Avg Score: -128.55 |\n",
"| Experiment: 3 | Episode: 7 | Score: -435.75 | Avg Score: -166.95 |\n",
"| Experiment: 3 | Episode: 8 | Score: -409.35 | Avg Score: -193.88 |\n",
"| Experiment: 3 | Episode: 9 | Score: -467.52 | Avg Score: -221.25 |\n",
"| Experiment: 3 | Episode: 10 | Score: -60.38 | Avg Score: -206.62 |\n",
"| Experiment: 3 | Episode: 11 | Score: -379.84 | Avg Score: -221.06 |\n",
"| Experiment: 3 | Episode: 12 | Score: -118.78 | Avg Score: -213.19 |\n",
"| Experiment: 3 | Episode: 13 | Score: -130.37 | Avg Score: -207.27 |\n",
"| Experiment: 3 | Episode: 14 | Score: -64.62 | Avg Score: -197.76 |\n",
"| Experiment: 3 | Episode: 15 | Score: -149.72 | Avg Score: -194.76 |\n",
"| Experiment: 3 | Episode: 16 | Score: -266.16 | Avg Score: -198.96 |\n",
"| Experiment: 3 | Episode: 17 | Score: -371.26 | Avg Score: -208.53 |\n",
"| Experiment: 3 | Episode: 18 | Score: -189.54 | Avg Score: -207.53 |\n",
"| Experiment: 3 | Episode: 19 | Score: -466.5 | Avg Score: -220.48 |\n",
"| Experiment: 3 | Episode: 20 | Score: -367.92 | Avg Score: -227.5 |\n",
"| Experiment: 3 | Episode: 21 | Score: -165.44 | Avg Score: -224.68 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 3 | Episode: 22 | Score: -326.83 | Avg Score: -229.12 |\n",
"| Experiment: 3 | Episode: 23 | Score: -236.75 | Avg Score: -229.44 |\n",
"| Experiment: 3 | Episode: 24 | Score: -434.7 | Avg Score: -237.65 |\n",
"| Experiment: 3 | Episode: 25 | Score: -60.8 | Avg Score: -230.85 |\n",
"| Experiment: 3 | Episode: 26 | Score: -232.0 | Avg Score: -230.89 |\n",
"| Experiment: 3 | Episode: 27 | Score: -77.92 | Avg Score: -225.43 |\n",
"| Experiment: 3 | Episode: 28 | Score: -207.78 | Avg Score: -224.82 |\n",
"| Experiment: 3 | Episode: 29 | Score: -325.12 | Avg Score: -228.16 |\n",
"| Experiment: 3 | Episode: 30 | Score: -375.5 | Avg Score: -232.92 |\n",
"| Experiment: 3 | Episode: 31 | Score: -282.29 | Avg Score: -234.46 |\n",
"| Experiment: 3 | Episode: 32 | Score: -55.49 | Avg Score: -229.04 |\n",
"| Experiment: 3 | Episode: 33 | Score: -37.44 | Avg Score: -223.4 |\n",
"| Experiment: 3 | Episode: 34 | Score: -193.56 | Avg Score: -222.55 |\n",
"| Experiment: 3 | Episode: 35 | Score: -308.7 | Avg Score: -224.94 |\n",
"| Experiment: 3 | Episode: 36 | Score: -313.93 | Avg Score: -227.35 |\n",
"| Experiment: 3 | Episode: 37 | Score: -58.5 | Avg Score: -222.9 |\n",
"| Experiment: 3 | Episode: 38 | Score: -300.18 | Avg Score: -224.88 |\n",
"| Experiment: 3 | Episode: 39 | Score: -397.45 | Avg Score: -229.2 |\n",
"| Experiment: 3 | Episode: 40 | Score: -119.99 | Avg Score: -226.54 |\n",
"| Experiment: 3 | Episode: 41 | Score: -290.51 | Avg Score: -228.06 |\n",
"| Experiment: 3 | Episode: 42 | Score: -119.13 | Avg Score: -225.52 |\n",
"| Experiment: 3 | Episode: 43 | Score: -307.28 | Avg Score: -227.38 |\n",
"| Experiment: 3 | Episode: 44 | Score: -92.51 | Avg Score: -224.39 |\n",
"| Experiment: 3 | Episode: 45 | Score: -257.73 | Avg Score: -225.11 |\n",
"| Experiment: 3 | Episode: 46 | Score: -110.34 | Avg Score: -222.67 |\n",
"| Experiment: 3 | Episode: 47 | Score: -230.07 | Avg Score: -222.82 |\n",
"| Experiment: 3 | Episode: 48 | Score: -65.48 | Avg Score: -219.61 |\n",
"| Experiment: 3 | Episode: 49 | Score: -35.74 | Avg Score: -215.93 |\n",
"| Experiment: 3 | Episode: 50 | Score: -211.66 | Avg Score: -215.85 |\n",
"| Experiment: 3 | Episode: 51 | Score: -152.4 | Avg Score: -214.63 |\n",
"| Experiment: 3 | Episode: 52 | Score: -324.42 | Avg Score: -216.7 |\n",
"| Experiment: 3 | Episode: 53 | Score: -174.1 | Avg Score: -215.91 |\n",
"| Experiment: 3 | Episode: 54 | Score: -340.93 | Avg Score: -218.19 |\n",
"| Experiment: 3 | Episode: 55 | Score: -244.01 | Avg Score: -218.65 |\n",
"| Experiment: 3 | Episode: 56 | Score: -325.05 | Avg Score: -220.51 |\n",
"| Experiment: 3 | Episode: 57 | Score: -204.08 | Avg Score: -220.23 |\n",
"| Experiment: 3 | Episode: 58 | Score: -258.08 | Avg Score: -220.87 |\n",
"| Experiment: 3 | Episode: 59 | Score: -253.66 | Avg Score: -221.42 |\n",
"| Experiment: 3 | Episode: 60 | Score: -293.4 | Avg Score: -222.6 |\n",
"| Experiment: 3 | Episode: 61 | Score: -237.39 | Avg Score: -222.84 |\n",
"| Experiment: 3 | Episode: 62 | Score: -125.21 | Avg Score: -221.29 |\n",
"| Experiment: 3 | Episode: 63 | Score: -272.51 | Avg Score: -222.09 |\n",
"| Experiment: 3 | Episode: 64 | Score: -168.18 | Avg Score: -221.26 |\n",
"| Experiment: 3 | Episode: 65 | Score: -85.68 | Avg Score: -219.2 |\n",
"| Experiment: 3 | Episode: 66 | Score: -212.34 | Avg Score: -219.1 |\n",
"| Experiment: 3 | Episode: 67 | Score: -41.72 | Avg Score: -216.49 |\n",
"| Experiment: 3 | Episode: 68 | Score: -373.9 | Avg Score: -218.77 |\n",
"| Experiment: 3 | Episode: 69 | Score: -509.61 | Avg Score: -222.93 |\n",
"| Experiment: 3 | Episode: 70 | Score: -227.34 | Avg Score: -222.99 |\n",
"| Experiment: 3 | Episode: 71 | Score: -208.68 | Avg Score: -222.79 |\n",
"| Experiment: 3 | Episode: 72 | Score: -283.35 | Avg Score: -223.62 |\n",
"| Experiment: 3 | Episode: 73 | Score: -33.23 | Avg Score: -221.05 |\n",
"| Experiment: 3 | Episode: 74 | Score: -116.97 | Avg Score: -219.66 |\n",
"| Experiment: 3 | Episode: 75 | Score: -139.2 | Avg Score: -218.6 |\n",
"| Experiment: 3 | Episode: 76 | Score: -87.03 | Avg Score: -216.89 |\n",
"| Experiment: 3 | Episode: 77 | Score: -35.46 | Avg Score: -214.57 |\n",
"| Experiment: 3 | Episode: 78 | Score: -357.61 | Avg Score: -216.38 |\n",
"| Experiment: 3 | Episode: 79 | Score: -295.72 | Avg Score: -217.37 |\n",
"| Experiment: 3 | Episode: 80 | Score: -391.86 | Avg Score: -219.52 |\n",
"| Experiment: 3 | Episode: 81 | Score: -95.26 | Avg Score: -218.01 |\n",
"| Experiment: 3 | Episode: 82 | Score: -203.45 | Avg Score: -217.83 |\n",
"| Experiment: 3 | Episode: 83 | Score: -136.65 | Avg Score: -216.87 |\n",
"| Experiment: 3 | Episode: 84 | Score: -256.86 | Avg Score: -217.34 |\n",
"| Experiment: 3 | Episode: 85 | Score: -69.76 | Avg Score: -215.62 |\n",
"| Experiment: 3 | Episode: 86 | Score: -457.78 | Avg Score: -218.41 |\n",
"| Experiment: 3 | Episode: 87 | Score: -134.66 | Avg Score: -217.45 |\n",
"| Experiment: 3 | Episode: 88 | Score: -273.39 | Avg Score: -218.08 |\n",
"| Experiment: 3 | Episode: 89 | Score: -89.08 | Avg Score: -216.65 |\n",
"| Experiment: 3 | Episode: 90 | Score: -56.89 | Avg Score: -214.89 |\n",
"| Experiment: 3 | Episode: 91 | Score: -337.39 | Avg Score: -216.22 |\n",
"| Experiment: 3 | Episode: 92 | Score: -312.18 | Avg Score: -217.26 |\n",
"| Experiment: 3 | Episode: 93 | Score: -126.51 | Avg Score: -216.29 |\n",
"| Experiment: 3 | Episode: 94 | Score: -369.69 | Avg Score: -217.91 |\n",
"| Experiment: 3 | Episode: 95 | Score: -216.47 | Avg Score: -217.89 |\n",
"| Experiment: 3 | Episode: 96 | Score: -55.87 | Avg Score: -216.22 |\n",
"| Experiment: 3 | Episode: 97 | Score: -266.78 | Avg Score: -216.74 |\n",
"| Experiment: 3 | Episode: 98 | Score: -87.53 | Avg Score: -215.43 |\n",
"| Experiment: 3 | Episode: 99 | Score: -144.09 | Avg Score: -214.72 |\n",
"| Experiment: 3 | Episode: 100 | Score: -190.06 | Avg Score: -215.58 |\n",
"| Experiment: 3 | Episode: 101 | Score: -178.99 | Avg Score: -215.85 |\n",
"| Experiment: 3 | Episode: 102 | Score: -275.31 | Avg Score: -217.61 |\n",
"| Experiment: 3 | Episode: 103 | Score: -390.15 | Avg Score: -219.78 |\n",
"| Experiment: 3 | Episode: 104 | Score: -91.58 | Avg Score: -219.18 |\n",
"| Experiment: 3 | Episode: 105 | Score: -397.99 | Avg Score: -221.87 |\n",
"| Experiment: 3 | Episode: 106 | Score: -314.19 | Avg Score: -224.1 |\n",
"| Experiment: 3 | Episode: 107 | Score: -168.47 | Avg Score: -221.43 |\n",
"| Experiment: 3 | Episode: 108 | Score: -400.11 | Avg Score: -221.34 |\n",
"| Experiment: 3 | Episode: 109 | Score: -341.49 | Avg Score: -220.08 |\n",
"| Experiment: 3 | Episode: 110 | Score: -450.55 | Avg Score: -223.98 |\n",
"| Experiment: 3 | Episode: 111 | Score: -93.94 | Avg Score: -221.12 |\n",
"| Experiment: 3 | Episode: 112 | Score: -260.86 | Avg Score: -222.54 |\n",
"| Experiment: 3 | Episode: 113 | Score: -318.24 | Avg Score: -224.42 |\n",
"| Experiment: 3 | Episode: 114 | Score: -156.83 | Avg Score: -225.34 |\n",
"| Experiment: 3 | Episode: 115 | Score: -475.06 | Avg Score: -228.59 |\n",
"| Experiment: 3 | Episode: 116 | Score: -376.88 | Avg Score: -229.7 |\n",
"| Experiment: 3 | Episode: 117 | Score: -312.21 | Avg Score: -229.11 |\n",
"| Experiment: 3 | Episode: 118 | Score: -109.43 | Avg Score: -228.31 |\n",
"| Experiment: 3 | Episode: 119 | Score: -403.11 | Avg Score: -227.68 |\n",
"| Experiment: 3 | Episode: 120 | Score: -386.54 | Avg Score: -227.86 |\n",
"| Experiment: 3 | Episode: 121 | Score: -364.31 | Avg Score: -229.85 |\n",
"| Experiment: 3 | Episode: 122 | Score: -493.87 | Avg Score: -231.52 |\n",
"| Experiment: 3 | Episode: 123 | Score: -397.46 | Avg Score: -233.13 |\n",
"| Experiment: 3 | Episode: 124 | Score: -93.72 | Avg Score: -229.72 |\n",
"| Experiment: 3 | Episode: 125 | Score: -248.65 | Avg Score: -231.6 |\n",
"| Experiment: 3 | Episode: 126 | Score: -393.55 | Avg Score: -233.21 |\n",
"| Experiment: 3 | Episode: 127 | Score: -245.91 | Avg Score: -234.89 |\n",
"| Experiment: 3 | Episode: 128 | Score: -409.71 | Avg Score: -236.91 |\n",
"| Experiment: 3 | Episode: 129 | Score: -141.36 | Avg Score: -235.07 |\n",
"| Experiment: 3 | Episode: 130 | Score: -29.52 | Avg Score: -231.61 |\n",
"| Experiment: 3 | Episode: 131 | Score: -213.73 | Avg Score: -230.93 |\n",
"| Experiment: 3 | Episode: 132 | Score: -164.53 | Avg Score: -232.02 |\n",
"| Experiment: 3 | Episode: 133 | Score: -176.64 | Avg Score: -233.41 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 3 | Episode: 134 | Score: -243.49 | Avg Score: -233.91 |\n",
"| Experiment: 3 | Episode: 135 | Score: -395.93 | Avg Score: -234.78 |\n",
"| Experiment: 3 | Episode: 136 | Score: -160.74 | Avg Score: -233.25 |\n",
"| Experiment: 3 | Episode: 137 | Score: -298.78 | Avg Score: -235.65 |\n",
"| Experiment: 3 | Episode: 138 | Score: -79.06 | Avg Score: -233.44 |\n",
"| Experiment: 3 | Episode: 139 | Score: -398.92 | Avg Score: -233.46 |\n",
"| Experiment: 3 | Episode: 140 | Score: -156.54 | Avg Score: -233.82 |\n",
"| Experiment: 3 | Episode: 141 | Score: -226.8 | Avg Score: -233.19 |\n",
"| Experiment: 3 | Episode: 142 | Score: -74.99 | Avg Score: -232.74 |\n",
"| Experiment: 3 | Episode: 143 | Score: -498.4 | Avg Score: -234.66 |\n",
"| Experiment: 3 | Episode: 144 | Score: -233.61 | Avg Score: -236.07 |\n",
"| Experiment: 3 | Episode: 145 | Score: -112.51 | Avg Score: -234.61 |\n",
"| Experiment: 3 | Episode: 146 | Score: -88.64 | Avg Score: -234.4 |\n",
"| Experiment: 3 | Episode: 147 | Score: -78.38 | Avg Score: -232.88 |\n",
"| Experiment: 3 | Episode: 148 | Score: -282.11 | Avg Score: -235.05 |\n",
"| Experiment: 3 | Episode: 149 | Score: -41.09 | Avg Score: -235.1 |\n",
"| Experiment: 3 | Episode: 150 | Score: -321.78 | Avg Score: -236.2 |\n",
"| Experiment: 3 | Episode: 151 | Score: -243.07 | Avg Score: -237.11 |\n",
"| Experiment: 3 | Episode: 152 | Score: -379.98 | Avg Score: -237.66 |\n",
"| Experiment: 3 | Episode: 153 | Score: -58.6 | Avg Score: -236.51 |\n",
"| Experiment: 3 | Episode: 154 | Score: -186.65 | Avg Score: -234.97 |\n",
"| Experiment: 3 | Episode: 155 | Score: -278.63 | Avg Score: -235.31 |\n",
"| Experiment: 3 | Episode: 156 | Score: -80.99 | Avg Score: -232.87 |\n",
"| Experiment: 3 | Episode: 157 | Score: -111.97 | Avg Score: -231.95 |\n",
"| Experiment: 3 | Episode: 158 | Score: -8.72 | Avg Score: -229.46 |\n",
"| Experiment: 3 | Episode: 159 | Score: -164.37 | Avg Score: -228.56 |\n",
"| Experiment: 3 | Episode: 160 | Score: -192.2 | Avg Score: -227.55 |\n",
"| Experiment: 3 | Episode: 161 | Score: -156.97 | Avg Score: -226.75 |\n",
"| Experiment: 3 | Episode: 162 | Score: -48.99 | Avg Score: -225.99 |\n",
"| Experiment: 3 | Episode: 163 | Score: -405.65 | Avg Score: -227.32 |\n",
"| Experiment: 3 | Episode: 164 | Score: -250.53 | Avg Score: -228.14 |\n",
"| Experiment: 3 | Episode: 165 | Score: -151.09 | Avg Score: -228.79 |\n",
"| Experiment: 3 | Episode: 166 | Score: -246.09 | Avg Score: -229.13 |\n",
"| Experiment: 3 | Episode: 167 | Score: -236.66 | Avg Score: -231.08 |\n",
"| Experiment: 3 | Episode: 168 | Score: -54.55 | Avg Score: -227.89 |\n",
"| Experiment: 3 | Episode: 169 | Score: -139.74 | Avg Score: -224.19 |\n",
"| Experiment: 3 | Episode: 170 | Score: -254.97 | Avg Score: -224.47 |\n",
"| Experiment: 3 | Episode: 171 | Score: -65.98 | Avg Score: -223.04 |\n",
"| Experiment: 3 | Episode: 172 | Score: -175.31 | Avg Score: -221.96 |\n",
"| Experiment: 3 | Episode: 173 | Score: -135.37 | Avg Score: -222.98 |\n",
"| Experiment: 3 | Episode: 174 | Score: -67.53 | Avg Score: -222.49 |\n",
"| Experiment: 3 | Episode: 175 | Score: -412.44 | Avg Score: -225.22 |\n",
"| Experiment: 3 | Episode: 176 | Score: -285.34 | Avg Score: -227.2 |\n",
"| Experiment: 3 | Episode: 177 | Score: -350.13 | Avg Score: -230.35 |\n",
"| Experiment: 3 | Episode: 178 | Score: -354.38 | Avg Score: -230.32 |\n",
"| Experiment: 3 | Episode: 179 | Score: -264.26 | Avg Score: -230.0 |\n",
"| Experiment: 3 | Episode: 180 | Score: -207.39 | Avg Score: -228.16 |\n",
"| Experiment: 3 | Episode: 181 | Score: -563.42 | Avg Score: -232.84 |\n",
"| Experiment: 3 | Episode: 182 | Score: -8.67 | Avg Score: -230.89 |\n",
"| Experiment: 3 | Episode: 183 | Score: -241.56 | Avg Score: -231.94 |\n",
"| Experiment: 3 | Episode: 184 | Score: -32.05 | Avg Score: -229.69 |\n",
"| Experiment: 3 | Episode: 185 | Score: -206.84 | Avg Score: -231.06 |\n",
"| Experiment: 3 | Episode: 186 | Score: -279.23 | Avg Score: -229.28 |\n",
"| Experiment: 3 | Episode: 187 | Score: -157.39 | Avg Score: -229.5 |\n",
"| Experiment: 3 | Episode: 188 | Score: -259.2 | Avg Score: -229.36 |\n",
"| Experiment: 3 | Episode: 189 | Score: -112.11 | Avg Score: -229.59 |\n",
"| Experiment: 3 | Episode: 190 | Score: -78.74 | Avg Score: -229.81 |\n",
"| Experiment: 3 | Episode: 191 | Score: -278.67 | Avg Score: -229.22 |\n",
"| Experiment: 3 | Episode: 192 | Score: -13.13 | Avg Score: -226.23 |\n",
"| Experiment: 3 | Episode: 193 | Score: -107.44 | Avg Score: -226.04 |\n",
"| Experiment: 3 | Episode: 194 | Score: -103.57 | Avg Score: -223.38 |\n",
"| Experiment: 3 | Episode: 195 | Score: -55.93 | Avg Score: -221.78 |\n",
"| Experiment: 3 | Episode: 196 | Score: -257.5 | Avg Score: -223.79 |\n",
"| Experiment: 3 | Episode: 197 | Score: -147.93 | Avg Score: -222.6 |\n",
"| Experiment: 3 | Episode: 198 | Score: -421.83 | Avg Score: -225.95 |\n",
"| Experiment: 3 | Episode: 199 | Score: -85.88 | Avg Score: -225.36 |\n",
"| Experiment: 3 | Episode: 200 | Score: -51.24 | Avg Score: -223.98 |\n",
"| Experiment: 3 | Episode: 201 | Score: -40.04 | Avg Score: -222.59 |\n",
"| Experiment: 3 | Episode: 202 | Score: -71.69 | Avg Score: -220.55 |\n",
"| Experiment: 3 | Episode: 203 | Score: -92.74 | Avg Score: -217.58 |\n",
"| Experiment: 3 | Episode: 204 | Score: -80.13 | Avg Score: -217.46 |\n",
"| Experiment: 3 | Episode: 205 | Score: -328.7 | Avg Score: -216.77 |\n",
"| Experiment: 3 | Episode: 206 | Score: -245.3 | Avg Score: -216.08 |\n",
"| Experiment: 3 | Episode: 207 | Score: -302.72 | Avg Score: -217.42 |\n",
"| Experiment: 3 | Episode: 208 | Score: -155.69 | Avg Score: -214.98 |\n",
"| Experiment: 3 | Episode: 209 | Score: -162.79 | Avg Score: -213.19 |\n",
"| Experiment: 3 | Episode: 210 | Score: -151.16 | Avg Score: -210.2 |\n",
"| Experiment: 3 | Episode: 211 | Score: -94.07 | Avg Score: -210.2 |\n",
"| Experiment: 3 | Episode: 212 | Score: -212.99 | Avg Score: -209.72 |\n",
"| Experiment: 3 | Episode: 213 | Score: -163.58 | Avg Score: -208.17 |\n",
"| Experiment: 3 | Episode: 214 | Score: -426.63 | Avg Score: -210.87 |\n",
"| Experiment: 3 | Episode: 215 | Score: -275.41 | Avg Score: -208.87 |\n",
"| Experiment: 3 | Episode: 216 | Score: -284.41 | Avg Score: -207.95 |\n",
"| Experiment: 3 | Episode: 217 | Score: -107.68 | Avg Score: -205.9 |\n",
"| Experiment: 3 | Episode: 218 | Score: -174.85 | Avg Score: -206.56 |\n",
"| Experiment: 3 | Episode: 219 | Score: 6.55 | Avg Score: -202.46 |\n",
"| Experiment: 3 | Episode: 220 | Score: -161.07 | Avg Score: -200.21 |\n",
"| Experiment: 3 | Episode: 221 | Score: -126.06 | Avg Score: -197.82 |\n",
"| Experiment: 3 | Episode: 222 | Score: -120.81 | Avg Score: -194.09 |\n",
"| Experiment: 3 | Episode: 223 | Score: -83.59 | Avg Score: -190.96 |\n",
"| Experiment: 3 | Episode: 224 | Score: -190.19 | Avg Score: -191.92 |\n",
"| Experiment: 3 | Episode: 225 | Score: -72.45 | Avg Score: -190.16 |\n",
"| Experiment: 3 | Episode: 226 | Score: -142.27 | Avg Score: -187.65 |\n",
"| Experiment: 3 | Episode: 227 | Score: -401.04 | Avg Score: -189.2 |\n",
"| Experiment: 3 | Episode: 228 | Score: -196.81 | Avg Score: -187.07 |\n",
"| Experiment: 3 | Episode: 229 | Score: -104.55 | Avg Score: -186.7 |\n",
"| Experiment: 3 | Episode: 230 | Score: -39.49 | Avg Score: -186.8 |\n",
"| Experiment: 3 | Episode: 231 | Score: -259.39 | Avg Score: -187.26 |\n",
"| Experiment: 3 | Episode: 232 | Score: -230.31 | Avg Score: -187.91 |\n",
"| Experiment: 3 | Episode: 233 | Score: -68.35 | Avg Score: -186.83 |\n",
"| Experiment: 3 | Episode: 234 | Score: -94.5 | Avg Score: -185.34 |\n",
"| Experiment: 3 | Episode: 235 | Score: -238.27 | Avg Score: -183.76 |\n",
"| Experiment: 3 | Episode: 236 | Score: -184.3 | Avg Score: -184.0 |\n",
"| Experiment: 3 | Episode: 237 | Score: -153.2 | Avg Score: -182.54 |\n",
"| Experiment: 3 | Episode: 238 | Score: -96.07 | Avg Score: -182.71 |\n",
"| Experiment: 3 | Episode: 239 | Score: -108.92 | Avg Score: -179.81 |\n",
"| Experiment: 3 | Episode: 240 | Score: -250.8 | Avg Score: -180.76 |\n",
"| Experiment: 3 | Episode: 241 | Score: -19.57 | Avg Score: -178.68 |\n",
"| Experiment: 3 | Episode: 242 | Score: -280.0 | Avg Score: -180.73 |\n",
"| Experiment: 3 | Episode: 243 | Score: -135.45 | Avg Score: -177.11 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 3 | Episode: 244 | Score: -157.24 | Avg Score: -176.34 |\n",
"| Experiment: 3 | Episode: 245 | Score: -85.17 | Avg Score: -176.07 |\n",
"| Experiment: 3 | Episode: 246 | Score: -212.11 | Avg Score: -177.3 |\n",
"| Experiment: 3 | Episode: 247 | Score: -239.51 | Avg Score: -178.91 |\n",
"| Experiment: 3 | Episode: 248 | Score: -518.34 | Avg Score: -181.28 |\n",
"| Experiment: 3 | Episode: 249 | Score: -82.38 | Avg Score: -181.69 |\n",
"| Experiment: 3 | Episode: 250 | Score: -92.92 | Avg Score: -179.4 |\n",
"| Experiment: 3 | Episode: 251 | Score: -187.07 | Avg Score: -178.84 |\n",
"| Experiment: 3 | Episode: 252 | Score: -77.43 | Avg Score: -175.82 |\n",
"| Experiment: 3 | Episode: 253 | Score: -392.64 | Avg Score: -179.16 |\n",
"| Experiment: 3 | Episode: 254 | Score: -88.19 | Avg Score: -178.17 |\n",
"| Experiment: 3 | Episode: 255 | Score: -159.84 | Avg Score: -176.98 |\n",
"| Experiment: 3 | Episode: 256 | Score: -147.45 | Avg Score: -177.65 |\n",
"| Experiment: 3 | Episode: 257 | Score: -226.09 | Avg Score: -178.79 |\n",
"| Experiment: 3 | Episode: 258 | Score: -112.9 | Avg Score: -179.83 |\n",
"| Experiment: 3 | Episode: 259 | Score: -116.6 | Avg Score: -179.35 |\n",
"| Experiment: 3 | Episode: 260 | Score: -96.75 | Avg Score: -178.4 |\n",
"| Experiment: 3 | Episode: 261 | Score: -178.47 | Avg Score: -178.61 |\n",
"| Experiment: 3 | Episode: 262 | Score: -212.58 | Avg Score: -180.25 |\n",
"| Experiment: 3 | Episode: 263 | Score: -96.77 | Avg Score: -177.16 |\n",
"| Experiment: 3 | Episode: 264 | Score: -87.13 | Avg Score: -175.53 |\n",
"| Experiment: 3 | Episode: 265 | Score: -64.73 | Avg Score: -174.66 |\n",
"| Experiment: 3 | Episode: 266 | Score: -241.37 | Avg Score: -174.62 |\n",
"| Experiment: 3 | Episode: 267 | Score: -206.79 | Avg Score: -174.32 |\n",
"| Experiment: 3 | Episode: 268 | Score: -33.66 | Avg Score: -174.11 |\n",
"| Experiment: 3 | Episode: 269 | Score: -78.3 | Avg Score: -173.49 |\n",
"| Experiment: 3 | Episode: 270 | Score: -92.56 | Avg Score: -171.87 |\n",
"| Experiment: 3 | Episode: 271 | Score: -157.21 | Avg Score: -172.78 |\n",
"| Experiment: 3 | Episode: 272 | Score: -209.82 | Avg Score: -173.13 |\n",
"| Experiment: 3 | Episode: 273 | Score: -150.42 | Avg Score: -173.28 |\n",
"| Experiment: 3 | Episode: 274 | Score: -147.42 | Avg Score: -174.08 |\n",
"| Experiment: 3 | Episode: 275 | Score: -72.99 | Avg Score: -170.68 |\n",
"| Experiment: 3 | Episode: 276 | Score: -70.36 | Avg Score: -168.53 |\n",
"| Experiment: 3 | Episode: 277 | Score: -89.34 | Avg Score: -165.92 |\n",
"| Experiment: 3 | Episode: 278 | Score: -125.05 | Avg Score: -163.63 |\n",
"| Experiment: 3 | Episode: 279 | Score: -122.92 | Avg Score: -162.22 |\n",
"| Experiment: 3 | Episode: 280 | Score: -373.24 | Avg Score: -163.88 |\n",
"| Experiment: 3 | Episode: 281 | Score: -74.98 | Avg Score: -158.99 |\n",
"| Experiment: 3 | Episode: 282 | Score: -42.05 | Avg Score: -159.33 |\n",
"| Experiment: 3 | Episode: 283 | Score: -144.25 | Avg Score: -158.35 |\n",
"| Experiment: 3 | Episode: 284 | Score: -76.39 | Avg Score: -158.8 |\n",
"| Experiment: 3 | Episode: 285 | Score: -152.59 | Avg Score: -158.25 |\n",
"| Experiment: 3 | Episode: 286 | Score: -73.1 | Avg Score: -156.19 |\n",
"| Experiment: 3 | Episode: 287 | Score: -41.82 | Avg Score: -155.04 |\n",
"| Experiment: 3 | Episode: 288 | Score: -236.2 | Avg Score: -154.81 |\n",
"| Experiment: 3 | Episode: 289 | Score: -42.74 | Avg Score: -154.11 |\n",
"| Experiment: 3 | Episode: 290 | Score: -128.36 | Avg Score: -154.61 |\n",
"| Experiment: 3 | Episode: 291 | Score: -132.82 | Avg Score: -153.15 |\n",
"| Experiment: 3 | Episode: 292 | Score: -300.12 | Avg Score: -156.02 |\n",
"| Experiment: 3 | Episode: 293 | Score: -102.14 | Avg Score: -155.97 |\n",
"| Experiment: 3 | Episode: 294 | Score: -80.41 | Avg Score: -155.74 |\n",
"| Experiment: 3 | Episode: 295 | Score: -55.21 | Avg Score: -155.73 |\n",
"| Experiment: 3 | Episode: 296 | Score: -99.46 | Avg Score: -154.15 |\n",
"| Experiment: 3 | Episode: 297 | Score: -241.01 | Avg Score: -155.08 |\n",
"| Experiment: 3 | Episode: 298 | Score: -176.04 | Avg Score: -152.62 |\n",
"| Experiment: 3 | Episode: 299 | Score: -115.13 | Avg Score: -152.91 |\n",
"| Experiment: 3 | Episode: 300 | Score: -237.8 | Avg Score: -154.78 |\n",
"| Experiment: 3 | Episode: 301 | Score: -84.85 | Avg Score: -155.23 |\n",
"| Experiment: 3 | Episode: 302 | Score: -273.33 | Avg Score: -157.24 |\n",
"| Experiment: 3 | Episode: 303 | Score: -134.57 | Avg Score: -157.66 |\n",
"| Experiment: 3 | Episode: 304 | Score: -56.35 | Avg Score: -157.42 |\n",
"| Experiment: 3 | Episode: 305 | Score: -173.74 | Avg Score: -155.87 |\n",
"| Experiment: 3 | Episode: 306 | Score: -120.44 | Avg Score: -154.63 |\n",
"| Experiment: 3 | Episode: 307 | Score: -163.65 | Avg Score: -153.23 |\n",
"| Experiment: 3 | Episode: 308 | Score: -390.53 | Avg Score: -155.58 |\n",
"| Experiment: 3 | Episode: 309 | Score: -94.59 | Avg Score: -154.9 |\n",
"| Experiment: 3 | Episode: 310 | Score: -352.89 | Avg Score: -156.92 |\n",
"| Experiment: 3 | Episode: 311 | Score: -144.39 | Avg Score: -157.42 |\n",
"| Experiment: 3 | Episode: 312 | Score: -99.9 | Avg Score: -156.29 |\n",
"| Experiment: 3 | Episode: 313 | Score: -177.96 | Avg Score: -156.43 |\n",
"| Experiment: 3 | Episode: 314 | Score: -105.2 | Avg Score: -153.22 |\n",
"| Experiment: 3 | Episode: 315 | Score: -97.96 | Avg Score: -151.45 |\n",
"| Experiment: 3 | Episode: 316 | Score: -159.14 | Avg Score: -150.19 |\n",
"| Experiment: 3 | Episode: 317 | Score: -103.29 | Avg Score: -150.15 |\n",
"| Experiment: 3 | Episode: 318 | Score: -41.88 | Avg Score: -148.82 |\n",
"| Experiment: 3 | Episode: 319 | Score: -101.8 | Avg Score: -149.9 |\n",
"| Experiment: 3 | Episode: 320 | Score: -124.83 | Avg Score: -149.54 |\n",
"| Experiment: 3 | Episode: 321 | Score: -125.39 | Avg Score: -149.53 |\n",
"| Experiment: 3 | Episode: 322 | Score: -182.52 | Avg Score: -150.15 |\n",
"| Experiment: 3 | Episode: 323 | Score: -291.39 | Avg Score: -152.23 |\n",
"| Experiment: 3 | Episode: 324 | Score: -82.38 | Avg Score: -151.15 |\n",
"| Experiment: 3 | Episode: 325 | Score: -157.45 | Avg Score: -152.0 |\n",
"| Experiment: 3 | Episode: 326 | Score: -95.55 | Avg Score: -151.53 |\n",
"| Experiment: 3 | Episode: 327 | Score: -111.06 | Avg Score: -148.63 |\n",
"| Experiment: 3 | Episode: 328 | Score: -77.95 | Avg Score: -147.45 |\n",
"| Experiment: 3 | Episode: 329 | Score: -152.37 | Avg Score: -147.92 |\n",
"| Experiment: 3 | Episode: 330 | Score: -90.69 | Avg Score: -148.44 |\n",
"| Experiment: 3 | Episode: 331 | Score: -91.43 | Avg Score: -146.76 |\n",
"| Experiment: 3 | Episode: 332 | Score: -145.36 | Avg Score: -145.91 |\n",
"| Experiment: 3 | Episode: 333 | Score: -45.34 | Avg Score: -145.68 |\n",
"| Experiment: 3 | Episode: 334 | Score: -76.58 | Avg Score: -145.5 |\n",
"| Experiment: 3 | Episode: 335 | Score: -79.47 | Avg Score: -143.91 |\n",
"| Experiment: 3 | Episode: 336 | Score: -112.28 | Avg Score: -143.19 |\n",
"| Experiment: 3 | Episode: 337 | Score: -61.55 | Avg Score: -142.27 |\n",
"| Experiment: 3 | Episode: 338 | Score: -75.9 | Avg Score: -142.07 |\n",
"| Experiment: 3 | Episode: 339 | Score: -55.86 | Avg Score: -141.54 |\n",
"| Experiment: 3 | Episode: 340 | Score: -80.18 | Avg Score: -139.83 |\n",
"| Experiment: 3 | Episode: 341 | Score: -272.49 | Avg Score: -142.36 |\n",
"| Experiment: 3 | Episode: 342 | Score: -113.85 | Avg Score: -140.7 |\n",
"| Experiment: 3 | Episode: 343 | Score: -55.91 | Avg Score: -139.91 |\n",
"| Experiment: 3 | Episode: 344 | Score: -112.74 | Avg Score: -139.46 |\n",
"| Experiment: 3 | Episode: 345 | Score: -131.15 | Avg Score: -139.92 |\n",
"| Experiment: 3 | Episode: 346 | Score: -251.61 | Avg Score: -140.32 |\n",
"| Experiment: 3 | Episode: 347 | Score: -80.86 | Avg Score: -138.73 |\n",
"| Experiment: 3 | Episode: 348 | Score: -55.46 | Avg Score: -134.1 |\n",
"| Experiment: 3 | Episode: 349 | Score: -237.55 | Avg Score: -135.65 |\n",
"| Experiment: 3 | Episode: 350 | Score: 9.54 | Avg Score: -134.63 |\n",
"| Experiment: 3 | Episode: 351 | Score: -117.94 | Avg Score: -133.94 |\n",
"| Experiment: 3 | Episode: 352 | Score: -287.41 | Avg Score: -136.04 |\n",
"| Experiment: 3 | Episode: 353 | Score: -131.94 | Avg Score: -133.43 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 3 | Episode: 354 | Score: -125.36 | Avg Score: -133.8 |\n",
"| Experiment: 3 | Episode: 355 | Score: -265.09 | Avg Score: -134.85 |\n",
"| Experiment: 3 | Episode: 356 | Score: -82.12 | Avg Score: -134.2 |\n",
"| Experiment: 3 | Episode: 357 | Score: -134.17 | Avg Score: -133.28 |\n",
"| Experiment: 3 | Episode: 358 | Score: -129.17 | Avg Score: -133.44 |\n",
"| Experiment: 3 | Episode: 359 | Score: -103.06 | Avg Score: -133.31 |\n",
"| Experiment: 3 | Episode: 360 | Score: -119.84 | Avg Score: -133.54 |\n",
"| Experiment: 3 | Episode: 361 | Score: -108.43 | Avg Score: -132.84 |\n",
"| Experiment: 3 | Episode: 362 | Score: -138.89 | Avg Score: -132.1 |\n",
"| Experiment: 3 | Episode: 363 | Score: -142.81 | Avg Score: -132.56 |\n",
"| Experiment: 3 | Episode: 364 | Score: -66.12 | Avg Score: -132.35 |\n",
"| Experiment: 3 | Episode: 365 | Score: -97.1 | Avg Score: -132.68 |\n",
"| Experiment: 3 | Episode: 366 | Score: -256.55 | Avg Score: -132.83 |\n",
"| Experiment: 3 | Episode: 367 | Score: -157.49 | Avg Score: -132.33 |\n",
"| Experiment: 3 | Episode: 368 | Score: -178.2 | Avg Score: -133.78 |\n",
"| Experiment: 3 | Episode: 369 | Score: -79.86 | Avg Score: -133.8 |\n",
"| Experiment: 3 | Episode: 370 | Score: -180.19 | Avg Score: -134.67 |\n",
"| Experiment: 3 | Episode: 371 | Score: -144.91 | Avg Score: -134.55 |\n",
"| Experiment: 3 | Episode: 372 | Score: -148.1 | Avg Score: -133.93 |\n",
"| Experiment: 3 | Episode: 373 | Score: -130.33 | Avg Score: -133.73 |\n",
"| Experiment: 3 | Episode: 374 | Score: -105.41 | Avg Score: -133.31 |\n",
"| Experiment: 3 | Episode: 375 | Score: -169.35 | Avg Score: -134.27 |\n",
"| Experiment: 3 | Episode: 376 | Score: -155.24 | Avg Score: -135.12 |\n",
"| Experiment: 3 | Episode: 377 | Score: -51.17 | Avg Score: -134.74 |\n",
"| Experiment: 3 | Episode: 378 | Score: -187.01 | Avg Score: -135.36 |\n",
"| Experiment: 3 | Episode: 379 | Score: -160.82 | Avg Score: -135.74 |\n",
"| Experiment: 3 | Episode: 380 | Score: -54.45 | Avg Score: -132.55 |\n",
"| Experiment: 3 | Episode: 381 | Score: -111.37 | Avg Score: -132.92 |\n",
"| Experiment: 3 | Episode: 382 | Score: -140.25 | Avg Score: -133.9 |\n",
"| Experiment: 3 | Episode: 383 | Score: -248.96 | Avg Score: -134.95 |\n",
"| Experiment: 3 | Episode: 384 | Score: -297.08 | Avg Score: -137.15 |\n",
"| Experiment: 3 | Episode: 385 | Score: -68.88 | Avg Score: -136.32 |\n",
"| Experiment: 3 | Episode: 386 | Score: -86.02 | Avg Score: -136.44 |\n",
"| Experiment: 3 | Episode: 387 | Score: -97.82 | Avg Score: -137.0 |\n",
"| Experiment: 3 | Episode: 388 | Score: -76.19 | Avg Score: -135.4 |\n",
"| Experiment: 3 | Episode: 389 | Score: -95.31 | Avg Score: -135.93 |\n",
"| Experiment: 3 | Episode: 390 | Score: -73.12 | Avg Score: -135.38 |\n",
"| Experiment: 3 | Episode: 391 | Score: -72.12 | Avg Score: -134.77 |\n",
"| Experiment: 3 | Episode: 392 | Score: -147.39 | Avg Score: -133.24 |\n",
"| Experiment: 3 | Episode: 393 | Score: -144.36 | Avg Score: -133.67 |\n",
"| Experiment: 3 | Episode: 394 | Score: -240.96 | Avg Score: -135.27 |\n",
"| Experiment: 3 | Episode: 395 | Score: -123.59 | Avg Score: -135.95 |\n",
"| Experiment: 3 | Episode: 396 | Score: -83.77 | Avg Score: -135.8 |\n",
"| Experiment: 3 | Episode: 397 | Score: -185.3 | Avg Score: -135.24 |\n",
"| Experiment: 3 | Episode: 398 | Score: -129.98 | Avg Score: -134.78 |\n",
"| Experiment: 3 | Episode: 399 | Score: -200.19 | Avg Score: -135.63 |\n",
"| Experiment: 3 | Episode: 400 | Score: -93.71 | Avg Score: -134.19 |\n",
"| Experiment: 3 | Episode: 401 | Score: -187.54 | Avg Score: -135.22 |\n",
"| Experiment: 3 | Episode: 402 | Score: -154.75 | Avg Score: -134.03 |\n",
"| Experiment: 3 | Episode: 403 | Score: -117.74 | Avg Score: -133.86 |\n",
"| Experiment: 3 | Episode: 404 | Score: -94.66 | Avg Score: -134.25 |\n",
"| Experiment: 3 | Episode: 405 | Score: -156.04 | Avg Score: -134.07 |\n",
"| Experiment: 3 | Episode: 406 | Score: -195.7 | Avg Score: -134.82 |\n",
"| Experiment: 3 | Episode: 407 | Score: -90.16 | Avg Score: -134.09 |\n",
"| Experiment: 3 | Episode: 408 | Score: -92.21 | Avg Score: -131.1 |\n",
"| Experiment: 3 | Episode: 409 | Score: -124.11 | Avg Score: -131.4 |\n",
"| Experiment: 3 | Episode: 410 | Score: -49.21 | Avg Score: -128.36 |\n",
"| Experiment: 3 | Episode: 411 | Score: -103.0 | Avg Score: -127.95 |\n",
"| Experiment: 3 | Episode: 412 | Score: -84.24 | Avg Score: -127.79 |\n",
"| Experiment: 3 | Episode: 413 | Score: -174.99 | Avg Score: -127.76 |\n",
"| Experiment: 3 | Episode: 414 | Score: -95.14 | Avg Score: -127.66 |\n",
"| Experiment: 3 | Episode: 415 | Score: -88.26 | Avg Score: -127.56 |\n",
"| Experiment: 3 | Episode: 416 | Score: -190.03 | Avg Score: -127.87 |\n",
"| Experiment: 3 | Episode: 417 | Score: -145.21 | Avg Score: -128.29 |\n",
"| Experiment: 3 | Episode: 418 | Score: -104.71 | Avg Score: -128.92 |\n",
"| Experiment: 3 | Episode: 419 | Score: -161.23 | Avg Score: -129.51 |\n",
"| Experiment: 3 | Episode: 420 | Score: -76.84 | Avg Score: -129.03 |\n",
"| Experiment: 3 | Episode: 421 | Score: -175.71 | Avg Score: -129.54 |\n",
"| Experiment: 3 | Episode: 422 | Score: -84.92 | Avg Score: -128.56 |\n",
"| Experiment: 3 | Episode: 423 | Score: -257.26 | Avg Score: -128.22 |\n",
"| Experiment: 3 | Episode: 424 | Score: -68.83 | Avg Score: -128.09 |\n",
"| Experiment: 3 | Episode: 425 | Score: -71.07 | Avg Score: -127.22 |\n",
"| Experiment: 3 | Episode: 426 | Score: -84.08 | Avg Score: -127.11 |\n",
"| Experiment: 3 | Episode: 427 | Score: -219.65 | Avg Score: -128.19 |\n",
"| Experiment: 3 | Episode: 428 | Score: -110.47 | Avg Score: -128.52 |\n",
"| Experiment: 3 | Episode: 429 | Score: -94.05 | Avg Score: -127.93 |\n",
"| Experiment: 3 | Episode: 430 | Score: -189.04 | Avg Score: -128.92 |\n",
"| Experiment: 3 | Episode: 431 | Score: -210.15 | Avg Score: -130.11 |\n",
"| Experiment: 3 | Episode: 432 | Score: -110.89 | Avg Score: -129.76 |\n",
"| Experiment: 3 | Episode: 433 | Score: -153.01 | Avg Score: -130.84 |\n",
"| Experiment: 3 | Episode: 434 | Score: -284.65 | Avg Score: -132.92 |\n",
"| Experiment: 3 | Episode: 435 | Score: -95.32 | Avg Score: -133.08 |\n",
"| Experiment: 3 | Episode: 436 | Score: -116.92 | Avg Score: -133.12 |\n",
"| Experiment: 3 | Episode: 437 | Score: -336.1 | Avg Score: -135.87 |\n",
"| Experiment: 3 | Episode: 438 | Score: -115.29 | Avg Score: -136.26 |\n",
"| Experiment: 3 | Episode: 439 | Score: -48.09 | Avg Score: -136.18 |\n",
"| Experiment: 3 | Episode: 440 | Score: -102.21 | Avg Score: -136.4 |\n",
"| Experiment: 3 | Episode: 441 | Score: -183.83 | Avg Score: -135.52 |\n",
"| Experiment: 3 | Episode: 442 | Score: -79.78 | Avg Score: -135.18 |\n",
"| Experiment: 3 | Episode: 443 | Score: -211.53 | Avg Score: -136.73 |\n",
"| Experiment: 3 | Episode: 444 | Score: -3.54 | Avg Score: -135.64 |\n",
"| Experiment: 3 | Episode: 445 | Score: -176.59 | Avg Score: -136.1 |\n",
"| Experiment: 3 | Episode: 446 | Score: -44.35 | Avg Score: -134.02 |\n",
"| Experiment: 3 | Episode: 447 | Score: -93.03 | Avg Score: -134.15 |\n",
"| Experiment: 3 | Episode: 448 | Score: -96.51 | Avg Score: -134.56 |\n",
"| Experiment: 3 | Episode: 449 | Score: -45.4 | Avg Score: -132.63 |\n",
"| Experiment: 3 | Episode: 450 | Score: -59.28 | Avg Score: -133.32 |\n",
"| Experiment: 3 | Episode: 451 | Score: -67.7 | Avg Score: -132.82 |\n",
"| Experiment: 3 | Episode: 452 | Score: -57.17 | Avg Score: -130.52 |\n",
"| Experiment: 3 | Episode: 453 | Score: 0.67 | Avg Score: -129.19 |\n",
"| Experiment: 3 | Episode: 454 | Score: -86.73 | Avg Score: -128.81 |\n",
"| Experiment: 3 | Episode: 455 | Score: -107.67 | Avg Score: -127.23 |\n",
"| Experiment: 3 | Episode: 456 | Score: -125.93 | Avg Score: -127.67 |\n",
"| Experiment: 3 | Episode: 457 | Score: -103.39 | Avg Score: -127.36 |\n",
"| Experiment: 3 | Episode: 458 | Score: -282.23 | Avg Score: -128.89 |\n",
"| Experiment: 3 | Episode: 459 | Score: -47.03 | Avg Score: -128.33 |\n",
"| Experiment: 3 | Episode: 460 | Score: -83.56 | Avg Score: -127.97 |\n",
"| Experiment: 3 | Episode: 461 | Score: -271.07 | Avg Score: -129.6 |\n",
"| Experiment: 3 | Episode: 462 | Score: -124.65 | Avg Score: -129.45 |\n",
"| Experiment: 3 | Episode: 463 | Score: -102.41 | Avg Score: -129.05 |\n",
"| Experiment: 3 | Episode: 464 | Score: -234.51 | Avg Score: -130.73 |\n",
"| Experiment: 3 | Episode: 465 | Score: -108.38 | Avg Score: -130.85 |\n",
"| Experiment: 3 | Episode: 466 | Score: -88.47 | Avg Score: -129.16 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 3 | Episode: 467 | Score: -132.67 | Avg Score: -128.92 |\n",
"| Experiment: 3 | Episode: 468 | Score: -51.05 | Avg Score: -127.65 |\n",
"| Experiment: 3 | Episode: 469 | Score: -101.06 | Avg Score: -127.86 |\n",
"| Experiment: 3 | Episode: 470 | Score: -256.05 | Avg Score: -128.62 |\n",
"| Experiment: 3 | Episode: 471 | Score: -298.97 | Avg Score: -130.16 |\n",
"| Experiment: 3 | Episode: 472 | Score: -60.44 | Avg Score: -129.28 |\n",
"| Experiment: 3 | Episode: 473 | Score: -74.93 | Avg Score: -128.73 |\n",
"| Experiment: 3 | Episode: 474 | Score: -55.93 | Avg Score: -128.23 |\n",
"| Experiment: 3 | Episode: 475 | Score: -369.62 | Avg Score: -130.23 |\n",
"| Experiment: 3 | Episode: 476 | Score: -53.15 | Avg Score: -129.21 |\n",
"| Experiment: 3 | Episode: 477 | Score: -116.73 | Avg Score: -129.87 |\n",
"| Experiment: 3 | Episode: 478 | Score: -18.06 | Avg Score: -128.18 |\n",
"| Experiment: 3 | Episode: 479 | Score: -90.38 | Avg Score: -127.47 |\n",
"| Experiment: 3 | Episode: 480 | Score: -150.72 | Avg Score: -128.44 |\n",
"| Experiment: 3 | Episode: 481 | Score: -102.61 | Avg Score: -128.35 |\n",
"| Experiment: 3 | Episode: 482 | Score: -120.08 | Avg Score: -128.15 |\n",
"| Experiment: 3 | Episode: 483 | Score: -102.45 | Avg Score: -126.68 |\n",
"| Experiment: 3 | Episode: 484 | Score: -48.47 | Avg Score: -124.2 |\n",
"| Experiment: 3 | Episode: 485 | Score: -66.62 | Avg Score: -124.17 |\n",
"| Experiment: 3 | Episode: 486 | Score: -67.98 | Avg Score: -123.99 |\n",
"| Experiment: 3 | Episode: 487 | Score: -194.01 | Avg Score: -124.96 |\n",
"| Experiment: 3 | Episode: 488 | Score: -268.02 | Avg Score: -126.87 |\n",
"| Experiment: 3 | Episode: 489 | Score: -98.01 | Avg Score: -126.9 |\n",
"| Experiment: 3 | Episode: 490 | Score: -115.38 | Avg Score: -127.32 |\n",
"| Experiment: 3 | Episode: 491 | Score: -200.49 | Avg Score: -128.61 |\n",
"| Experiment: 3 | Episode: 492 | Score: -177.84 | Avg Score: -128.91 |\n",
"| Experiment: 3 | Episode: 493 | Score: -89.8 | Avg Score: -128.37 |\n",
"| Experiment: 3 | Episode: 494 | Score: -87.57 | Avg Score: -126.83 |\n",
"| Experiment: 3 | Episode: 495 | Score: -188.5 | Avg Score: -127.48 |\n",
"| Experiment: 3 | Episode: 496 | Score: -259.27 | Avg Score: -129.24 |\n",
"| Experiment: 3 | Episode: 497 | Score: -27.58 | Avg Score: -127.66 |\n",
"| Experiment: 3 | Episode: 498 | Score: -309.89 | Avg Score: -129.46 |\n",
"| Experiment: 3 | Episode: 499 | Score: -45.13 | Avg Score: -127.91 |\n",
"| Experiment: 3 | Episode: 500 | Score: -192.44 | Avg Score: -128.89 |\n",
"| Experiment: 3 | Episode: 501 | Score: -2.51 | Avg Score: -127.04 |\n",
"| Experiment: 3 | Episode: 502 | Score: -109.83 | Avg Score: -126.59 |\n",
"| Experiment: 3 | Episode: 503 | Score: -71.57 | Avg Score: -126.13 |\n",
"| Experiment: 3 | Episode: 504 | Score: -82.86 | Avg Score: -126.02 |\n",
"| Experiment: 3 | Episode: 505 | Score: -229.18 | Avg Score: -126.75 |\n",
"| Experiment: 3 | Episode: 506 | Score: -7.72 | Avg Score: -124.87 |\n",
"| Experiment: 3 | Episode: 507 | Score: -211.66 | Avg Score: -126.08 |\n",
"| Experiment: 3 | Episode: 508 | Score: -41.23 | Avg Score: -125.57 |\n",
"| Experiment: 3 | Episode: 509 | Score: -67.58 | Avg Score: -125.01 |\n",
"| Experiment: 3 | Episode: 510 | Score: -92.43 | Avg Score: -125.44 |\n",
"| Experiment: 3 | Episode: 511 | Score: -88.26 | Avg Score: -125.29 |\n",
"| Experiment: 3 | Episode: 512 | Score: -199.59 | Avg Score: -126.45 |\n",
"| Experiment: 3 | Episode: 513 | Score: -96.9 | Avg Score: -125.66 |\n",
"| Experiment: 3 | Episode: 514 | Score: -106.73 | Avg Score: -125.78 |\n",
"| Experiment: 3 | Episode: 515 | Score: -77.8 | Avg Score: -125.68 |\n",
"| Experiment: 3 | Episode: 516 | Score: -155.2 | Avg Score: -125.33 |\n",
"| Experiment: 3 | Episode: 517 | Score: -131.76 | Avg Score: -125.19 |\n",
"| Experiment: 3 | Episode: 518 | Score: -118.42 | Avg Score: -125.33 |\n",
"| Experiment: 3 | Episode: 519 | Score: -269.54 | Avg Score: -126.41 |\n",
"| Experiment: 3 | Episode: 520 | Score: -121.93 | Avg Score: -126.86 |\n",
"| Experiment: 3 | Episode: 521 | Score: -89.55 | Avg Score: -126.0 |\n",
"| Experiment: 3 | Episode: 522 | Score: -169.6 | Avg Score: -126.85 |\n",
"| Experiment: 3 | Episode: 523 | Score: -58.6 | Avg Score: -124.86 |\n",
"| Experiment: 3 | Episode: 524 | Score: -102.88 | Avg Score: -125.2 |\n",
"| Experiment: 3 | Episode: 525 | Score: -95.49 | Avg Score: -125.45 |\n",
"| Experiment: 3 | Episode: 526 | Score: -92.32 | Avg Score: -125.53 |\n",
"| Experiment: 3 | Episode: 527 | Score: -73.51 | Avg Score: -124.07 |\n",
"| Experiment: 3 | Episode: 528 | Score: -100.34 | Avg Score: -123.97 |\n",
"| Experiment: 3 | Episode: 529 | Score: -228.22 | Avg Score: -125.31 |\n",
"| Experiment: 3 | Episode: 530 | Score: -260.21 | Avg Score: -126.02 |\n",
"| Experiment: 3 | Episode: 531 | Score: -170.84 | Avg Score: -125.63 |\n",
"| Experiment: 3 | Episode: 532 | Score: -83.4 | Avg Score: -125.35 |\n",
"| Experiment: 3 | Episode: 533 | Score: -116.32 | Avg Score: -124.99 |\n",
"| Experiment: 3 | Episode: 534 | Score: -116.22 | Avg Score: -123.3 |\n",
"| Experiment: 3 | Episode: 535 | Score: -175.85 | Avg Score: -124.11 |\n",
"| Experiment: 3 | Episode: 536 | Score: -93.66 | Avg Score: -123.87 |\n",
"| Experiment: 3 | Episode: 537 | Score: -94.26 | Avg Score: -121.46 |\n",
"| Experiment: 3 | Episode: 538 | Score: -142.47 | Avg Score: -121.73 |\n",
"| Experiment: 3 | Episode: 539 | Score: -120.92 | Avg Score: -122.46 |\n",
"| Experiment: 3 | Episode: 540 | Score: -106.31 | Avg Score: -122.5 |\n",
"| Experiment: 3 | Episode: 541 | Score: -117.39 | Avg Score: -121.83 |\n",
"| Experiment: 3 | Episode: 542 | Score: -97.27 | Avg Score: -122.01 |\n",
"| Experiment: 3 | Episode: 543 | Score: -112.58 | Avg Score: -121.02 |\n",
"| Experiment: 3 | Episode: 544 | Score: -178.01 | Avg Score: -122.76 |\n",
"| Experiment: 3 | Episode: 545 | Score: -283.76 | Avg Score: -123.83 |\n",
"| Experiment: 3 | Episode: 546 | Score: -71.02 | Avg Score: -124.1 |\n",
"| Experiment: 3 | Episode: 547 | Score: -187.15 | Avg Score: -125.04 |\n",
"| Experiment: 3 | Episode: 548 | Score: -84.9 | Avg Score: -124.93 |\n",
"| Experiment: 3 | Episode: 549 | Score: -56.08 | Avg Score: -125.03 |\n",
"| Experiment: 3 | Episode: 550 | Score: -141.51 | Avg Score: -125.85 |\n",
"| Experiment: 3 | Episode: 551 | Score: -29.65 | Avg Score: -125.47 |\n",
"| Experiment: 3 | Episode: 552 | Score: -108.2 | Avg Score: -125.98 |\n",
"| Experiment: 3 | Episode: 553 | Score: -94.99 | Avg Score: -126.94 |\n",
"| Experiment: 3 | Episode: 554 | Score: -85.59 | Avg Score: -126.93 |\n",
"| Experiment: 3 | Episode: 555 | Score: -1.16 | Avg Score: -125.86 |\n",
"| Experiment: 3 | Episode: 556 | Score: -104.45 | Avg Score: -125.65 |\n",
"| Experiment: 3 | Episode: 557 | Score: -40.09 | Avg Score: -125.02 |\n",
"| Experiment: 3 | Episode: 558 | Score: -94.52 | Avg Score: -123.14 |\n",
"| Experiment: 3 | Episode: 559 | Score: -78.73 | Avg Score: -123.46 |\n",
"| Experiment: 3 | Episode: 560 | Score: -36.01 | Avg Score: -122.98 |\n",
"| Experiment: 3 | Episode: 561 | Score: -70.93 | Avg Score: -120.98 |\n",
"| Experiment: 3 | Episode: 562 | Score: -165.2 | Avg Score: -121.39 |\n",
"| Experiment: 3 | Episode: 563 | Score: -219.69 | Avg Score: -122.56 |\n",
"| Experiment: 3 | Episode: 564 | Score: -87.77 | Avg Score: -121.09 |\n",
"| Experiment: 3 | Episode: 565 | Score: -95.66 | Avg Score: -120.96 |\n",
"| Experiment: 3 | Episode: 566 | Score: -54.12 | Avg Score: -120.62 |\n",
"| Experiment: 3 | Episode: 567 | Score: -84.05 | Avg Score: -120.13 |\n",
"| Experiment: 3 | Episode: 568 | Score: -56.9 | Avg Score: -120.19 |\n",
"| Experiment: 3 | Episode: 569 | Score: -62.05 | Avg Score: -119.8 |\n",
"| Experiment: 3 | Episode: 570 | Score: -81.24 | Avg Score: -118.05 |\n",
"| Experiment: 3 | Episode: 571 | Score: -111.96 | Avg Score: -116.18 |\n",
"| Experiment: 3 | Episode: 572 | Score: -67.77 | Avg Score: -116.26 |\n",
"| Experiment: 3 | Episode: 573 | Score: -88.8 | Avg Score: -116.4 |\n",
"| Experiment: 3 | Episode: 574 | Score: -83.99 | Avg Score: -116.68 |\n",
"| Experiment: 3 | Episode: 575 | Score: -86.63 | Avg Score: -113.85 |\n",
"| Experiment: 3 | Episode: 576 | Score: -115.09 | Avg Score: -114.47 |\n",
"| Experiment: 3 | Episode: 577 | Score: -99.73 | Avg Score: -114.3 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 3 | Episode: 578 | Score: -197.76 | Avg Score: -116.09 |\n",
"| Experiment: 3 | Episode: 579 | Score: -147.11 | Avg Score: -116.66 |\n",
"| Experiment: 3 | Episode: 580 | Score: -98.35 | Avg Score: -116.14 |\n",
"| Experiment: 3 | Episode: 581 | Score: -40.68 | Avg Score: -115.52 |\n",
"| Experiment: 3 | Episode: 582 | Score: -111.18 | Avg Score: -115.43 |\n",
"| Experiment: 3 | Episode: 583 | Score: -120.07 | Avg Score: -115.6 |\n",
"| Experiment: 3 | Episode: 584 | Score: -66.82 | Avg Score: -115.79 |\n",
"| Experiment: 3 | Episode: 585 | Score: -101.22 | Avg Score: -116.13 |\n",
"| Experiment: 3 | Episode: 586 | Score: -86.97 | Avg Score: -116.32 |\n",
"| Experiment: 3 | Episode: 587 | Score: -167.3 | Avg Score: -116.06 |\n",
"| Experiment: 3 | Episode: 588 | Score: -262.66 | Avg Score: -116.0 |\n",
"| Experiment: 3 | Episode: 589 | Score: -91.62 | Avg Score: -115.94 |\n",
"| Experiment: 3 | Episode: 590 | Score: -66.0 | Avg Score: -115.45 |\n",
"| Experiment: 3 | Episode: 591 | Score: -163.97 | Avg Score: -115.08 |\n",
"| Experiment: 3 | Episode: 592 | Score: -62.59 | Avg Score: -113.93 |\n",
"| Experiment: 3 | Episode: 593 | Score: -78.54 | Avg Score: -113.82 |\n",
"| Experiment: 3 | Episode: 594 | Score: -171.92 | Avg Score: -114.66 |\n",
"| Experiment: 3 | Episode: 595 | Score: -31.47 | Avg Score: -113.09 |\n",
"| Experiment: 3 | Episode: 596 | Score: -46.12 | Avg Score: -110.96 |\n",
"| Experiment: 3 | Episode: 597 | Score: -78.05 | Avg Score: -111.46 |\n",
"| Experiment: 3 | Episode: 598 | Score: -164.32 | Avg Score: -110.01 |\n",
"| Experiment: 3 | Episode: 599 | Score: -84.17 | Avg Score: -110.4 |\n",
"| Experiment: 3 | Episode: 600 | Score: -200.85 | Avg Score: -110.48 |\n",
"| Experiment: 3 | Episode: 601 | Score: -32.95 | Avg Score: -110.78 |\n",
"| Experiment: 3 | Episode: 602 | Score: -39.01 | Avg Score: -110.08 |\n",
"| Experiment: 3 | Episode: 603 | Score: -315.19 | Avg Score: -112.51 |\n",
"| Experiment: 3 | Episode: 604 | Score: -123.83 | Avg Score: -112.92 |\n",
"| Experiment: 3 | Episode: 605 | Score: -276.23 | Avg Score: -113.39 |\n",
"| Experiment: 3 | Episode: 606 | Score: -174.6 | Avg Score: -115.06 |\n",
"| Experiment: 3 | Episode: 607 | Score: -37.93 | Avg Score: -113.32 |\n",
"| Experiment: 3 | Episode: 608 | Score: -56.44 | Avg Score: -113.48 |\n",
"| Experiment: 3 | Episode: 609 | Score: -120.04 | Avg Score: -114.0 |\n",
"| Experiment: 3 | Episode: 610 | Score: -47.49 | Avg Score: -113.55 |\n",
"| Experiment: 3 | Episode: 611 | Score: -303.27 | Avg Score: -115.7 |\n",
"| Experiment: 3 | Episode: 612 | Score: -119.63 | Avg Score: -114.9 |\n",
"| Experiment: 3 | Episode: 613 | Score: -56.87 | Avg Score: -114.5 |\n",
"| Experiment: 3 | Episode: 614 | Score: -71.53 | Avg Score: -114.15 |\n",
"| Experiment: 3 | Episode: 615 | Score: -66.14 | Avg Score: -114.03 |\n",
"| Experiment: 3 | Episode: 616 | Score: -152.73 | Avg Score: -114.01 |\n",
"| Experiment: 3 | Episode: 617 | Score: -69.1 | Avg Score: -113.38 |\n",
"| Experiment: 3 | Episode: 618 | Score: -53.98 | Avg Score: -112.74 |\n",
"| Experiment: 3 | Episode: 619 | Score: -88.53 | Avg Score: -110.93 |\n",
"| Experiment: 3 | Episode: 620 | Score: -78.4 | Avg Score: -110.49 |\n",
"| Experiment: 3 | Episode: 621 | Score: -34.05 | Avg Score: -109.94 |\n",
"| Experiment: 3 | Episode: 622 | Score: -78.95 | Avg Score: -109.03 |\n",
"| Experiment: 3 | Episode: 623 | Score: -74.7 | Avg Score: -109.19 |\n",
"| Experiment: 3 | Episode: 624 | Score: -67.23 | Avg Score: -108.84 |\n",
"| Experiment: 3 | Episode: 625 | Score: -195.56 | Avg Score: -109.84 |\n",
"| Experiment: 3 | Episode: 626 | Score: -83.25 | Avg Score: -109.75 |\n",
"| Experiment: 3 | Episode: 627 | Score: -152.01 | Avg Score: -110.53 |\n",
"| Experiment: 3 | Episode: 628 | Score: -66.11 | Avg Score: -110.19 |\n",
"| Experiment: 3 | Episode: 629 | Score: -62.31 | Avg Score: -108.53 |\n",
"| Experiment: 3 | Episode: 630 | Score: -59.13 | Avg Score: -106.52 |\n",
"| Experiment: 3 | Episode: 631 | Score: -153.71 | Avg Score: -106.35 |\n",
"| Experiment: 3 | Episode: 632 | Score: -104.79 | Avg Score: -106.56 |\n",
"| Experiment: 3 | Episode: 633 | Score: -106.39 | Avg Score: -106.46 |\n",
"| Experiment: 3 | Episode: 634 | Score: 30.21 | Avg Score: -105.0 |\n",
"| Experiment: 3 | Episode: 635 | Score: -162.26 | Avg Score: -104.86 |\n",
"| Experiment: 3 | Episode: 636 | Score: -90.9 | Avg Score: -104.83 |\n",
"| Experiment: 3 | Episode: 637 | Score: -95.61 | Avg Score: -104.85 |\n",
"| Experiment: 3 | Episode: 638 | Score: -226.02 | Avg Score: -105.68 |\n",
"| Experiment: 3 | Episode: 639 | Score: -167.92 | Avg Score: -106.15 |\n",
"| Experiment: 3 | Episode: 640 | Score: -342.03 | Avg Score: -108.51 |\n",
"| Experiment: 3 | Episode: 641 | Score: -173.57 | Avg Score: -109.07 |\n",
"| Experiment: 3 | Episode: 642 | Score: -51.65 | Avg Score: -108.62 |\n",
"| Experiment: 3 | Episode: 643 | Score: -81.77 | Avg Score: -108.31 |\n",
"| Experiment: 3 | Episode: 644 | Score: -68.57 | Avg Score: -107.21 |\n",
"| Experiment: 3 | Episode: 645 | Score: -73.27 | Avg Score: -105.11 |\n",
"| Experiment: 3 | Episode: 646 | Score: -161.2 | Avg Score: -106.01 |\n",
"| Experiment: 3 | Episode: 647 | Score: -14.03 | Avg Score: -104.28 |\n",
"| Experiment: 3 | Episode: 648 | Score: -71.4 | Avg Score: -104.14 |\n",
"| Experiment: 3 | Episode: 649 | Score: -259.57 | Avg Score: -106.18 |\n",
"| Experiment: 3 | Episode: 650 | Score: -75.89 | Avg Score: -105.52 |\n",
"| Experiment: 3 | Episode: 651 | Score: -185.26 | Avg Score: -107.08 |\n",
"| Experiment: 3 | Episode: 652 | Score: -97.7 | Avg Score: -106.97 |\n",
"| Experiment: 3 | Episode: 653 | Score: -80.66 | Avg Score: -106.83 |\n",
"| Experiment: 3 | Episode: 654 | Score: -100.18 | Avg Score: -106.98 |\n",
"| Experiment: 3 | Episode: 655 | Score: -146.28 | Avg Score: -108.43 |\n",
"| Experiment: 3 | Episode: 656 | Score: -127.52 | Avg Score: -108.66 |\n",
"| Experiment: 3 | Episode: 657 | Score: -55.14 | Avg Score: -108.81 |\n",
"| Experiment: 3 | Episode: 658 | Score: -115.32 | Avg Score: -109.02 |\n",
"| Experiment: 3 | Episode: 659 | Score: -99.87 | Avg Score: -109.23 |\n",
"| Experiment: 3 | Episode: 660 | Score: -259.29 | Avg Score: -111.46 |\n",
"| Experiment: 3 | Episode: 661 | Score: -123.57 | Avg Score: -111.99 |\n",
"| Experiment: 3 | Episode: 662 | Score: -80.74 | Avg Score: -111.14 |\n",
"| Experiment: 3 | Episode: 663 | Score: -93.39 | Avg Score: -109.88 |\n",
"| Experiment: 3 | Episode: 664 | Score: -137.99 | Avg Score: -110.38 |\n",
"| Experiment: 3 | Episode: 665 | Score: -115.95 | Avg Score: -110.58 |\n",
"| Experiment: 3 | Episode: 666 | Score: -100.24 | Avg Score: -111.05 |\n",
"| Experiment: 3 | Episode: 667 | Score: -118.28 | Avg Score: -111.39 |\n",
"| Experiment: 3 | Episode: 668 | Score: -163.51 | Avg Score: -112.45 |\n",
"| Experiment: 3 | Episode: 669 | Score: -22.19 | Avg Score: -112.06 |\n",
"| Experiment: 3 | Episode: 670 | Score: -157.51 | Avg Score: -112.82 |\n",
"| Experiment: 3 | Episode: 671 | Score: -103.42 | Avg Score: -112.73 |\n",
"| Experiment: 3 | Episode: 672 | Score: -110.2 | Avg Score: -113.16 |\n",
"| Experiment: 3 | Episode: 673 | Score: -69.01 | Avg Score: -112.96 |\n",
"| Experiment: 3 | Episode: 674 | Score: -465.03 | Avg Score: -116.77 |\n",
"| Experiment: 3 | Episode: 675 | Score: -71.33 | Avg Score: -116.62 |\n",
"| Experiment: 3 | Episode: 676 | Score: -51.58 | Avg Score: -115.98 |\n",
"| Experiment: 3 | Episode: 677 | Score: -78.22 | Avg Score: -115.77 |\n",
"| Experiment: 3 | Episode: 678 | Score: -108.26 | Avg Score: -114.87 |\n",
"| Experiment: 3 | Episode: 679 | Score: -30.93 | Avg Score: -113.71 |\n",
"| Experiment: 3 | Episode: 680 | Score: -168.56 | Avg Score: -114.41 |\n",
"| Experiment: 3 | Episode: 681 | Score: -39.68 | Avg Score: -114.4 |\n",
"| Experiment: 3 | Episode: 682 | Score: -82.52 | Avg Score: -114.12 |\n",
"| Experiment: 3 | Episode: 683 | Score: -132.45 | Avg Score: -114.24 |\n",
"| Experiment: 3 | Episode: 684 | Score: -45.35 | Avg Score: -114.02 |\n",
"| Experiment: 3 | Episode: 685 | Score: -106.07 | Avg Score: -114.07 |\n",
"| Experiment: 3 | Episode: 686 | Score: -186.04 | Avg Score: -115.06 |\n",
"| Experiment: 3 | Episode: 687 | Score: -365.24 | Avg Score: -117.04 |\n",
"| Experiment: 3 | Episode: 688 | Score: -80.53 | Avg Score: -115.22 |\n",
"| Experiment: 3 | Episode: 689 | Score: -70.85 | Avg Score: -115.01 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 3 | Episode: 690 | Score: -110.07 | Avg Score: -115.45 |\n",
"| Experiment: 3 | Episode: 691 | Score: -67.16 | Avg Score: -114.49 |\n",
"| Experiment: 3 | Episode: 692 | Score: -65.43 | Avg Score: -114.51 |\n",
"| Experiment: 3 | Episode: 693 | Score: -40.06 | Avg Score: -114.13 |\n",
"| Experiment: 3 | Episode: 694 | Score: -59.4 | Avg Score: -113.01 |\n",
"| Experiment: 3 | Episode: 695 | Score: -162.52 | Avg Score: -114.32 |\n",
"| Experiment: 3 | Episode: 696 | Score: -87.44 | Avg Score: -114.73 |\n",
"| Experiment: 3 | Episode: 697 | Score: -92.54 | Avg Score: -114.87 |\n",
"| Experiment: 3 | Episode: 698 | Score: -47.41 | Avg Score: -113.7 |\n",
"| Experiment: 3 | Episode: 699 | Score: -238.44 | Avg Score: -115.25 |\n",
"| Experiment: 3 | Episode: 700 | Score: -104.67 | Avg Score: -114.29 |\n",
"| Experiment: 3 | Episode: 701 | Score: -89.79 | Avg Score: -114.85 |\n",
"| Experiment: 3 | Episode: 702 | Score: -121.63 | Avg Score: -115.68 |\n",
"| Experiment: 3 | Episode: 703 | Score: -100.23 | Avg Score: -113.53 |\n",
"| Experiment: 3 | Episode: 704 | Score: -185.13 | Avg Score: -114.14 |\n",
"| Experiment: 3 | Episode: 705 | Score: -85.77 | Avg Score: -112.24 |\n",
"| Experiment: 3 | Episode: 706 | Score: -147.73 | Avg Score: -111.97 |\n",
"| Experiment: 3 | Episode: 707 | Score: -85.05 | Avg Score: -112.44 |\n",
"| Experiment: 3 | Episode: 708 | Score: -102.98 | Avg Score: -112.91 |\n",
"| Experiment: 3 | Episode: 709 | Score: -114.03 | Avg Score: -112.85 |\n",
"| Experiment: 3 | Episode: 710 | Score: -151.26 | Avg Score: -113.88 |\n",
"| Experiment: 3 | Episode: 711 | Score: -74.31 | Avg Score: -111.59 |\n",
"| Experiment: 3 | Episode: 712 | Score: -130.72 | Avg Score: -111.71 |\n",
"| Experiment: 3 | Episode: 713 | Score: -38.29 | Avg Score: -111.52 |\n",
"| Experiment: 3 | Episode: 714 | Score: -138.47 | Avg Score: -112.19 |\n",
"| Experiment: 3 | Episode: 715 | Score: -116.59 | Avg Score: -112.69 |\n",
"| Experiment: 3 | Episode: 716 | Score: -124.13 | Avg Score: -112.41 |\n",
"| Experiment: 3 | Episode: 717 | Score: -82.88 | Avg Score: -112.55 |\n",
"| Experiment: 3 | Episode: 718 | Score: -66.56 | Avg Score: -112.67 |\n",
"| Experiment: 3 | Episode: 719 | Score: -61.1 | Avg Score: -112.4 |\n",
"| Experiment: 3 | Episode: 720 | Score: -89.36 | Avg Score: -112.51 |\n",
"| Experiment: 3 | Episode: 721 | Score: -126.34 | Avg Score: -113.43 |\n",
"| Experiment: 3 | Episode: 722 | Score: -90.75 | Avg Score: -113.55 |\n",
"| Experiment: 3 | Episode: 723 | Score: -104.51 | Avg Score: -113.85 |\n",
"| Experiment: 3 | Episode: 724 | Score: -66.32 | Avg Score: -113.84 |\n",
"| Experiment: 3 | Episode: 725 | Score: -85.66 | Avg Score: -112.74 |\n",
"| Experiment: 3 | Episode: 726 | Score: -130.11 | Avg Score: -113.21 |\n",
"| Experiment: 3 | Episode: 727 | Score: -85.75 | Avg Score: -112.54 |\n",
"| Experiment: 3 | Episode: 728 | Score: -191.57 | Avg Score: -113.8 |\n",
"| Experiment: 3 | Episode: 729 | Score: -106.22 | Avg Score: -114.24 |\n",
"| Experiment: 3 | Episode: 730 | Score: -154.77 | Avg Score: -115.19 |\n",
"| Experiment: 3 | Episode: 731 | Score: -87.7 | Avg Score: -114.53 |\n",
"| Experiment: 3 | Episode: 732 | Score: -93.16 | Avg Score: -114.42 |\n",
"| Experiment: 3 | Episode: 733 | Score: -107.17 | Avg Score: -114.42 |\n",
"| Experiment: 3 | Episode: 734 | Score: -69.57 | Avg Score: -115.42 |\n",
"| Experiment: 3 | Episode: 735 | Score: -75.09 | Avg Score: -114.55 |\n",
"| Experiment: 3 | Episode: 736 | Score: -59.79 | Avg Score: -114.24 |\n",
"| Experiment: 3 | Episode: 737 | Score: -71.0 | Avg Score: -113.99 |\n",
"| Experiment: 3 | Episode: 738 | Score: -118.01 | Avg Score: -112.91 |\n",
"| Experiment: 3 | Episode: 739 | Score: -255.09 | Avg Score: -113.79 |\n",
"| Experiment: 3 | Episode: 740 | Score: -80.8 | Avg Score: -111.17 |\n",
"| Experiment: 3 | Episode: 741 | Score: -80.37 | Avg Score: -110.24 |\n",
"| Experiment: 3 | Episode: 742 | Score: -86.23 | Avg Score: -110.59 |\n",
"| Experiment: 3 | Episode: 743 | Score: -91.34 | Avg Score: -110.68 |\n",
"| Experiment: 3 | Episode: 744 | Score: -116.38 | Avg Score: -111.16 |\n",
"| Experiment: 3 | Episode: 745 | Score: -74.64 | Avg Score: -111.17 |\n",
"| Experiment: 3 | Episode: 746 | Score: -114.68 | Avg Score: -110.71 |\n",
"| Experiment: 3 | Episode: 747 | Score: -179.45 | Avg Score: -112.36 |\n",
"| Experiment: 3 | Episode: 748 | Score: -68.32 | Avg Score: -112.33 |\n",
"| Experiment: 3 | Episode: 749 | Score: -120.16 | Avg Score: -110.94 |\n",
"| Experiment: 3 | Episode: 750 | Score: -107.21 | Avg Score: -111.25 |\n",
"| Experiment: 3 | Episode: 751 | Score: -46.13 | Avg Score: -109.86 |\n",
"| Experiment: 3 | Episode: 752 | Score: -113.15 | Avg Score: -110.01 |\n",
"| Experiment: 3 | Episode: 753 | Score: -104.55 | Avg Score: -110.25 |\n",
"| Experiment: 3 | Episode: 754 | Score: -160.18 | Avg Score: -110.85 |\n",
"| Experiment: 3 | Episode: 755 | Score: -148.03 | Avg Score: -110.87 |\n",
"| Experiment: 3 | Episode: 756 | Score: -102.86 | Avg Score: -110.62 |\n",
"| Experiment: 3 | Episode: 757 | Score: -116.19 | Avg Score: -111.23 |\n",
"| Experiment: 3 | Episode: 758 | Score: -89.73 | Avg Score: -110.98 |\n",
"| Experiment: 3 | Episode: 759 | Score: -199.35 | Avg Score: -111.97 |\n",
"| Experiment: 3 | Episode: 760 | Score: -43.71 | Avg Score: -109.82 |\n",
"| Experiment: 3 | Episode: 761 | Score: -182.78 | Avg Score: -110.41 |\n",
"| Experiment: 3 | Episode: 762 | Score: -81.23 | Avg Score: -110.42 |\n",
"| Experiment: 3 | Episode: 763 | Score: -98.93 | Avg Score: -110.47 |\n",
"| Experiment: 3 | Episode: 764 | Score: -120.98 | Avg Score: -110.3 |\n",
"| Experiment: 3 | Episode: 765 | Score: -315.21 | Avg Score: -112.29 |\n",
"| Experiment: 3 | Episode: 766 | Score: -44.44 | Avg Score: -111.74 |\n",
"| Experiment: 3 | Episode: 767 | Score: -81.62 | Avg Score: -111.37 |\n",
"| Experiment: 3 | Episode: 768 | Score: -58.75 | Avg Score: -110.32 |\n",
"| Experiment: 3 | Episode: 769 | Score: -98.0 | Avg Score: -111.08 |\n",
"| Experiment: 3 | Episode: 770 | Score: -85.52 | Avg Score: -110.36 |\n",
"| Experiment: 3 | Episode: 771 | Score: -50.0 | Avg Score: -109.82 |\n",
"| Experiment: 3 | Episode: 772 | Score: -96.92 | Avg Score: -109.69 |\n",
"| Experiment: 3 | Episode: 773 | Score: -97.08 | Avg Score: -109.97 |\n",
"| Experiment: 3 | Episode: 774 | Score: -8.11 | Avg Score: -105.4 |\n",
"| Experiment: 3 | Episode: 775 | Score: -72.77 | Avg Score: -105.42 |\n",
"| Experiment: 3 | Episode: 776 | Score: -224.14 | Avg Score: -107.14 |\n",
"| Experiment: 3 | Episode: 777 | Score: -84.99 | Avg Score: -107.21 |\n",
"| Experiment: 3 | Episode: 778 | Score: -387.24 | Avg Score: -110.0 |\n",
"| Experiment: 3 | Episode: 779 | Score: -36.31 | Avg Score: -110.05 |\n",
"| Experiment: 3 | Episode: 780 | Score: -111.26 | Avg Score: -109.48 |\n",
"| Experiment: 3 | Episode: 781 | Score: -94.95 | Avg Score: -110.03 |\n",
"| Experiment: 3 | Episode: 782 | Score: -127.37 | Avg Score: -110.48 |\n",
"| Experiment: 3 | Episode: 783 | Score: -252.2 | Avg Score: -111.68 |\n",
"| Experiment: 3 | Episode: 784 | Score: -103.91 | Avg Score: -112.27 |\n",
"| Experiment: 3 | Episode: 785 | Score: -80.48 | Avg Score: -112.01 |\n",
"| Experiment: 3 | Episode: 786 | Score: -52.84 | Avg Score: -110.68 |\n",
"| Experiment: 3 | Episode: 787 | Score: -64.22 | Avg Score: -107.67 |\n",
"| Experiment: 3 | Episode: 788 | Score: -135.94 | Avg Score: -108.22 |\n",
"| Experiment: 3 | Episode: 789 | Score: -170.79 | Avg Score: -109.22 |\n",
"| Experiment: 3 | Episode: 790 | Score: -109.22 | Avg Score: -109.21 |\n",
"| Experiment: 3 | Episode: 791 | Score: -74.45 | Avg Score: -109.29 |\n",
"| Experiment: 3 | Episode: 792 | Score: -164.89 | Avg Score: -110.28 |\n",
"| Experiment: 3 | Episode: 793 | Score: -98.13 | Avg Score: -110.86 |\n",
"| Experiment: 3 | Episode: 794 | Score: -81.29 | Avg Score: -111.08 |\n",
"| Experiment: 3 | Episode: 795 | Score: -188.54 | Avg Score: -111.34 |\n",
"| Experiment: 3 | Episode: 796 | Score: -102.15 | Avg Score: -111.49 |\n",
"| Experiment: 3 | Episode: 797 | Score: -101.7 | Avg Score: -111.58 |\n",
"| Experiment: 3 | Episode: 798 | Score: -125.87 | Avg Score: -112.36 |\n",
"| Experiment: 3 | Episode: 799 | Score: -61.27 | Avg Score: -110.59 |\n",
"| Experiment: 3 | Episode: 800 | Score: -197.69 | Avg Score: -111.52 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 3 | Episode: 801 | Score: -83.39 | Avg Score: -111.46 |\n",
"| Experiment: 3 | Episode: 802 | Score: -119.69 | Avg Score: -111.44 |\n",
"| Experiment: 3 | Episode: 803 | Score: -108.63 | Avg Score: -111.52 |\n",
"| Experiment: 3 | Episode: 804 | Score: -285.76 | Avg Score: -112.53 |\n",
"| Experiment: 3 | Episode: 805 | Score: -128.16 | Avg Score: -112.95 |\n",
"| Experiment: 3 | Episode: 806 | Score: -76.04 | Avg Score: -112.24 |\n",
"| Experiment: 3 | Episode: 807 | Score: -130.87 | Avg Score: -112.69 |\n",
"| Experiment: 3 | Episode: 808 | Score: -171.55 | Avg Score: -113.38 |\n",
"| Experiment: 3 | Episode: 809 | Score: -167.85 | Avg Score: -113.92 |\n",
"| Experiment: 3 | Episode: 810 | Score: -275.81 | Avg Score: -115.16 |\n",
"| Experiment: 3 | Episode: 811 | Score: -105.81 | Avg Score: -115.48 |\n",
"| Experiment: 3 | Episode: 812 | Score: -57.44 | Avg Score: -114.75 |\n",
"| Experiment: 3 | Episode: 813 | Score: -41.52 | Avg Score: -114.78 |\n",
"| Experiment: 3 | Episode: 814 | Score: -264.47 | Avg Score: -116.04 |\n",
"| Experiment: 3 | Episode: 815 | Score: -49.45 | Avg Score: -115.37 |\n",
"| Experiment: 3 | Episode: 816 | Score: -75.37 | Avg Score: -114.88 |\n",
"| Experiment: 3 | Episode: 817 | Score: -157.86 | Avg Score: -115.63 |\n",
"| Experiment: 3 | Episode: 818 | Score: -65.62 | Avg Score: -115.62 |\n",
"| Experiment: 3 | Episode: 819 | Score: -151.49 | Avg Score: -116.52 |\n",
"| Experiment: 3 | Episode: 820 | Score: -126.12 | Avg Score: -116.89 |\n",
"| Experiment: 3 | Episode: 821 | Score: -70.33 | Avg Score: -116.33 |\n",
"| Experiment: 3 | Episode: 822 | Score: -106.65 | Avg Score: -116.49 |\n",
"| Experiment: 3 | Episode: 823 | Score: -225.26 | Avg Score: -117.7 |\n",
"| Experiment: 3 | Episode: 824 | Score: -132.72 | Avg Score: -118.36 |\n",
"| Experiment: 3 | Episode: 825 | Score: -56.74 | Avg Score: -118.07 |\n",
"| Experiment: 3 | Episode: 826 | Score: -71.69 | Avg Score: -117.49 |\n",
"| Experiment: 3 | Episode: 827 | Score: -78.27 | Avg Score: -117.41 |\n",
"| Experiment: 3 | Episode: 828 | Score: -54.9 | Avg Score: -116.05 |\n",
"| Experiment: 3 | Episode: 829 | Score: -73.43 | Avg Score: -115.72 |\n",
"| Experiment: 3 | Episode: 830 | Score: -282.02 | Avg Score: -116.99 |\n",
"| Experiment: 3 | Episode: 831 | Score: -141.05 | Avg Score: -117.53 |\n",
"| Experiment: 3 | Episode: 832 | Score: -69.65 | Avg Score: -117.29 |\n",
"| Experiment: 3 | Episode: 833 | Score: -120.01 | Avg Score: -117.42 |\n",
"| Experiment: 3 | Episode: 834 | Score: -78.92 | Avg Score: -117.51 |\n",
"| Experiment: 3 | Episode: 835 | Score: -112.83 | Avg Score: -117.89 |\n",
"| Experiment: 3 | Episode: 836 | Score: -200.62 | Avg Score: -119.3 |\n",
"| Experiment: 3 | Episode: 837 | Score: -250.4 | Avg Score: -121.09 |\n",
"| Experiment: 3 | Episode: 838 | Score: -87.49 | Avg Score: -120.79 |\n",
"| Experiment: 3 | Episode: 839 | Score: -79.25 | Avg Score: -119.03 |\n",
"| Experiment: 3 | Episode: 840 | Score: -131.88 | Avg Score: -119.54 |\n",
"| Experiment: 3 | Episode: 841 | Score: -108.38 | Avg Score: -119.82 |\n",
"| Experiment: 3 | Episode: 842 | Score: -238.67 | Avg Score: -121.34 |\n",
"| Experiment: 3 | Episode: 843 | Score: -63.63 | Avg Score: -121.07 |\n",
"| Experiment: 3 | Episode: 844 | Score: -7.31 | Avg Score: -119.98 |\n",
"| Experiment: 3 | Episode: 845 | Score: -48.39 | Avg Score: -119.71 |\n",
"| Experiment: 3 | Episode: 846 | Score: -109.79 | Avg Score: -119.66 |\n",
"| Experiment: 3 | Episode: 847 | Score: -33.55 | Avg Score: -118.2 |\n",
"| Experiment: 3 | Episode: 848 | Score: -70.03 | Avg Score: -118.22 |\n",
"| Experiment: 3 | Episode: 849 | Score: -204.9 | Avg Score: -119.07 |\n",
"| Experiment: 3 | Episode: 850 | Score: -76.33 | Avg Score: -118.76 |\n",
"| Experiment: 3 | Episode: 851 | Score: -72.24 | Avg Score: -119.02 |\n",
"| Experiment: 3 | Episode: 852 | Score: -82.43 | Avg Score: -118.71 |\n",
"| Experiment: 3 | Episode: 853 | Score: -89.15 | Avg Score: -118.56 |\n",
"| Experiment: 3 | Episode: 854 | Score: -86.7 | Avg Score: -117.83 |\n",
"| Experiment: 3 | Episode: 855 | Score: -195.16 | Avg Score: -118.3 |\n",
"| Experiment: 3 | Episode: 856 | Score: -180.96 | Avg Score: -119.08 |\n",
"| Experiment: 3 | Episode: 857 | Score: -191.86 | Avg Score: -119.83 |\n",
"| Experiment: 3 | Episode: 858 | Score: -111.99 | Avg Score: -120.06 |\n",
"| Experiment: 3 | Episode: 859 | Score: -142.5 | Avg Score: -119.49 |\n",
"| Experiment: 3 | Episode: 860 | Score: -129.65 | Avg Score: -120.35 |\n",
"| Experiment: 3 | Episode: 861 | Score: -123.91 | Avg Score: -119.76 |\n",
"| Experiment: 3 | Episode: 862 | Score: -97.14 | Avg Score: -119.92 |\n",
"| Experiment: 3 | Episode: 863 | Score: -97.86 | Avg Score: -119.91 |\n",
"| Experiment: 3 | Episode: 864 | Score: -182.34 | Avg Score: -120.52 |\n",
"| Experiment: 3 | Episode: 865 | Score: -218.97 | Avg Score: -119.56 |\n",
"| Experiment: 3 | Episode: 866 | Score: -84.88 | Avg Score: -119.96 |\n",
"| Experiment: 3 | Episode: 867 | Score: -249.1 | Avg Score: -121.64 |\n",
"| Experiment: 3 | Episode: 868 | Score: -90.01 | Avg Score: -121.95 |\n",
"| Experiment: 3 | Episode: 869 | Score: -150.12 | Avg Score: -122.47 |\n",
"| Experiment: 3 | Episode: 870 | Score: -150.6 | Avg Score: -123.12 |\n",
"| Experiment: 3 | Episode: 871 | Score: -109.13 | Avg Score: -123.71 |\n",
"| Experiment: 3 | Episode: 872 | Score: -40.72 | Avg Score: -123.15 |\n",
"| Experiment: 3 | Episode: 873 | Score: -233.82 | Avg Score: -124.52 |\n",
"| Experiment: 3 | Episode: 874 | Score: -372.91 | Avg Score: -128.17 |\n",
"| Experiment: 3 | Episode: 875 | Score: -70.8 | Avg Score: -128.15 |\n",
"| Experiment: 3 | Episode: 876 | Score: -30.93 | Avg Score: -126.22 |\n",
"| Experiment: 3 | Episode: 877 | Score: -101.76 | Avg Score: -126.38 |\n",
"| Experiment: 3 | Episode: 878 | Score: -59.39 | Avg Score: -123.1 |\n",
"| Experiment: 3 | Episode: 879 | Score: -84.01 | Avg Score: -123.58 |\n",
"| Experiment: 3 | Episode: 880 | Score: -61.32 | Avg Score: -123.08 |\n",
"| Experiment: 3 | Episode: 881 | Score: -128.84 | Avg Score: -123.42 |\n",
"| Experiment: 3 | Episode: 882 | Score: -206.17 | Avg Score: -124.21 |\n",
"| Experiment: 3 | Episode: 883 | Score: -48.0 | Avg Score: -122.17 |\n",
"| Experiment: 3 | Episode: 884 | Score: -123.0 | Avg Score: -122.36 |\n",
"| Experiment: 3 | Episode: 885 | Score: -92.36 | Avg Score: -122.48 |\n",
"| Experiment: 3 | Episode: 886 | Score: -80.73 | Avg Score: -122.76 |\n",
"| Experiment: 3 | Episode: 887 | Score: -75.58 | Avg Score: -122.87 |\n",
"| Experiment: 3 | Episode: 888 | Score: -72.28 | Avg Score: -122.23 |\n",
"| Experiment: 3 | Episode: 889 | Score: -63.16 | Avg Score: -121.16 |\n",
"| Experiment: 3 | Episode: 890 | Score: -100.14 | Avg Score: -121.07 |\n",
"| Experiment: 3 | Episode: 891 | Score: -173.64 | Avg Score: -122.06 |\n",
"| Experiment: 3 | Episode: 892 | Score: -131.76 | Avg Score: -121.73 |\n",
"| Experiment: 3 | Episode: 893 | Score: -98.56 | Avg Score: -121.73 |\n",
"| Experiment: 3 | Episode: 894 | Score: -71.39 | Avg Score: -121.63 |\n",
"| Experiment: 3 | Episode: 895 | Score: -174.73 | Avg Score: -121.49 |\n",
"| Experiment: 3 | Episode: 896 | Score: -107.61 | Avg Score: -121.55 |\n",
"| Experiment: 3 | Episode: 897 | Score: -142.63 | Avg Score: -121.96 |\n",
"| Experiment: 3 | Episode: 898 | Score: -149.28 | Avg Score: -122.19 |\n",
"| Experiment: 3 | Episode: 899 | Score: -82.75 | Avg Score: -122.41 |\n",
"| Experiment: 3 | Episode: 900 | Score: -89.26 | Avg Score: -121.32 |\n",
"| Experiment: 3 | Episode: 901 | Score: -175.38 | Avg Score: -122.24 |\n",
"| Experiment: 3 | Episode: 902 | Score: -139.11 | Avg Score: -122.44 |\n",
"| Experiment: 3 | Episode: 903 | Score: -107.97 | Avg Score: -122.43 |\n",
"| Experiment: 3 | Episode: 904 | Score: -120.99 | Avg Score: -120.78 |\n",
"| Experiment: 3 | Episode: 905 | Score: -81.43 | Avg Score: -120.31 |\n",
"| Experiment: 3 | Episode: 906 | Score: -37.45 | Avg Score: -119.93 |\n",
"| Experiment: 3 | Episode: 907 | Score: -91.1 | Avg Score: -119.53 |\n",
"| Experiment: 3 | Episode: 908 | Score: -47.11 | Avg Score: -118.29 |\n",
"| Experiment: 3 | Episode: 909 | Score: -236.71 | Avg Score: -118.98 |\n",
"| Experiment: 3 | Episode: 910 | Score: -71.08 | Avg Score: -116.93 |\n",
"| Experiment: 3 | Episode: 911 | Score: -26.8 | Avg Score: -116.14 |\n",
"| Experiment: 3 | Episode: 912 | Score: -81.11 | Avg Score: -116.37 |\n",
"| Experiment: 3 | Episode: 913 | Score: -178.14 | Avg Score: -117.74 |\n",
"| Experiment: 3 | Episode: 914 | Score: -93.11 | Avg Score: -116.03 |\n",
"| Experiment: 3 | Episode: 915 | Score: -52.59 | Avg Score: -116.06 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 3 | Episode: 916 | Score: -101.32 | Avg Score: -116.32 |\n",
"| Experiment: 3 | Episode: 917 | Score: -79.95 | Avg Score: -115.54 |\n",
"| Experiment: 3 | Episode: 918 | Score: -82.32 | Avg Score: -115.71 |\n",
"| Experiment: 3 | Episode: 919 | Score: -154.91 | Avg Score: -115.74 |\n",
"| Experiment: 3 | Episode: 920 | Score: -129.24 | Avg Score: -115.77 |\n",
"| Experiment: 3 | Episode: 921 | Score: -99.21 | Avg Score: -116.06 |\n",
"| Experiment: 3 | Episode: 922 | Score: -82.66 | Avg Score: -115.82 |\n",
"| Experiment: 3 | Episode: 923 | Score: -42.36 | Avg Score: -113.99 |\n",
"| Experiment: 3 | Episode: 924 | Score: -32.05 | Avg Score: -112.98 |\n",
"| Experiment: 3 | Episode: 925 | Score: -97.41 | Avg Score: -113.39 |\n",
"| Experiment: 3 | Episode: 926 | Score: -72.07 | Avg Score: -113.39 |\n",
"| Experiment: 3 | Episode: 927 | Score: -111.06 | Avg Score: -113.72 |\n",
"| Experiment: 3 | Episode: 928 | Score: -128.65 | Avg Score: -114.46 |\n",
"| Experiment: 3 | Episode: 929 | Score: -92.77 | Avg Score: -114.65 |\n",
"| Experiment: 3 | Episode: 930 | Score: -96.2 | Avg Score: -112.8 |\n",
"| Experiment: 3 | Episode: 931 | Score: -94.72 | Avg Score: -112.33 |\n",
"| Experiment: 3 | Episode: 932 | Score: -192.9 | Avg Score: -113.56 |\n",
"| Experiment: 3 | Episode: 933 | Score: -125.04 | Avg Score: -113.61 |\n",
"| Experiment: 3 | Episode: 934 | Score: -122.09 | Avg Score: -114.05 |\n",
"| Experiment: 3 | Episode: 935 | Score: -46.3 | Avg Score: -113.38 |\n",
"| Experiment: 3 | Episode: 936 | Score: -111.7 | Avg Score: -112.49 |\n",
"| Experiment: 3 | Episode: 937 | Score: -65.51 | Avg Score: -110.64 |\n",
"| Experiment: 3 | Episode: 938 | Score: -66.67 | Avg Score: -110.44 |\n",
"| Experiment: 3 | Episode: 939 | Score: -149.47 | Avg Score: -111.14 |\n",
"| Experiment: 3 | Episode: 940 | Score: -111.55 | Avg Score: -110.93 |\n",
"| Experiment: 3 | Episode: 941 | Score: 135.93 | Avg Score: -108.49 |\n",
"| Experiment: 3 | Episode: 942 | Score: -96.35 | Avg Score: -107.07 |\n",
"| Experiment: 3 | Episode: 943 | Score: -36.0 | Avg Score: -106.79 |\n",
"| Experiment: 3 | Episode: 944 | Score: -199.72 | Avg Score: -108.72 |\n",
"| Experiment: 3 | Episode: 945 | Score: -56.25 | Avg Score: -108.79 |\n",
"| Experiment: 3 | Episode: 946 | Score: -82.69 | Avg Score: -108.52 |\n",
"| Experiment: 3 | Episode: 947 | Score: -170.3 | Avg Score: -109.89 |\n",
"| Experiment: 3 | Episode: 948 | Score: -132.23 | Avg Score: -110.51 |\n",
"| Experiment: 3 | Episode: 949 | Score: -44.14 | Avg Score: -108.91 |\n",
"| Experiment: 3 | Episode: 950 | Score: -84.57 | Avg Score: -108.99 |\n",
"| Experiment: 3 | Episode: 951 | Score: -156.64 | Avg Score: -109.83 |\n",
"| Experiment: 3 | Episode: 952 | Score: 8.68 | Avg Score: -108.92 |\n",
"| Experiment: 3 | Episode: 953 | Score: -82.48 | Avg Score: -108.85 |\n",
"| Experiment: 3 | Episode: 954 | Score: -165.72 | Avg Score: -109.64 |\n",
"| Experiment: 3 | Episode: 955 | Score: -71.77 | Avg Score: -108.41 |\n",
"| Experiment: 3 | Episode: 956 | Score: -88.57 | Avg Score: -107.49 |\n",
"| Experiment: 3 | Episode: 957 | Score: -181.19 | Avg Score: -107.38 |\n",
"| Experiment: 3 | Episode: 958 | Score: -150.71 | Avg Score: -107.77 |\n",
"| Experiment: 3 | Episode: 959 | Score: -182.7 | Avg Score: -108.17 |\n",
"| Experiment: 3 | Episode: 960 | Score: -99.67 | Avg Score: -107.87 |\n",
"| Experiment: 3 | Episode: 961 | Score: -85.48 | Avg Score: -107.48 |\n",
"| Experiment: 3 | Episode: 962 | Score: -124.33 | Avg Score: -107.76 |\n",
"| Experiment: 3 | Episode: 963 | Score: -14.71 | Avg Score: -106.92 |\n",
"| Experiment: 3 | Episode: 964 | Score: -93.76 | Avg Score: -106.04 |\n",
"| Experiment: 3 | Episode: 965 | Score: -8.33 | Avg Score: -103.93 |\n",
"| Experiment: 3 | Episode: 966 | Score: -116.04 | Avg Score: -104.24 |\n",
"| Experiment: 3 | Episode: 967 | Score: -86.97 | Avg Score: -102.62 |\n",
"| Experiment: 3 | Episode: 968 | Score: -265.45 | Avg Score: -104.38 |\n",
"| Experiment: 3 | Episode: 969 | Score: -189.81 | Avg Score: -104.77 |\n",
"| Experiment: 3 | Episode: 970 | Score: -128.22 | Avg Score: -104.55 |\n",
"| Experiment: 3 | Episode: 971 | Score: -5.91 | Avg Score: -103.52 |\n",
"| Experiment: 3 | Episode: 972 | Score: -68.75 | Avg Score: -103.8 |\n",
"| Experiment: 3 | Episode: 973 | Score: -88.78 | Avg Score: -102.35 |\n",
"| Experiment: 3 | Episode: 974 | Score: -82.74 | Avg Score: -99.45 |\n",
"| Experiment: 3 | Episode: 975 | Score: -123.0 | Avg Score: -99.97 |\n",
"| Experiment: 3 | Episode: 976 | Score: -81.42 | Avg Score: -100.47 |\n",
"| Experiment: 3 | Episode: 977 | Score: -120.63 | Avg Score: -100.66 |\n",
"| Experiment: 3 | Episode: 978 | Score: -64.41 | Avg Score: -100.71 |\n",
"| Experiment: 3 | Episode: 979 | Score: -93.48 | Avg Score: -100.81 |\n",
"| Experiment: 3 | Episode: 980 | Score: -187.77 | Avg Score: -102.07 |\n",
"| Experiment: 3 | Episode: 981 | Score: -68.96 | Avg Score: -101.47 |\n",
"| Experiment: 3 | Episode: 982 | Score: -69.08 | Avg Score: -100.1 |\n",
"| Experiment: 3 | Episode: 983 | Score: -120.27 | Avg Score: -100.82 |\n",
"| Experiment: 3 | Episode: 984 | Score: -69.05 | Avg Score: -100.29 |\n",
"| Experiment: 3 | Episode: 985 | Score: -102.1 | Avg Score: -100.38 |\n",
"| Experiment: 3 | Episode: 986 | Score: -137.45 | Avg Score: -100.95 |\n",
"| Experiment: 3 | Episode: 987 | Score: -58.18 | Avg Score: -100.78 |\n",
"| Experiment: 3 | Episode: 988 | Score: -42.01 | Avg Score: -100.47 |\n",
"| Experiment: 3 | Episode: 989 | Score: -156.01 | Avg Score: -101.4 |\n",
"| Experiment: 3 | Episode: 990 | Score: -108.83 | Avg Score: -101.49 |\n",
"| Experiment: 3 | Episode: 991 | Score: -114.12 | Avg Score: -100.89 |\n",
"| Experiment: 3 | Episode: 992 | Score: -89.16 | Avg Score: -100.47 |\n",
"| Experiment: 3 | Episode: 993 | Score: -374.4 | Avg Score: -103.23 |\n",
"| Experiment: 3 | Episode: 994 | Score: -66.36 | Avg Score: -103.18 |\n",
"| Experiment: 3 | Episode: 995 | Score: -90.59 | Avg Score: -102.33 |\n",
"| Experiment: 3 | Episode: 996 | Score: -81.78 | Avg Score: -102.08 |\n",
"| Experiment: 3 | Episode: 997 | Score: -104.17 | Avg Score: -101.69 |\n",
"| Experiment: 3 | Episode: 998 | Score: -301.81 | Avg Score: -103.22 |\n",
"| Experiment: 3 | Episode: 999 | Score: -80.65 | Avg Score: -103.2 |\n",
"| Experiment: 3 | Episode: 1000 | Score: -47.17 | Avg Score: -102.77 |\n",
"| Experiment: 3 | Episode: 1001 | Score: -98.43 | Avg Score: -102.01 |\n",
"| Experiment: 3 | Episode: 1002 | Score: -105.45 | Avg Score: -101.67 |\n",
"| Experiment: 3 | Episode: 1003 | Score: -42.1 | Avg Score: -101.01 |\n",
"| Experiment: 3 | Episode: 1004 | Score: -98.9 | Avg Score: -100.79 |\n",
"| Experiment: 3 | Episode: 1005 | Score: 15.1 | Avg Score: -99.82 |\n",
"| Experiment: 3 | Episode: 1006 | Score: -169.58 | Avg Score: -101.14 |\n",
"| Experiment: 3 | Episode: 1007 | Score: -45.92 | Avg Score: -100.69 |\n",
"| Experiment: 3 | Episode: 1008 | Score: -167.55 | Avg Score: -101.9 |\n",
"| Experiment: 3 | Episode: 1009 | Score: -261.47 | Avg Score: -102.15 |\n",
"| Experiment: 3 | Episode: 1010 | Score: -137.8 | Avg Score: -102.81 |\n",
"| Experiment: 3 | Episode: 1011 | Score: -189.67 | Avg Score: -104.44 |\n",
"| Experiment: 3 | Episode: 1012 | Score: -95.12 | Avg Score: -104.58 |\n",
"| Experiment: 3 | Episode: 1013 | Score: -85.46 | Avg Score: -103.65 |\n",
"| Experiment: 3 | Episode: 1014 | Score: -80.48 | Avg Score: -103.53 |\n",
"| Experiment: 3 | Episode: 1015 | Score: -183.62 | Avg Score: -104.84 |\n",
"| Experiment: 3 | Episode: 1016 | Score: -56.9 | Avg Score: -104.39 |\n",
"| Experiment: 3 | Episode: 1017 | Score: -206.64 | Avg Score: -105.66 |\n",
"| Experiment: 3 | Episode: 1018 | Score: -95.93 | Avg Score: -105.8 |\n",
"| Experiment: 3 | Episode: 1019 | Score: -86.7 | Avg Score: -105.11 |\n",
"| Experiment: 3 | Episode: 1020 | Score: -245.89 | Avg Score: -106.28 |\n",
"| Experiment: 3 | Episode: 1021 | Score: -114.96 | Avg Score: -106.44 |\n",
"| Experiment: 3 | Episode: 1022 | Score: -94.11 | Avg Score: -106.55 |\n",
"| Experiment: 3 | Episode: 1023 | Score: -53.8 | Avg Score: -106.67 |\n",
"| Experiment: 3 | Episode: 1024 | Score: -89.11 | Avg Score: -107.24 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 3 | Episode: 1025 | Score: -107.37 | Avg Score: -107.34 |\n",
"| Experiment: 3 | Episode: 1026 | Score: -93.64 | Avg Score: -107.55 |\n",
"| Experiment: 3 | Episode: 1027 | Score: -16.78 | Avg Score: -106.61 |\n",
"| Experiment: 3 | Episode: 1028 | Score: -107.89 | Avg Score: -106.4 |\n",
"| Experiment: 3 | Episode: 1029 | Score: -473.7 | Avg Score: -110.21 |\n",
"| Experiment: 3 | Episode: 1030 | Score: -82.4 | Avg Score: -110.07 |\n",
"| Experiment: 3 | Episode: 1031 | Score: -52.38 | Avg Score: -109.65 |\n",
"| Experiment: 3 | Episode: 1032 | Score: -122.97 | Avg Score: -108.95 |\n",
"| Experiment: 3 | Episode: 1033 | Score: -69.04 | Avg Score: -108.39 |\n",
"| Experiment: 3 | Episode: 1034 | Score: -88.06 | Avg Score: -108.05 |\n",
"| Experiment: 3 | Episode: 1035 | Score: -56.23 | Avg Score: -108.15 |\n",
"| Experiment: 3 | Episode: 1036 | Score: -14.94 | Avg Score: -107.18 |\n",
"| Experiment: 3 | Episode: 1037 | Score: -123.21 | Avg Score: -107.76 |\n",
"| Experiment: 3 | Episode: 1038 | Score: -88.92 | Avg Score: -107.98 |\n",
"| Experiment: 3 | Episode: 1039 | Score: -194.66 | Avg Score: -108.43 |\n",
"| Experiment: 3 | Episode: 1040 | Score: -122.22 | Avg Score: -108.54 |\n",
"| Experiment: 3 | Episode: 1041 | Score: -76.27 | Avg Score: -110.66 |\n",
"| Experiment: 3 | Episode: 1042 | Score: -75.77 | Avg Score: -110.46 |\n",
"| Experiment: 3 | Episode: 1043 | Score: -205.16 | Avg Score: -112.15 |\n",
"| Experiment: 3 | Episode: 1044 | Score: -69.69 | Avg Score: -110.85 |\n",
"| Experiment: 3 | Episode: 1045 | Score: -68.24 | Avg Score: -110.97 |\n",
"| Experiment: 3 | Episode: 1046 | Score: -211.52 | Avg Score: -112.26 |\n",
"| Experiment: 3 | Episode: 1047 | Score: -78.45 | Avg Score: -111.34 |\n",
"| Experiment: 3 | Episode: 1048 | Score: -109.9 | Avg Score: -111.12 |\n",
"| Experiment: 3 | Episode: 1049 | Score: -70.79 | Avg Score: -111.38 |\n",
"| Experiment: 3 | Episode: 1050 | Score: -256.37 | Avg Score: -113.1 |\n",
"| Experiment: 3 | Episode: 1051 | Score: -57.69 | Avg Score: -112.11 |\n",
"| Experiment: 3 | Episode: 1052 | Score: -46.97 | Avg Score: -112.67 |\n",
"| Experiment: 3 | Episode: 1053 | Score: -94.14 | Avg Score: -112.78 |\n",
"| Experiment: 3 | Episode: 1054 | Score: -48.9 | Avg Score: -111.62 |\n",
"| Experiment: 3 | Episode: 1055 | Score: -141.33 | Avg Score: -112.31 |\n",
"| Experiment: 3 | Episode: 1056 | Score: -116.69 | Avg Score: -112.59 |\n",
"| Experiment: 3 | Episode: 1057 | Score: -94.51 | Avg Score: -111.73 |\n",
"| Experiment: 3 | Episode: 1058 | Score: -195.38 | Avg Score: -112.17 |\n",
"| Experiment: 3 | Episode: 1059 | Score: -59.92 | Avg Score: -110.94 |\n",
"| Experiment: 3 | Episode: 1060 | Score: -124.86 | Avg Score: -111.2 |\n",
"| Experiment: 3 | Episode: 1061 | Score: -58.29 | Avg Score: -110.92 |\n",
"| Experiment: 3 | Episode: 1062 | Score: -108.54 | Avg Score: -110.77 |\n",
"| Experiment: 3 | Episode: 1063 | Score: -133.35 | Avg Score: -111.95 |\n",
"| Experiment: 3 | Episode: 1064 | Score: -137.98 | Avg Score: -112.39 |\n",
"| Experiment: 3 | Episode: 1065 | Score: -75.04 | Avg Score: -113.06 |\n",
"| Experiment: 3 | Episode: 1066 | Score: -192.44 | Avg Score: -113.83 |\n",
"| Experiment: 3 | Episode: 1067 | Score: -208.56 | Avg Score: -115.04 |\n",
"| Experiment: 3 | Episode: 1068 | Score: -61.05 | Avg Score: -113.0 |\n",
"| Experiment: 3 | Episode: 1069 | Score: -32.4 | Avg Score: -111.42 |\n",
"| Experiment: 3 | Episode: 1070 | Score: -133.98 | Avg Score: -111.48 |\n",
"| Experiment: 3 | Episode: 1071 | Score: -119.28 | Avg Score: -112.61 |\n",
"| Experiment: 3 | Episode: 1072 | Score: -98.01 | Avg Score: -112.91 |\n",
"| Experiment: 3 | Episode: 1073 | Score: -114.73 | Avg Score: -113.17 |\n",
"| Experiment: 3 | Episode: 1074 | Score: -99.31 | Avg Score: -113.33 |\n",
"| Experiment: 3 | Episode: 1075 | Score: -58.84 | Avg Score: -112.69 |\n",
"| Experiment: 3 | Episode: 1076 | Score: -145.28 | Avg Score: -113.33 |\n",
"| Experiment: 3 | Episode: 1077 | Score: 12.48 | Avg Score: -112.0 |\n",
"| Experiment: 3 | Episode: 1078 | Score: -177.93 | Avg Score: -113.13 |\n",
"| Experiment: 3 | Episode: 1079 | Score: -63.7 | Avg Score: -112.84 |\n",
"| Experiment: 3 | Episode: 1080 | Score: -216.14 | Avg Score: -113.12 |\n",
"| Experiment: 3 | Episode: 1081 | Score: -181.75 | Avg Score: -114.25 |\n",
"| Experiment: 3 | Episode: 1082 | Score: -155.49 | Avg Score: -115.11 |\n",
"| Experiment: 3 | Episode: 1083 | Score: -93.72 | Avg Score: -114.85 |\n",
"| Experiment: 3 | Episode: 1084 | Score: -106.47 | Avg Score: -115.22 |\n",
"| Experiment: 3 | Episode: 1085 | Score: -220.49 | Avg Score: -116.4 |\n",
"| Experiment: 3 | Episode: 1086 | Score: -226.04 | Avg Score: -117.29 |\n",
"| Experiment: 3 | Episode: 1087 | Score: -70.66 | Avg Score: -117.42 |\n",
"| Experiment: 3 | Episode: 1088 | Score: -79.63 | Avg Score: -117.79 |\n",
"| Experiment: 3 | Episode: 1089 | Score: -54.14 | Avg Score: -116.77 |\n",
"| Experiment: 3 | Episode: 1090 | Score: -200.4 | Avg Score: -117.69 |\n",
"| Experiment: 3 | Episode: 1091 | Score: -30.81 | Avg Score: -116.86 |\n",
"| Experiment: 3 | Episode: 1092 | Score: -65.58 | Avg Score: -116.62 |\n",
"| Experiment: 3 | Episode: 1093 | Score: -88.14 | Avg Score: -113.76 |\n",
"| Experiment: 3 | Episode: 1094 | Score: -128.62 | Avg Score: -114.38 |\n",
"| Experiment: 3 | Episode: 1095 | Score: -45.89 | Avg Score: -113.93 |\n",
"| Experiment: 3 | Episode: 1096 | Score: -94.6 | Avg Score: -114.06 |\n",
"| Experiment: 3 | Episode: 1097 | Score: -51.3 | Avg Score: -113.53 |\n",
"| Experiment: 3 | Episode: 1098 | Score: -70.94 | Avg Score: -111.22 |\n",
"| Experiment: 3 | Episode: 1099 | Score: -86.63 | Avg Score: -111.28 |\n",
"| Experiment: 3 | Episode: 1100 | Score: -75.25 | Avg Score: -111.56 |\n",
"| Experiment: 3 | Episode: 1101 | Score: -94.08 | Avg Score: -111.52 |\n",
"| Experiment: 3 | Episode: 1102 | Score: -76.66 | Avg Score: -111.23 |\n",
"| Experiment: 3 | Episode: 1103 | Score: -103.88 | Avg Score: -111.85 |\n",
"| Experiment: 3 | Episode: 1104 | Score: -206.11 | Avg Score: -112.92 |\n",
"| Experiment: 3 | Episode: 1105 | Score: -160.84 | Avg Score: -114.68 |\n",
"| Experiment: 3 | Episode: 1106 | Score: -61.59 | Avg Score: -113.6 |\n",
"| Experiment: 3 | Episode: 1107 | Score: -190.33 | Avg Score: -115.05 |\n",
"| Experiment: 3 | Episode: 1108 | Score: -141.42 | Avg Score: -114.78 |\n",
"| Experiment: 3 | Episode: 1109 | Score: -124.52 | Avg Score: -113.42 |\n",
"| Experiment: 3 | Episode: 1110 | Score: -158.94 | Avg Score: -113.63 |\n",
"| Experiment: 3 | Episode: 1111 | Score: -95.92 | Avg Score: -112.69 |\n",
"| Experiment: 3 | Episode: 1112 | Score: -154.36 | Avg Score: -113.28 |\n",
"| Experiment: 3 | Episode: 1113 | Score: -50.54 | Avg Score: -112.93 |\n",
"| Experiment: 3 | Episode: 1114 | Score: -125.78 | Avg Score: -113.39 |\n",
"| Experiment: 3 | Episode: 1115 | Score: -173.03 | Avg Score: -113.28 |\n",
"| Experiment: 3 | Episode: 1116 | Score: -86.93 | Avg Score: -113.58 |\n",
"| Experiment: 3 | Episode: 1117 | Score: -157.61 | Avg Score: -113.09 |\n",
"| Experiment: 3 | Episode: 1118 | Score: -236.04 | Avg Score: -114.49 |\n",
"| Experiment: 3 | Episode: 1119 | Score: -73.03 | Avg Score: -114.35 |\n",
"| Experiment: 3 | Episode: 1120 | Score: -127.03 | Avg Score: -113.17 |\n",
"| Experiment: 3 | Episode: 1121 | Score: -122.02 | Avg Score: -113.24 |\n",
"| Experiment: 3 | Episode: 1122 | Score: -51.28 | Avg Score: -112.81 |\n",
"| Experiment: 3 | Episode: 1123 | Score: -33.33 | Avg Score: -112.6 |\n",
"| Experiment: 3 | Episode: 1124 | Score: -115.99 | Avg Score: -112.87 |\n",
"| Experiment: 3 | Episode: 1125 | Score: -206.74 | Avg Score: -113.87 |\n",
"| Experiment: 3 | Episode: 1126 | Score: -191.36 | Avg Score: -114.84 |\n",
"| Experiment: 3 | Episode: 1127 | Score: -191.38 | Avg Score: -116.59 |\n",
"| Experiment: 3 | Episode: 1128 | Score: -166.03 | Avg Score: -117.17 |\n",
"| Experiment: 3 | Episode: 1129 | Score: -146.3 | Avg Score: -113.9 |\n",
"| Experiment: 3 | Episode: 1130 | Score: -95.93 | Avg Score: -114.03 |\n",
"| Experiment: 3 | Episode: 1131 | Score: -225.07 | Avg Score: -115.76 |\n",
"| Experiment: 3 | Episode: 1132 | Score: -81.85 | Avg Score: -115.35 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 3 | Episode: 1133 | Score: -22.53 | Avg Score: -114.88 |\n",
"| Experiment: 3 | Episode: 1134 | Score: -125.8 | Avg Score: -115.26 |\n",
"| Experiment: 3 | Episode: 1135 | Score: -220.31 | Avg Score: -116.9 |\n",
"| Experiment: 3 | Episode: 1136 | Score: -46.36 | Avg Score: -117.21 |\n",
"| Experiment: 3 | Episode: 1137 | Score: -342.74 | Avg Score: -119.41 |\n",
"| Experiment: 3 | Episode: 1138 | Score: -144.82 | Avg Score: -119.97 |\n",
"| Experiment: 3 | Episode: 1139 | Score: -102.55 | Avg Score: -119.05 |\n",
"| Experiment: 3 | Episode: 1140 | Score: -90.62 | Avg Score: -118.73 |\n",
"| Experiment: 3 | Episode: 1141 | Score: -50.92 | Avg Score: -118.48 |\n",
"| Experiment: 3 | Episode: 1142 | Score: -68.0 | Avg Score: -118.4 |\n",
"| Experiment: 3 | Episode: 1143 | Score: -82.86 | Avg Score: -117.18 |\n",
"| Experiment: 3 | Episode: 1144 | Score: -50.16 | Avg Score: -116.98 |\n",
"| Experiment: 3 | Episode: 1145 | Score: -46.34 | Avg Score: -116.76 |\n",
"| Experiment: 3 | Episode: 1146 | Score: -70.94 | Avg Score: -115.36 |\n",
"| Experiment: 3 | Episode: 1147 | Score: -81.34 | Avg Score: -115.39 |\n",
"| Experiment: 3 | Episode: 1148 | Score: -74.39 | Avg Score: -115.03 |\n",
"| Experiment: 3 | Episode: 1149 | Score: -77.87 | Avg Score: -115.1 |\n",
"| Experiment: 3 | Episode: 1150 | Score: -119.51 | Avg Score: -113.73 |\n",
"| Experiment: 3 | Episode: 1151 | Score: -85.69 | Avg Score: -114.01 |\n",
"| Experiment: 3 | Episode: 1152 | Score: -135.94 | Avg Score: -114.9 |\n",
"| Experiment: 3 | Episode: 1153 | Score: -66.45 | Avg Score: -114.63 |\n",
"| Experiment: 3 | Episode: 1154 | Score: -102.64 | Avg Score: -115.16 |\n",
"| Experiment: 3 | Episode: 1155 | Score: -30.5 | Avg Score: -114.06 |\n",
"| Experiment: 3 | Episode: 1156 | Score: -66.62 | Avg Score: -113.55 |\n",
"| Experiment: 3 | Episode: 1157 | Score: -87.08 | Avg Score: -113.48 |\n",
"| Experiment: 3 | Episode: 1158 | Score: -134.28 | Avg Score: -112.87 |\n",
"| Experiment: 3 | Episode: 1159 | Score: -69.91 | Avg Score: -112.97 |\n",
"| Experiment: 3 | Episode: 1160 | Score: -65.56 | Avg Score: -112.38 |\n",
"| Experiment: 3 | Episode: 1161 | Score: -139.21 | Avg Score: -113.19 |\n",
"| Experiment: 3 | Episode: 1162 | Score: -67.4 | Avg Score: -112.77 |\n",
"| Experiment: 3 | Episode: 1163 | Score: -85.35 | Avg Score: -112.29 |\n",
"| Experiment: 3 | Episode: 1164 | Score: -79.8 | Avg Score: -111.71 |\n",
"| Experiment: 3 | Episode: 1165 | Score: -90.22 | Avg Score: -111.86 |\n",
"| Experiment: 3 | Episode: 1166 | Score: -98.56 | Avg Score: -110.92 |\n",
"| Experiment: 3 | Episode: 1167 | Score: -47.2 | Avg Score: -109.31 |\n",
"| Experiment: 3 | Episode: 1168 | Score: -124.66 | Avg Score: -109.95 |\n",
"| Experiment: 3 | Episode: 1169 | Score: -110.79 | Avg Score: -110.73 |\n",
"| Experiment: 3 | Episode: 1170 | Score: -83.19 | Avg Score: -110.22 |\n",
"| Experiment: 3 | Episode: 1171 | Score: -57.1 | Avg Score: -109.6 |\n",
"| Experiment: 3 | Episode: 1172 | Score: -74.8 | Avg Score: -109.37 |\n",
"| Experiment: 3 | Episode: 1173 | Score: -86.21 | Avg Score: -109.08 |\n",
"| Experiment: 3 | Episode: 1174 | Score: -39.76 | Avg Score: -108.49 |\n",
"| Experiment: 3 | Episode: 1175 | Score: -85.31 | Avg Score: -108.75 |\n",
"| Experiment: 3 | Episode: 1176 | Score: -207.4 | Avg Score: -109.37 |\n",
"| Experiment: 3 | Episode: 1177 | Score: -117.85 | Avg Score: -110.68 |\n",
"| Experiment: 3 | Episode: 1178 | Score: -65.1 | Avg Score: -109.55 |\n",
"| Experiment: 3 | Episode: 1179 | Score: -105.73 | Avg Score: -109.97 |\n",
"| Experiment: 3 | Episode: 1180 | Score: 26.04 | Avg Score: -107.55 |\n",
"| Experiment: 3 | Episode: 1181 | Score: -181.33 | Avg Score: -107.54 |\n",
"| Experiment: 3 | Episode: 1182 | Score: -44.51 | Avg Score: -106.43 |\n",
"| Experiment: 3 | Episode: 1183 | Score: -136.99 | Avg Score: -106.87 |\n",
"| Experiment: 3 | Episode: 1184 | Score: -132.23 | Avg Score: -107.12 |\n",
"| Experiment: 3 | Episode: 1185 | Score: -88.51 | Avg Score: -105.8 |\n",
"| Experiment: 3 | Episode: 1186 | Score: -156.17 | Avg Score: -105.11 |\n",
"| Experiment: 3 | Episode: 1187 | Score: -243.35 | Avg Score: -106.83 |\n",
"| Experiment: 3 | Episode: 1188 | Score: -79.0 | Avg Score: -106.83 |\n",
"| Experiment: 3 | Episode: 1189 | Score: -103.54 | Avg Score: -107.32 |\n",
"| Experiment: 3 | Episode: 1190 | Score: -187.15 | Avg Score: -107.19 |\n",
"| Experiment: 3 | Episode: 1191 | Score: -126.14 | Avg Score: -108.14 |\n",
"| Experiment: 3 | Episode: 1192 | Score: -170.79 | Avg Score: -109.19 |\n",
"| Experiment: 3 | Episode: 1193 | Score: -151.75 | Avg Score: -109.83 |\n",
"| Experiment: 3 | Episode: 1194 | Score: -112.61 | Avg Score: -109.67 |\n",
"| Experiment: 3 | Episode: 1195 | Score: -155.27 | Avg Score: -110.76 |\n",
"| Experiment: 3 | Episode: 1196 | Score: -71.45 | Avg Score: -110.53 |\n",
"| Experiment: 3 | Episode: 1197 | Score: -69.67 | Avg Score: -110.72 |\n",
"| Experiment: 3 | Episode: 1198 | Score: -50.17 | Avg Score: -110.51 |\n",
"| Experiment: 3 | Episode: 1199 | Score: -88.25 | Avg Score: -110.52 |\n",
"| Experiment: 3 | Episode: 1200 | Score: -67.07 | Avg Score: -110.44 |\n",
"| Experiment: 3 | Episode: 1201 | Score: -134.52 | Avg Score: -110.85 |\n",
"| Experiment: 3 | Episode: 1202 | Score: -45.79 | Avg Score: -110.54 |\n",
"| Experiment: 3 | Episode: 1203 | Score: -95.84 | Avg Score: -110.46 |\n",
"| Experiment: 3 | Episode: 1204 | Score: -88.47 | Avg Score: -109.28 |\n",
"| Experiment: 3 | Episode: 1205 | Score: -114.68 | Avg Score: -108.82 |\n",
"| Experiment: 3 | Episode: 1206 | Score: -27.31 | Avg Score: -108.48 |\n",
"| Experiment: 3 | Episode: 1207 | Score: -155.58 | Avg Score: -108.13 |\n",
"| Experiment: 3 | Episode: 1208 | Score: -169.9 | Avg Score: -108.41 |\n",
"| Experiment: 3 | Episode: 1209 | Score: -136.15 | Avg Score: -108.53 |\n",
"| Experiment: 3 | Episode: 1210 | Score: -74.87 | Avg Score: -107.69 |\n",
"| Experiment: 3 | Episode: 1211 | Score: 0.27 | Avg Score: -106.73 |\n",
"| Experiment: 3 | Episode: 1212 | Score: -149.1 | Avg Score: -106.67 |\n",
"| Experiment: 3 | Episode: 1213 | Score: -67.2 | Avg Score: -106.84 |\n",
"| Experiment: 3 | Episode: 1214 | Score: -49.11 | Avg Score: -106.07 |\n",
"| Experiment: 3 | Episode: 1215 | Score: -92.05 | Avg Score: -105.27 |\n",
"| Experiment: 3 | Episode: 1216 | Score: -74.76 | Avg Score: -105.14 |\n",
"| Experiment: 3 | Episode: 1217 | Score: -152.65 | Avg Score: -105.09 |\n",
"| Experiment: 3 | Episode: 1218 | Score: -118.48 | Avg Score: -103.92 |\n",
"| Experiment: 3 | Episode: 1219 | Score: -118.01 | Avg Score: -104.37 |\n",
"| Experiment: 3 | Episode: 1220 | Score: -55.73 | Avg Score: -103.65 |\n",
"| Experiment: 3 | Episode: 1221 | Score: -43.79 | Avg Score: -102.87 |\n",
"| Experiment: 3 | Episode: 1222 | Score: -217.75 | Avg Score: -104.54 |\n",
"| Experiment: 3 | Episode: 1223 | Score: -156.27 | Avg Score: -105.77 |\n",
"| Experiment: 3 | Episode: 1224 | Score: -88.95 | Avg Score: -105.5 |\n",
"| Experiment: 3 | Episode: 1225 | Score: -103.81 | Avg Score: -104.47 |\n",
"| Experiment: 3 | Episode: 1226 | Score: 55.15 | Avg Score: -102.0 |\n",
"| Experiment: 3 | Episode: 1227 | Score: -160.2 | Avg Score: -101.69 |\n",
"| Experiment: 3 | Episode: 1228 | Score: -85.84 | Avg Score: -100.89 |\n",
"| Experiment: 3 | Episode: 1229 | Score: -53.14 | Avg Score: -99.96 |\n",
"| Experiment: 3 | Episode: 1230 | Score: -151.82 | Avg Score: -100.52 |\n",
"| Experiment: 3 | Episode: 1231 | Score: -70.58 | Avg Score: -98.97 |\n",
"| Experiment: 3 | Episode: 1232 | Score: -106.98 | Avg Score: -99.22 |\n",
"| Experiment: 3 | Episode: 1233 | Score: -118.97 | Avg Score: -100.19 |\n",
"| Experiment: 3 | Episode: 1234 | Score: -135.81 | Avg Score: -100.29 |\n",
"| Experiment: 3 | Episode: 1235 | Score: -38.12 | Avg Score: -98.46 |\n",
"| Experiment: 3 | Episode: 1236 | Score: -41.92 | Avg Score: -98.42 |\n",
"| Experiment: 3 | Episode: 1237 | Score: -35.41 | Avg Score: -95.35 |\n",
"| Experiment: 3 | Episode: 1238 | Score: -100.6 | Avg Score: -94.9 |\n",
"| Experiment: 3 | Episode: 1239 | Score: -214.69 | Avg Score: -96.03 |\n",
"| Experiment: 3 | Episode: 1240 | Score: -71.8 | Avg Score: -95.84 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 3 | Episode: 1241 | Score: -162.44 | Avg Score: -96.95 |\n",
"| Experiment: 3 | Episode: 1242 | Score: -85.83 | Avg Score: -97.13 |\n",
"| Experiment: 3 | Episode: 1243 | Score: -82.4 | Avg Score: -97.13 |\n",
"| Experiment: 3 | Episode: 1244 | Score: -93.36 | Avg Score: -97.56 |\n",
"| Experiment: 3 | Episode: 1245 | Score: -99.59 | Avg Score: -98.09 |\n",
"| Experiment: 3 | Episode: 1246 | Score: -48.01 | Avg Score: -97.86 |\n",
"| Experiment: 3 | Episode: 1247 | Score: -227.5 | Avg Score: -99.32 |\n",
"| Experiment: 3 | Episode: 1248 | Score: -373.14 | Avg Score: -102.31 |\n",
"| Experiment: 3 | Episode: 1249 | Score: -38.06 | Avg Score: -101.91 |\n",
"| Experiment: 3 | Episode: 1250 | Score: 9.5 | Avg Score: -100.62 |\n",
"| Experiment: 3 | Episode: 1251 | Score: -42.45 | Avg Score: -100.19 |\n",
"| Experiment: 3 | Episode: 1252 | Score: -81.67 | Avg Score: -99.65 |\n",
"| Experiment: 3 | Episode: 1253 | Score: -115.67 | Avg Score: -100.14 |\n",
"| Experiment: 3 | Episode: 1254 | Score: -131.92 | Avg Score: -100.43 |\n",
"| Experiment: 3 | Episode: 1255 | Score: -39.1 | Avg Score: -100.52 |\n",
"| Experiment: 3 | Episode: 1256 | Score: -69.81 | Avg Score: -100.55 |\n",
"| Experiment: 3 | Episode: 1257 | Score: -79.4 | Avg Score: -100.47 |\n",
"| Experiment: 3 | Episode: 1258 | Score: -82.06 | Avg Score: -99.95 |\n",
"| Experiment: 3 | Episode: 1259 | Score: -63.56 | Avg Score: -99.89 |\n",
"| Experiment: 3 | Episode: 1260 | Score: -49.97 | Avg Score: -99.73 |\n",
"| Experiment: 3 | Episode: 1261 | Score: -125.24 | Avg Score: -99.59 |\n",
"| Experiment: 3 | Episode: 1262 | Score: -76.37 | Avg Score: -99.68 |\n",
"| Experiment: 3 | Episode: 1263 | Score: -103.72 | Avg Score: -99.87 |\n",
"| Experiment: 3 | Episode: 1264 | Score: -34.62 | Avg Score: -99.41 |\n",
"| Experiment: 3 | Episode: 1265 | Score: -96.57 | Avg Score: -99.48 |\n",
"| Experiment: 3 | Episode: 1266 | Score: -185.8 | Avg Score: -100.35 |\n",
"| Experiment: 3 | Episode: 1267 | Score: -137.22 | Avg Score: -101.25 |\n",
"| Experiment: 3 | Episode: 1268 | Score: -67.5 | Avg Score: -100.68 |\n",
"| Experiment: 3 | Episode: 1269 | Score: -155.83 | Avg Score: -101.13 |\n",
"| Experiment: 3 | Episode: 1270 | Score: -28.41 | Avg Score: -100.58 |\n",
"| Experiment: 3 | Episode: 1271 | Score: -100.68 | Avg Score: -101.02 |\n",
"| Experiment: 3 | Episode: 1272 | Score: -117.05 | Avg Score: -101.44 |\n",
"| Experiment: 3 | Episode: 1273 | Score: -96.34 | Avg Score: -101.54 |\n",
"| Experiment: 3 | Episode: 1274 | Score: -35.03 | Avg Score: -101.49 |\n",
"| Experiment: 3 | Episode: 1275 | Score: -74.6 | Avg Score: -101.39 |\n",
"| Experiment: 3 | Episode: 1276 | Score: -84.29 | Avg Score: -100.16 |\n",
"| Experiment: 3 | Episode: 1277 | Score: -119.96 | Avg Score: -100.18 |\n",
"| Experiment: 3 | Episode: 1278 | Score: -109.0 | Avg Score: -100.62 |\n",
"| Experiment: 3 | Episode: 1279 | Score: -51.21 | Avg Score: -100.07 |\n",
"| Experiment: 3 | Episode: 1280 | Score: -109.4 | Avg Score: -101.42 |\n",
"| Experiment: 3 | Episode: 1281 | Score: -54.94 | Avg Score: -100.16 |\n",
"| Experiment: 3 | Episode: 1282 | Score: -49.23 | Avg Score: -100.21 |\n",
"| Experiment: 3 | Episode: 1283 | Score: -171.79 | Avg Score: -100.56 |\n",
"| Experiment: 3 | Episode: 1284 | Score: -77.51 | Avg Score: -100.01 |\n",
"| Experiment: 3 | Episode: 1285 | Score: -75.16 | Avg Score: -99.88 |\n",
"| Experiment: 3 | Episode: 1286 | Score: -226.15 | Avg Score: -100.58 |\n",
"| Experiment: 3 | Episode: 1287 | Score: -73.94 | Avg Score: -98.88 |\n",
"| Experiment: 3 | Episode: 1288 | Score: -96.66 | Avg Score: -99.06 |\n",
"| Experiment: 3 | Episode: 1289 | Score: -20.55 | Avg Score: -98.23 |\n",
"| Experiment: 3 | Episode: 1290 | Score: -204.79 | Avg Score: -98.4 |\n",
"| Experiment: 3 | Episode: 1291 | Score: -80.8 | Avg Score: -97.95 |\n",
"| Experiment: 3 | Episode: 1292 | Score: -58.5 | Avg Score: -96.83 |\n",
"| Experiment: 3 | Episode: 1293 | Score: -143.26 | Avg Score: -96.74 |\n",
"| Experiment: 3 | Episode: 1294 | Score: -100.58 | Avg Score: -96.62 |\n",
"| Experiment: 3 | Episode: 1295 | Score: -122.16 | Avg Score: -96.29 |\n",
"| Experiment: 3 | Episode: 1296 | Score: -62.51 | Avg Score: -96.2 |\n",
"| Experiment: 3 | Episode: 1297 | Score: -119.44 | Avg Score: -96.7 |\n",
"| Experiment: 3 | Episode: 1298 | Score: -41.97 | Avg Score: -96.62 |\n",
"| Experiment: 3 | Episode: 1299 | Score: -87.58 | Avg Score: -96.61 |\n",
"| Experiment: 3 | Episode: 1300 | Score: -59.8 | Avg Score: -96.54 |\n",
"| Experiment: 3 | Episode: 1301 | Score: -26.09 | Avg Score: -95.45 |\n",
"| Experiment: 3 | Episode: 1302 | Score: -172.84 | Avg Score: -96.72 |\n",
"| Experiment: 3 | Episode: 1303 | Score: -112.55 | Avg Score: -96.89 |\n",
"| Experiment: 3 | Episode: 1304 | Score: -86.1 | Avg Score: -96.87 |\n",
"| Experiment: 3 | Episode: 1305 | Score: -46.41 | Avg Score: -96.19 |\n",
"| Experiment: 3 | Episode: 1306 | Score: -476.28 | Avg Score: -100.67 |\n",
"| Experiment: 3 | Episode: 1307 | Score: -49.46 | Avg Score: -99.61 |\n",
"| Experiment: 3 | Episode: 1308 | Score: -178.32 | Avg Score: -99.7 |\n",
"| Experiment: 3 | Episode: 1309 | Score: -402.91 | Avg Score: -102.37 |\n",
"| Experiment: 3 | Episode: 1310 | Score: -102.61 | Avg Score: -102.64 |\n",
"| Experiment: 3 | Episode: 1311 | Score: -74.5 | Avg Score: -103.39 |\n",
"| Experiment: 3 | Episode: 1312 | Score: -105.8 | Avg Score: -102.96 |\n",
"| Experiment: 3 | Episode: 1313 | Score: -66.0 | Avg Score: -102.95 |\n",
"| Experiment: 3 | Episode: 1314 | Score: -68.69 | Avg Score: -103.14 |\n",
"| Experiment: 3 | Episode: 1315 | Score: -110.7 | Avg Score: -103.33 |\n",
"| Experiment: 3 | Episode: 1316 | Score: -104.94 | Avg Score: -103.63 |\n",
"| Experiment: 3 | Episode: 1317 | Score: -125.44 | Avg Score: -103.36 |\n",
"| Experiment: 3 | Episode: 1318 | Score: -84.04 | Avg Score: -103.01 |\n",
"| Experiment: 3 | Episode: 1319 | Score: -6.23 | Avg Score: -101.9 |\n",
"| Experiment: 3 | Episode: 1320 | Score: -53.95 | Avg Score: -101.88 |\n",
"| Experiment: 3 | Episode: 1321 | Score: -41.32 | Avg Score: -101.85 |\n",
"| Experiment: 3 | Episode: 1322 | Score: -93.84 | Avg Score: -100.61 |\n",
"| Experiment: 3 | Episode: 1323 | Score: -89.48 | Avg Score: -99.95 |\n",
"| Experiment: 3 | Episode: 1324 | Score: -64.38 | Avg Score: -99.7 |\n",
"| Experiment: 3 | Episode: 1325 | Score: -107.02 | Avg Score: -99.73 |\n",
"| Experiment: 3 | Episode: 1326 | Score: -77.81 | Avg Score: -101.06 |\n",
"| Experiment: 3 | Episode: 1327 | Score: -109.05 | Avg Score: -100.55 |\n",
"| Experiment: 3 | Episode: 1328 | Score: -115.3 | Avg Score: -100.84 |\n",
"| Experiment: 3 | Episode: 1329 | Score: -55.57 | Avg Score: -100.87 |\n",
"| Experiment: 3 | Episode: 1330 | Score: -203.7 | Avg Score: -101.39 |\n",
"| Experiment: 3 | Episode: 1331 | Score: -74.15 | Avg Score: -101.42 |\n",
"| Experiment: 3 | Episode: 1332 | Score: -89.55 | Avg Score: -101.25 |\n",
"| Experiment: 3 | Episode: 1333 | Score: -80.01 | Avg Score: -100.86 |\n",
"| Experiment: 3 | Episode: 1334 | Score: -66.36 | Avg Score: -100.17 |\n",
"| Experiment: 3 | Episode: 1335 | Score: -107.8 | Avg Score: -100.86 |\n",
"| Experiment: 3 | Episode: 1336 | Score: -70.47 | Avg Score: -101.15 |\n",
"| Experiment: 3 | Episode: 1337 | Score: -70.77 | Avg Score: -101.5 |\n",
"| Experiment: 3 | Episode: 1338 | Score: -255.35 | Avg Score: -103.05 |\n",
"| Experiment: 3 | Episode: 1339 | Score: -77.21 | Avg Score: -101.67 |\n",
"| Experiment: 3 | Episode: 1340 | Score: -101.26 | Avg Score: -101.97 |\n",
"| Experiment: 3 | Episode: 1341 | Score: 119.6 | Avg Score: -99.15 |\n",
"| Experiment: 3 | Episode: 1342 | Score: -41.44 | Avg Score: -98.7 |\n",
"| Experiment: 3 | Episode: 1343 | Score: -60.27 | Avg Score: -98.48 |\n",
"| Experiment: 3 | Episode: 1344 | Score: -43.78 | Avg Score: -97.99 |\n",
"| Experiment: 3 | Episode: 1345 | Score: -102.55 | Avg Score: -98.02 |\n",
"| Experiment: 3 | Episode: 1346 | Score: -215.13 | Avg Score: -99.69 |\n",
"| Experiment: 3 | Episode: 1347 | Score: -56.93 | Avg Score: -97.98 |\n",
"| Experiment: 3 | Episode: 1348 | Score: -46.95 | Avg Score: -94.72 |\n",
"| Experiment: 3 | Episode: 1349 | Score: -135.33 | Avg Score: -95.69 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 3 | Episode: 1350 | Score: -165.8 | Avg Score: -97.45 |\n",
"| Experiment: 3 | Episode: 1351 | Score: -38.46 | Avg Score: -97.41 |\n",
"| Experiment: 3 | Episode: 1352 | Score: -160.16 | Avg Score: -98.19 |\n",
"| Experiment: 3 | Episode: 1353 | Score: -27.09 | Avg Score: -97.31 |\n",
"| Experiment: 3 | Episode: 1354 | Score: -78.02 | Avg Score: -96.77 |\n",
"| Experiment: 3 | Episode: 1355 | Score: -108.04 | Avg Score: -97.46 |\n",
"| Experiment: 3 | Episode: 1356 | Score: -52.67 | Avg Score: -97.28 |\n",
"| Experiment: 3 | Episode: 1357 | Score: -51.86 | Avg Score: -97.01 |\n",
"| Experiment: 3 | Episode: 1358 | Score: -43.92 | Avg Score: -96.63 |\n",
"| Experiment: 3 | Episode: 1359 | Score: -102.14 | Avg Score: -97.01 |\n",
"| Experiment: 3 | Episode: 1360 | Score: -108.03 | Avg Score: -97.59 |\n",
"| Experiment: 3 | Episode: 1361 | Score: -67.77 | Avg Score: -97.02 |\n",
"| Experiment: 3 | Episode: 1362 | Score: -48.5 | Avg Score: -96.74 |\n",
"| Experiment: 3 | Episode: 1363 | Score: -92.61 | Avg Score: -96.63 |\n",
"| Experiment: 3 | Episode: 1364 | Score: -96.08 | Avg Score: -97.24 |\n",
"| Experiment: 3 | Episode: 1365 | Score: -57.29 | Avg Score: -96.85 |\n",
"| Experiment: 3 | Episode: 1366 | Score: -94.38 | Avg Score: -95.94 |\n",
"| Experiment: 3 | Episode: 1367 | Score: -75.64 | Avg Score: -95.32 |\n",
"| Experiment: 3 | Episode: 1368 | Score: -92.11 | Avg Score: -95.57 |\n",
"| Experiment: 3 | Episode: 1369 | Score: -90.79 | Avg Score: -94.92 |\n",
"| Experiment: 3 | Episode: 1370 | Score: -62.12 | Avg Score: -95.25 |\n",
"| Experiment: 3 | Episode: 1371 | Score: -58.76 | Avg Score: -94.84 |\n",
"| Experiment: 3 | Episode: 1372 | Score: -92.26 | Avg Score: -94.59 |\n",
"| Experiment: 3 | Episode: 1373 | Score: -88.64 | Avg Score: -94.51 |\n",
"| Experiment: 3 | Episode: 1374 | Score: -132.62 | Avg Score: -95.49 |\n",
"| Experiment: 3 | Episode: 1375 | Score: -70.52 | Avg Score: -95.45 |\n",
"| Experiment: 3 | Episode: 1376 | Score: -73.92 | Avg Score: -95.34 |\n",
"| Experiment: 3 | Episode: 1377 | Score: -10.29 | Avg Score: -94.24 |\n",
"| Experiment: 3 | Episode: 1378 | Score: -71.84 | Avg Score: -93.87 |\n",
"| Experiment: 3 | Episode: 1379 | Score: -30.34 | Avg Score: -93.66 |\n",
"| Experiment: 3 | Episode: 1380 | Score: -85.54 | Avg Score: -93.43 |\n",
"| Experiment: 3 | Episode: 1381 | Score: -59.97 | Avg Score: -93.48 |\n",
"| Experiment: 3 | Episode: 1382 | Score: -33.72 | Avg Score: -93.32 |\n",
"| Experiment: 3 | Episode: 1383 | Score: -82.33 | Avg Score: -92.43 |\n",
"| Experiment: 3 | Episode: 1384 | Score: 22.29 | Avg Score: -91.43 |\n",
"| Experiment: 3 | Episode: 1385 | Score: -81.97 | Avg Score: -91.5 |\n",
"| Experiment: 3 | Episode: 1386 | Score: -33.87 | Avg Score: -89.57 |\n",
"| Experiment: 3 | Episode: 1387 | Score: -60.66 | Avg Score: -89.44 |\n",
"| Experiment: 3 | Episode: 1388 | Score: -32.49 | Avg Score: -88.8 |\n",
"| Experiment: 3 | Episode: 1389 | Score: 4.92 | Avg Score: -88.54 |\n",
"| Experiment: 3 | Episode: 1390 | Score: -96.28 | Avg Score: -87.46 |\n",
"| Experiment: 3 | Episode: 1391 | Score: -39.63 | Avg Score: -87.05 |\n",
"| Experiment: 3 | Episode: 1392 | Score: -74.87 | Avg Score: -87.21 |\n",
"| Experiment: 3 | Episode: 1393 | Score: -101.65 | Avg Score: -86.8 |\n",
"| Experiment: 3 | Episode: 1394 | Score: -63.34 | Avg Score: -86.42 |\n",
"| Experiment: 3 | Episode: 1395 | Score: -36.2 | Avg Score: -85.56 |\n",
"| Experiment: 3 | Episode: 1396 | Score: -81.74 | Avg Score: -85.76 |\n",
"| Experiment: 3 | Episode: 1397 | Score: -57.05 | Avg Score: -85.13 |\n",
"| Experiment: 3 | Episode: 1398 | Score: -34.13 | Avg Score: -85.05 |\n",
"| Experiment: 3 | Episode: 1399 | Score: -10.21 | Avg Score: -84.28 |\n",
"| Experiment: 3 | Episode: 1400 | Score: -56.26 | Avg Score: -84.24 |\n",
"| Experiment: 3 | Episode: 1401 | Score: -32.82 | Avg Score: -84.31 |\n",
"| Experiment: 3 | Episode: 1402 | Score: -88.63 | Avg Score: -83.47 |\n",
"| Experiment: 3 | Episode: 1403 | Score: -93.08 | Avg Score: -83.28 |\n",
"| Experiment: 3 | Episode: 1404 | Score: -97.12 | Avg Score: -83.39 |\n",
"| Experiment: 3 | Episode: 1405 | Score: 18.09 | Avg Score: -82.74 |\n",
"| Experiment: 3 | Episode: 1406 | Score: -63.56 | Avg Score: -78.61 |\n",
"| Experiment: 3 | Episode: 1407 | Score: -34.06 | Avg Score: -78.46 |\n",
"| Experiment: 3 | Episode: 1408 | Score: -88.39 | Avg Score: -77.56 |\n",
"| Experiment: 3 | Episode: 1409 | Score: -76.13 | Avg Score: -74.29 |\n",
"| Experiment: 3 | Episode: 1410 | Score: -88.83 | Avg Score: -74.15 |\n",
"| Experiment: 3 | Episode: 1411 | Score: -35.67 | Avg Score: -73.77 |\n",
"| Experiment: 3 | Episode: 1412 | Score: -52.02 | Avg Score: -73.23 |\n",
"| Experiment: 3 | Episode: 1413 | Score: -65.31 | Avg Score: -73.22 |\n",
"| Experiment: 3 | Episode: 1414 | Score: -51.72 | Avg Score: -73.05 |\n",
"| Experiment: 3 | Episode: 1415 | Score: -63.86 | Avg Score: -72.58 |\n",
"| Experiment: 3 | Episode: 1416 | Score: -107.76 | Avg Score: -72.61 |\n",
"| Experiment: 3 | Episode: 1417 | Score: -55.32 | Avg Score: -71.91 |\n",
"| Experiment: 3 | Episode: 1418 | Score: -146.01 | Avg Score: -72.53 |\n",
"| Experiment: 3 | Episode: 1419 | Score: -28.93 | Avg Score: -72.76 |\n",
"| Experiment: 3 | Episode: 1420 | Score: -62.79 | Avg Score: -72.85 |\n",
"| Experiment: 3 | Episode: 1421 | Score: -77.92 | Avg Score: -73.21 |\n",
"| Experiment: 3 | Episode: 1422 | Score: -99.22 | Avg Score: -73.26 |\n",
"| Experiment: 3 | Episode: 1423 | Score: -68.35 | Avg Score: -73.05 |\n",
"| Experiment: 3 | Episode: 1424 | Score: -87.73 | Avg Score: -73.29 |\n",
"| Experiment: 3 | Episode: 1425 | Score: -47.65 | Avg Score: -72.69 |\n",
"| Experiment: 3 | Episode: 1426 | Score: -76.12 | Avg Score: -72.68 |\n",
"| Experiment: 3 | Episode: 1427 | Score: -75.56 | Avg Score: -72.34 |\n",
"| Experiment: 3 | Episode: 1428 | Score: -213.14 | Avg Score: -73.32 |\n",
"| Experiment: 3 | Episode: 1429 | Score: -64.91 | Avg Score: -73.41 |\n",
"| Experiment: 3 | Episode: 1430 | Score: -86.4 | Avg Score: -72.24 |\n",
"| Experiment: 3 | Episode: 1431 | Score: -43.22 | Avg Score: -71.93 |\n",
"| Experiment: 3 | Episode: 1432 | Score: -67.74 | Avg Score: -71.71 |\n",
"| Experiment: 3 | Episode: 1433 | Score: -61.51 | Avg Score: -71.53 |\n",
"| Experiment: 3 | Episode: 1434 | Score: -51.32 | Avg Score: -71.38 |\n",
"| Experiment: 3 | Episode: 1435 | Score: -122.84 | Avg Score: -71.53 |\n",
"| Experiment: 3 | Episode: 1436 | Score: -198.99 | Avg Score: -72.81 |\n",
"| Experiment: 3 | Episode: 1437 | Score: -112.56 | Avg Score: -73.23 |\n",
"| Experiment: 3 | Episode: 1438 | Score: -35.52 | Avg Score: -71.03 |\n",
"| Experiment: 3 | Episode: 1439 | Score: -82.54 | Avg Score: -71.09 |\n",
"| Experiment: 3 | Episode: 1440 | Score: -235.24 | Avg Score: -72.43 |\n",
"| Experiment: 3 | Episode: 1441 | Score: -65.05 | Avg Score: -74.27 |\n",
"| Experiment: 3 | Episode: 1442 | Score: -68.57 | Avg Score: -74.54 |\n",
"| Experiment: 3 | Episode: 1443 | Score: 163.91 | Avg Score: -72.3 |\n",
"| Experiment: 3 | Episode: 1444 | Score: -58.47 | Avg Score: -72.45 |\n",
"| Experiment: 3 | Episode: 1445 | Score: -107.64 | Avg Score: -72.5 |\n",
"| Experiment: 3 | Episode: 1446 | Score: -122.03 | Avg Score: -71.57 |\n",
"| Experiment: 3 | Episode: 1447 | Score: -157.36 | Avg Score: -72.57 |\n",
"| Experiment: 3 | Episode: 1448 | Score: -179.7 | Avg Score: -73.9 |\n",
"| Experiment: 3 | Episode: 1449 | Score: -33.17 | Avg Score: -72.88 |\n",
"| Experiment: 3 | Episode: 1450 | Score: -110.62 | Avg Score: -72.33 |\n",
"| Experiment: 3 | Episode: 1451 | Score: -69.07 | Avg Score: -72.63 |\n",
"| Experiment: 3 | Episode: 1452 | Score: -90.17 | Avg Score: -71.93 |\n",
"| Experiment: 3 | Episode: 1453 | Score: -57.95 | Avg Score: -72.24 |\n",
"| Experiment: 3 | Episode: 1454 | Score: -206.25 | Avg Score: -73.52 |\n",
"| Experiment: 3 | Episode: 1455 | Score: -40.94 | Avg Score: -72.85 |\n",
"| Experiment: 3 | Episode: 1456 | Score: -45.76 | Avg Score: -72.78 |\n",
"| Experiment: 3 | Episode: 1457 | Score: -127.17 | Avg Score: -73.54 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 3 | Episode: 1458 | Score: -115.48 | Avg Score: -74.25 |\n",
"| Experiment: 3 | Episode: 1459 | Score: -264.41 | Avg Score: -75.88 |\n",
"| Experiment: 3 | Episode: 1460 | Score: -55.42 | Avg Score: -75.35 |\n",
"| Experiment: 3 | Episode: 1461 | Score: -24.91 | Avg Score: -74.92 |\n",
"| Experiment: 3 | Episode: 1462 | Score: -14.54 | Avg Score: -74.58 |\n",
"| Experiment: 3 | Episode: 1463 | Score: -218.66 | Avg Score: -75.84 |\n",
"| Experiment: 3 | Episode: 1464 | Score: -50.1 | Avg Score: -75.38 |\n",
"| Experiment: 3 | Episode: 1465 | Score: -112.59 | Avg Score: -75.93 |\n",
"| Experiment: 3 | Episode: 1466 | Score: -105.12 | Avg Score: -76.04 |\n",
"| Experiment: 3 | Episode: 1467 | Score: -102.49 | Avg Score: -76.31 |\n",
"| Experiment: 3 | Episode: 1468 | Score: -141.54 | Avg Score: -76.8 |\n",
"| Experiment: 3 | Episode: 1469 | Score: -90.31 | Avg Score: -76.8 |\n",
"| Experiment: 3 | Episode: 1470 | Score: -69.37 | Avg Score: -76.87 |\n",
"| Experiment: 3 | Episode: 1471 | Score: -64.73 | Avg Score: -76.93 |\n",
"| Experiment: 3 | Episode: 1472 | Score: -159.85 | Avg Score: -77.61 |\n",
"| Experiment: 3 | Episode: 1473 | Score: -40.92 | Avg Score: -77.13 |\n",
"| Experiment: 3 | Episode: 1474 | Score: -139.12 | Avg Score: -77.2 |\n",
"| Experiment: 3 | Episode: 1475 | Score: -44.88 | Avg Score: -76.94 |\n",
"| Experiment: 3 | Episode: 1476 | Score: -116.34 | Avg Score: -77.36 |\n",
"| Experiment: 3 | Episode: 1477 | Score: -46.71 | Avg Score: -77.73 |\n",
"| Experiment: 3 | Episode: 1478 | Score: -5.3 | Avg Score: -77.06 |\n",
"| Experiment: 3 | Episode: 1479 | Score: -81.09 | Avg Score: -77.57 |\n",
"| Experiment: 3 | Episode: 1480 | Score: -65.82 | Avg Score: -77.37 |\n",
"| Experiment: 3 | Episode: 1481 | Score: -82.76 | Avg Score: -77.6 |\n",
"| Experiment: 3 | Episode: 1482 | Score: -212.48 | Avg Score: -79.39 |\n",
"| Experiment: 3 | Episode: 1483 | Score: -116.5 | Avg Score: -79.73 |\n",
"| Experiment: 3 | Episode: 1484 | Score: -59.75 | Avg Score: -80.55 |\n",
"| Experiment: 3 | Episode: 1485 | Score: 29.99 | Avg Score: -79.43 |\n",
"| Experiment: 3 | Episode: 1486 | Score: -99.7 | Avg Score: -80.09 |\n",
"| Experiment: 3 | Episode: 1487 | Score: -81.15 | Avg Score: -80.29 |\n",
"| Experiment: 3 | Episode: 1488 | Score: -32.99 | Avg Score: -80.3 |\n",
"| Experiment: 3 | Episode: 1489 | Score: -112.88 | Avg Score: -81.48 |\n",
"| Experiment: 3 | Episode: 1490 | Score: -35.96 | Avg Score: -80.87 |\n",
"| Experiment: 3 | Episode: 1491 | Score: -97.59 | Avg Score: -81.45 |\n",
"| Experiment: 3 | Episode: 1492 | Score: -57.62 | Avg Score: -81.28 |\n",
"| Experiment: 3 | Episode: 1493 | Score: -82.19 | Avg Score: -81.09 |\n",
"| Experiment: 3 | Episode: 1494 | Score: -75.51 | Avg Score: -81.21 |\n",
"| Experiment: 3 | Episode: 1495 | Score: -39.31 | Avg Score: -81.24 |\n",
"| Experiment: 3 | Episode: 1496 | Score: -40.79 | Avg Score: -80.83 |\n",
"| Experiment: 3 | Episode: 1497 | Score: -81.76 | Avg Score: -81.08 |\n",
"| Experiment: 3 | Episode: 1498 | Score: -106.39 | Avg Score: -81.8 |\n",
"| Experiment: 3 | Episode: 1499 | Score: -67.94 | Avg Score: -82.38 |\n",
"| Experiment: 3 | Episode: 1500 | Score: -125.71 | Avg Score: -83.07 |\n",
"| Experiment: 3 | Episode: 1501 | Score: -79.04 | Avg Score: -83.53 |\n",
"| Experiment: 3 | Episode: 1502 | Score: -108.8 | Avg Score: -83.73 |\n",
"| Experiment: 3 | Episode: 1503 | Score: -95.05 | Avg Score: -83.75 |\n",
"| Experiment: 3 | Episode: 1504 | Score: -0.35 | Avg Score: -82.79 |\n",
"| Experiment: 3 | Episode: 1505 | Score: -80.56 | Avg Score: -83.77 |\n",
"| Experiment: 3 | Episode: 1506 | Score: -60.29 | Avg Score: -83.74 |\n",
"| Experiment: 3 | Episode: 1507 | Score: -159.76 | Avg Score: -85.0 |\n",
"| Experiment: 3 | Episode: 1508 | Score: 24.22 | Avg Score: -83.87 |\n",
"| Experiment: 3 | Episode: 1509 | Score: -71.93 | Avg Score: -83.83 |\n",
"| Experiment: 3 | Episode: 1510 | Score: -50.59 | Avg Score: -83.45 |\n",
"| Experiment: 3 | Episode: 1511 | Score: -137.97 | Avg Score: -84.47 |\n",
"| Experiment: 3 | Episode: 1512 | Score: -28.99 | Avg Score: -84.24 |\n",
"| Experiment: 3 | Episode: 1513 | Score: -115.95 | Avg Score: -84.75 |\n",
"| Experiment: 3 | Episode: 1514 | Score: -59.85 | Avg Score: -84.83 |\n",
"| Experiment: 3 | Episode: 1515 | Score: -117.09 | Avg Score: -85.36 |\n",
"| Experiment: 3 | Episode: 1516 | Score: -172.65 | Avg Score: -86.01 |\n",
"| Experiment: 3 | Episode: 1517 | Score: -55.73 | Avg Score: -86.01 |\n",
"| Experiment: 3 | Episode: 1518 | Score: -158.93 | Avg Score: -86.14 |\n",
"| Experiment: 3 | Episode: 1519 | Score: -47.25 | Avg Score: -86.33 |\n",
"| Experiment: 3 | Episode: 1520 | Score: -35.74 | Avg Score: -86.05 |\n",
"| Experiment: 3 | Episode: 1521 | Score: -27.84 | Avg Score: -85.55 |\n",
"| Experiment: 3 | Episode: 1522 | Score: -40.0 | Avg Score: -84.96 |\n",
"| Experiment: 3 | Episode: 1523 | Score: -61.5 | Avg Score: -84.89 |\n",
"| Experiment: 3 | Episode: 1524 | Score: -94.83 | Avg Score: -84.96 |\n",
"| Experiment: 3 | Episode: 1525 | Score: -212.52 | Avg Score: -86.61 |\n",
"| Experiment: 3 | Episode: 1526 | Score: -43.5 | Avg Score: -86.29 |\n",
"| Experiment: 3 | Episode: 1527 | Score: -42.66 | Avg Score: -85.96 |\n",
"| Experiment: 3 | Episode: 1528 | Score: 21.28 | Avg Score: -83.61 |\n",
"| Experiment: 3 | Episode: 1529 | Score: -32.61 | Avg Score: -83.29 |\n",
"| Experiment: 3 | Episode: 1530 | Score: -58.57 | Avg Score: -83.01 |\n",
"| Experiment: 3 | Episode: 1531 | Score: -133.25 | Avg Score: -83.91 |\n",
"| Experiment: 3 | Episode: 1532 | Score: -75.8 | Avg Score: -83.99 |\n",
"| Experiment: 3 | Episode: 1533 | Score: -91.06 | Avg Score: -84.29 |\n",
"| Experiment: 3 | Episode: 1534 | Score: -66.7 | Avg Score: -84.44 |\n",
"| Experiment: 3 | Episode: 1535 | Score: -27.61 | Avg Score: -83.49 |\n",
"| Experiment: 3 | Episode: 1536 | Score: -78.36 | Avg Score: -82.28 |\n",
"| Experiment: 3 | Episode: 1537 | Score: -55.39 | Avg Score: -81.71 |\n",
"| Experiment: 3 | Episode: 1538 | Score: -87.09 | Avg Score: -82.23 |\n",
"| Experiment: 3 | Episode: 1539 | Score: -287.67 | Avg Score: -84.28 |\n",
"| Experiment: 3 | Episode: 1540 | Score: -61.58 | Avg Score: -82.54 |\n",
"| Experiment: 3 | Episode: 1541 | Score: -25.52 | Avg Score: -82.15 |\n",
"| Experiment: 3 | Episode: 1542 | Score: -47.97 | Avg Score: -81.94 |\n",
"| Experiment: 3 | Episode: 1543 | Score: -110.75 | Avg Score: -84.69 |\n",
"| Experiment: 3 | Episode: 1544 | Score: -44.97 | Avg Score: -84.55 |\n",
"| Experiment: 3 | Episode: 1545 | Score: -41.14 | Avg Score: -83.89 |\n",
"| Experiment: 3 | Episode: 1546 | Score: -65.7 | Avg Score: -83.32 |\n",
"| Experiment: 3 | Episode: 1547 | Score: -91.91 | Avg Score: -82.67 |\n",
"| Experiment: 3 | Episode: 1548 | Score: -86.74 | Avg Score: -81.74 |\n",
"| Experiment: 3 | Episode: 1549 | Score: 83.01 | Avg Score: -80.58 |\n",
"| Experiment: 3 | Episode: 1550 | Score: -23.27 | Avg Score: -79.7 |\n",
"| Experiment: 3 | Episode: 1551 | Score: -60.07 | Avg Score: -79.61 |\n",
"| Experiment: 3 | Episode: 1552 | Score: -47.07 | Avg Score: -79.18 |\n",
"| Experiment: 3 | Episode: 1553 | Score: -72.78 | Avg Score: -79.33 |\n",
"| Experiment: 3 | Episode: 1554 | Score: -101.44 | Avg Score: -78.28 |\n",
"| Experiment: 3 | Episode: 1555 | Score: -33.39 | Avg Score: -78.21 |\n",
"| Experiment: 3 | Episode: 1556 | Score: -201.46 | Avg Score: -79.77 |\n",
"| Experiment: 3 | Episode: 1557 | Score: -113.07 | Avg Score: -79.62 |\n",
"| Experiment: 3 | Episode: 1558 | Score: -83.22 | Avg Score: -79.3 |\n",
"| Experiment: 3 | Episode: 1559 | Score: -20.13 | Avg Score: -76.86 |\n",
"| Experiment: 3 | Episode: 1560 | Score: -65.1 | Avg Score: -76.96 |\n",
"| Experiment: 3 | Episode: 1561 | Score: -126.67 | Avg Score: -77.97 |\n",
"| Experiment: 3 | Episode: 1562 | Score: -5.35 | Avg Score: -77.88 |\n",
"| Experiment: 3 | Episode: 1563 | Score: -72.95 | Avg Score: -76.42 |\n",
"| Experiment: 3 | Episode: 1564 | Score: -68.11 | Avg Score: -76.6 |\n",
"| Experiment: 3 | Episode: 1565 | Score: -20.84 | Avg Score: -75.69 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 3 | Episode: 1566 | Score: 5.92 | Avg Score: -74.58 |\n",
"| Experiment: 3 | Episode: 1567 | Score: -3.61 | Avg Score: -73.59 |\n",
"| Experiment: 3 | Episode: 1568 | Score: -86.52 | Avg Score: -73.04 |\n",
"| Experiment: 3 | Episode: 1569 | Score: -98.81 | Avg Score: -73.12 |\n",
"| Experiment: 3 | Episode: 1570 | Score: -106.35 | Avg Score: -73.49 |\n",
"| Experiment: 3 | Episode: 1571 | Score: -42.75 | Avg Score: -73.27 |\n",
"| Experiment: 3 | Episode: 1572 | Score: -55.96 | Avg Score: -72.23 |\n",
"| Experiment: 3 | Episode: 1573 | Score: -63.88 | Avg Score: -72.46 |\n",
"| Experiment: 3 | Episode: 1574 | Score: -55.2 | Avg Score: -71.62 |\n",
"| Experiment: 3 | Episode: 1575 | Score: -52.92 | Avg Score: -71.7 |\n",
"| Experiment: 3 | Episode: 1576 | Score: -55.74 | Avg Score: -71.1 |\n",
"| Experiment: 3 | Episode: 1577 | Score: -44.81 | Avg Score: -71.08 |\n",
"| Experiment: 3 | Episode: 1578 | Score: -76.1 | Avg Score: -71.79 |\n",
"| Experiment: 3 | Episode: 1579 | Score: -34.34 | Avg Score: -71.32 |\n",
"| Experiment: 3 | Episode: 1580 | Score: -107.6 | Avg Score: -71.74 |\n",
"| Experiment: 3 | Episode: 1581 | Score: -487.73 | Avg Score: -75.79 |\n",
"| Experiment: 3 | Episode: 1582 | Score: -29.38 | Avg Score: -73.96 |\n",
"| Experiment: 3 | Episode: 1583 | Score: -156.79 | Avg Score: -74.36 |\n",
"| Experiment: 3 | Episode: 1584 | Score: -41.88 | Avg Score: -74.18 |\n",
"| Experiment: 3 | Episode: 1585 | Score: -75.83 | Avg Score: -75.24 |\n",
"| Experiment: 3 | Episode: 1586 | Score: -35.95 | Avg Score: -74.6 |\n",
"| Experiment: 3 | Episode: 1587 | Score: -35.3 | Avg Score: -74.14 |\n",
"| Experiment: 3 | Episode: 1588 | Score: -6.62 | Avg Score: -73.88 |\n",
"| Experiment: 3 | Episode: 1589 | Score: -215.8 | Avg Score: -74.91 |\n",
"| Experiment: 3 | Episode: 1590 | Score: -107.3 | Avg Score: -75.62 |\n",
"| Experiment: 3 | Episode: 1591 | Score: -71.78 | Avg Score: -75.36 |\n",
"| Experiment: 3 | Episode: 1592 | Score: -85.97 | Avg Score: -75.65 |\n",
"| Experiment: 3 | Episode: 1593 | Score: -165.96 | Avg Score: -76.49 |\n",
"| Experiment: 3 | Episode: 1594 | Score: -188.54 | Avg Score: -77.62 |\n",
"| Experiment: 3 | Episode: 1595 | Score: -27.08 | Avg Score: -77.49 |\n",
"| Experiment: 3 | Episode: 1596 | Score: -87.11 | Avg Score: -77.96 |\n",
"| Experiment: 3 | Episode: 1597 | Score: -53.18 | Avg Score: -77.67 |\n",
"| Experiment: 3 | Episode: 1598 | Score: -84.25 | Avg Score: -77.45 |\n",
"| Experiment: 3 | Episode: 1599 | Score: -37.41 | Avg Score: -77.14 |\n",
"| Experiment: 3 | Episode: 1600 | Score: -48.62 | Avg Score: -76.37 |\n",
"| Experiment: 3 | Episode: 1601 | Score: -69.0 | Avg Score: -76.27 |\n",
"| Experiment: 3 | Episode: 1602 | Score: -87.56 | Avg Score: -76.06 |\n",
"| Experiment: 3 | Episode: 1603 | Score: -116.23 | Avg Score: -76.27 |\n",
"| Experiment: 3 | Episode: 1604 | Score: -81.75 | Avg Score: -77.09 |\n",
"| Experiment: 3 | Episode: 1605 | Score: -56.68 | Avg Score: -76.85 |\n",
"| Experiment: 3 | Episode: 1606 | Score: -48.46 | Avg Score: -76.73 |\n",
"| Experiment: 3 | Episode: 1607 | Score: -60.28 | Avg Score: -75.73 |\n",
"| Experiment: 3 | Episode: 1608 | Score: -62.42 | Avg Score: -76.6 |\n",
"| Experiment: 3 | Episode: 1609 | Score: -33.18 | Avg Score: -76.21 |\n",
"| Experiment: 3 | Episode: 1610 | Score: -90.68 | Avg Score: -76.61 |\n",
"| Experiment: 3 | Episode: 1611 | Score: -43.36 | Avg Score: -75.67 |\n",
"| Experiment: 3 | Episode: 1612 | Score: -52.76 | Avg Score: -75.91 |\n",
"| Experiment: 3 | Episode: 1613 | Score: -43.82 | Avg Score: -75.18 |\n",
"| Experiment: 3 | Episode: 1614 | Score: -60.29 | Avg Score: -75.19 |\n",
"| Experiment: 3 | Episode: 1615 | Score: -23.98 | Avg Score: -74.26 |\n",
"| Experiment: 3 | Episode: 1616 | Score: -86.4 | Avg Score: -73.39 |\n",
"| Experiment: 3 | Episode: 1617 | Score: -66.35 | Avg Score: -73.5 |\n",
"| Experiment: 3 | Episode: 1618 | Score: -71.17 | Avg Score: -72.62 |\n",
"| Experiment: 3 | Episode: 1619 | Score: -80.12 | Avg Score: -72.95 |\n",
"| Experiment: 3 | Episode: 1620 | Score: -33.88 | Avg Score: -72.93 |\n",
"| Experiment: 3 | Episode: 1621 | Score: 112.75 | Avg Score: -71.53 |\n",
"| Experiment: 3 | Episode: 1622 | Score: -33.11 | Avg Score: -71.46 |\n",
"| Experiment: 3 | Episode: 1623 | Score: -64.84 | Avg Score: -71.49 |\n",
"| Experiment: 3 | Episode: 1624 | Score: -73.4 | Avg Score: -71.28 |\n",
"| Experiment: 3 | Episode: 1625 | Score: -47.47 | Avg Score: -69.63 |\n",
"| Experiment: 3 | Episode: 1626 | Score: -45.07 | Avg Score: -69.64 |\n",
"| Experiment: 3 | Episode: 1627 | Score: -16.57 | Avg Score: -69.38 |\n",
"| Experiment: 3 | Episode: 1628 | Score: -70.98 | Avg Score: -70.3 |\n",
"| Experiment: 3 | Episode: 1629 | Score: -82.83 | Avg Score: -70.81 |\n",
"| Experiment: 3 | Episode: 1630 | Score: -126.38 | Avg Score: -71.49 |\n",
"| Experiment: 3 | Episode: 1631 | Score: -44.18 | Avg Score: -70.59 |\n",
"| Experiment: 3 | Episode: 1632 | Score: -120.76 | Avg Score: -71.04 |\n",
"| Experiment: 3 | Episode: 1633 | Score: -92.15 | Avg Score: -71.05 |\n",
"| Experiment: 3 | Episode: 1634 | Score: -126.77 | Avg Score: -71.66 |\n",
"| Experiment: 3 | Episode: 1635 | Score: -56.88 | Avg Score: -71.95 |\n",
"| Experiment: 3 | Episode: 1636 | Score: -82.89 | Avg Score: -71.99 |\n",
"| Experiment: 3 | Episode: 1637 | Score: -93.88 | Avg Score: -72.38 |\n",
"| Experiment: 3 | Episode: 1638 | Score: -60.01 | Avg Score: -72.11 |\n",
"| Experiment: 3 | Episode: 1639 | Score: -90.23 | Avg Score: -70.13 |\n",
"| Experiment: 3 | Episode: 1640 | Score: -197.27 | Avg Score: -71.49 |\n",
"| Experiment: 3 | Episode: 1641 | Score: -62.11 | Avg Score: -71.86 |\n",
"| Experiment: 3 | Episode: 1642 | Score: -52.4 | Avg Score: -71.9 |\n",
"| Experiment: 3 | Episode: 1643 | Score: -22.91 | Avg Score: -71.02 |\n",
"| Experiment: 3 | Episode: 1644 | Score: -71.06 | Avg Score: -71.28 |\n",
"| Experiment: 3 | Episode: 1645 | Score: -89.42 | Avg Score: -71.77 |\n",
"| Experiment: 3 | Episode: 1646 | Score: -91.16 | Avg Score: -72.02 |\n",
"| Experiment: 3 | Episode: 1647 | Score: -50.42 | Avg Score: -71.61 |\n",
"| Experiment: 3 | Episode: 1648 | Score: -90.49 | Avg Score: -71.64 |\n",
"| Experiment: 3 | Episode: 1649 | Score: -32.78 | Avg Score: -72.8 |\n",
"| Experiment: 3 | Episode: 1650 | Score: -72.47 | Avg Score: -73.29 |\n",
"| Experiment: 3 | Episode: 1651 | Score: -47.05 | Avg Score: -73.16 |\n",
"| Experiment: 3 | Episode: 1652 | Score: -33.59 | Avg Score: -73.03 |\n",
"| Experiment: 3 | Episode: 1653 | Score: -60.99 | Avg Score: -72.91 |\n",
"| Experiment: 3 | Episode: 1654 | Score: -65.32 | Avg Score: -72.55 |\n",
"| Experiment: 3 | Episode: 1655 | Score: -86.27 | Avg Score: -73.08 |\n",
"| Experiment: 3 | Episode: 1656 | Score: -128.04 | Avg Score: -72.34 |\n",
"| Experiment: 3 | Episode: 1657 | Score: -144.67 | Avg Score: -72.66 |\n",
"| Experiment: 3 | Episode: 1658 | Score: -0.13 | Avg Score: -71.83 |\n",
"| Experiment: 3 | Episode: 1659 | Score: -84.23 | Avg Score: -72.47 |\n",
"| Experiment: 3 | Episode: 1660 | Score: -57.41 | Avg Score: -72.39 |\n",
"| Experiment: 3 | Episode: 1661 | Score: -86.07 | Avg Score: -71.99 |\n",
"| Experiment: 3 | Episode: 1662 | Score: -74.43 | Avg Score: -72.68 |\n",
"| Experiment: 3 | Episode: 1663 | Score: -59.82 | Avg Score: -72.55 |\n",
"| Experiment: 3 | Episode: 1664 | Score: -68.05 | Avg Score: -72.55 |\n",
"| Experiment: 3 | Episode: 1665 | Score: -54.79 | Avg Score: -72.89 |\n",
"| Experiment: 3 | Episode: 1666 | Score: -50.15 | Avg Score: -73.45 |\n",
"| Experiment: 3 | Episode: 1667 | Score: -33.04 | Avg Score: -73.74 |\n",
"| Experiment: 3 | Episode: 1668 | Score: -26.16 | Avg Score: -73.14 |\n",
"| Experiment: 3 | Episode: 1669 | Score: -10.39 | Avg Score: -72.25 |\n",
"| Experiment: 3 | Episode: 1670 | Score: -74.95 | Avg Score: -71.94 |\n",
"| Experiment: 3 | Episode: 1671 | Score: -30.75 | Avg Score: -71.82 |\n",
"| Experiment: 3 | Episode: 1672 | Score: -63.9 | Avg Score: -71.9 |\n",
"| Experiment: 3 | Episode: 1673 | Score: -93.8 | Avg Score: -72.2 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 3 | Episode: 1674 | Score: -80.3 | Avg Score: -72.45 |\n",
"| Experiment: 3 | Episode: 1675 | Score: -73.01 | Avg Score: -72.65 |\n",
"| Experiment: 3 | Episode: 1676 | Score: -42.85 | Avg Score: -72.52 |\n",
"| Experiment: 3 | Episode: 1677 | Score: -41.68 | Avg Score: -72.49 |\n",
"| Experiment: 3 | Episode: 1678 | Score: -94.57 | Avg Score: -72.67 |\n",
"| Experiment: 3 | Episode: 1679 | Score: -36.0 | Avg Score: -72.69 |\n",
"| Experiment: 3 | Episode: 1680 | Score: -90.23 | Avg Score: -72.52 |\n",
"| Experiment: 3 | Episode: 1681 | Score: -131.66 | Avg Score: -68.96 |\n",
"| Experiment: 3 | Episode: 1682 | Score: -6.73 | Avg Score: -68.73 |\n",
"| Experiment: 3 | Episode: 1683 | Score: -61.67 | Avg Score: -67.78 |\n",
"| Experiment: 3 | Episode: 1684 | Score: -91.46 | Avg Score: -68.27 |\n",
"| Experiment: 3 | Episode: 1685 | Score: -95.84 | Avg Score: -68.47 |\n",
"| Experiment: 3 | Episode: 1686 | Score: -96.7 | Avg Score: -69.08 |\n",
"| Experiment: 3 | Episode: 1687 | Score: -58.09 | Avg Score: -69.31 |\n",
"| Experiment: 3 | Episode: 1688 | Score: -22.56 | Avg Score: -69.47 |\n",
"| Experiment: 3 | Episode: 1689 | Score: -129.47 | Avg Score: -68.61 |\n",
"| Experiment: 3 | Episode: 1690 | Score: -94.67 | Avg Score: -68.48 |\n",
"| Experiment: 3 | Episode: 1691 | Score: -18.26 | Avg Score: -67.94 |\n",
"| Experiment: 3 | Episode: 1692 | Score: -56.41 | Avg Score: -67.65 |\n",
"| Experiment: 3 | Episode: 1693 | Score: -17.42 | Avg Score: -66.16 |\n",
"| Experiment: 3 | Episode: 1694 | Score: -93.22 | Avg Score: -65.21 |\n",
"| Experiment: 3 | Episode: 1695 | Score: -141.7 | Avg Score: -66.36 |\n",
"| Experiment: 3 | Episode: 1696 | Score: -48.85 | Avg Score: -65.97 |\n",
"| Experiment: 3 | Episode: 1697 | Score: -42.55 | Avg Score: -65.87 |\n",
"| Experiment: 3 | Episode: 1698 | Score: -28.38 | Avg Score: -65.31 |\n",
"| Experiment: 3 | Episode: 1699 | Score: -36.31 | Avg Score: -65.3 |\n",
"| Experiment: 3 | Episode: 1700 | Score: -46.64 | Avg Score: -65.28 |\n",
"| Experiment: 3 | Episode: 1701 | Score: -57.71 | Avg Score: -65.16 |\n",
"| Experiment: 3 | Episode: 1702 | Score: -8.3 | Avg Score: -64.37 |\n",
"| Experiment: 3 | Episode: 1703 | Score: -40.58 | Avg Score: -63.62 |\n",
"| Experiment: 3 | Episode: 1704 | Score: -69.21 | Avg Score: -63.49 |\n",
"| Experiment: 3 | Episode: 1705 | Score: -108.7 | Avg Score: -64.01 |\n",
"| Experiment: 3 | Episode: 1706 | Score: -59.04 | Avg Score: -64.12 |\n",
"| Experiment: 3 | Episode: 1707 | Score: -58.46 | Avg Score: -64.1 |\n",
"| Experiment: 3 | Episode: 1708 | Score: -45.33 | Avg Score: -63.93 |\n",
"| Experiment: 3 | Episode: 1709 | Score: -95.87 | Avg Score: -64.55 |\n",
"| Experiment: 3 | Episode: 1710 | Score: -20.22 | Avg Score: -63.85 |\n",
"| Experiment: 3 | Episode: 1711 | Score: -43.56 | Avg Score: -63.85 |\n",
"| Experiment: 3 | Episode: 1712 | Score: -92.17 | Avg Score: -64.25 |\n",
"| Experiment: 3 | Episode: 1713 | Score: -51.01 | Avg Score: -64.32 |\n",
"| Experiment: 3 | Episode: 1714 | Score: -28.11 | Avg Score: -64.0 |\n",
"| Experiment: 3 | Episode: 1715 | Score: -79.51 | Avg Score: -64.55 |\n",
"| Experiment: 3 | Episode: 1716 | Score: -199.99 | Avg Score: -65.69 |\n",
"| Experiment: 3 | Episode: 1717 | Score: -63.35 | Avg Score: -65.66 |\n",
"| Experiment: 3 | Episode: 1718 | Score: -62.62 | Avg Score: -65.57 |\n",
"| Experiment: 3 | Episode: 1719 | Score: -226.35 | Avg Score: -67.03 |\n",
"| Experiment: 3 | Episode: 1720 | Score: -56.17 | Avg Score: -67.26 |\n",
"| Experiment: 3 | Episode: 1721 | Score: -68.16 | Avg Score: -69.07 |\n",
"| Experiment: 3 | Episode: 1722 | Score: -51.87 | Avg Score: -69.25 |\n",
"| Experiment: 3 | Episode: 1723 | Score: -120.77 | Avg Score: -69.81 |\n",
"| Experiment: 3 | Episode: 1724 | Score: -18.21 | Avg Score: -69.26 |\n",
"| Experiment: 3 | Episode: 1725 | Score: -20.82 | Avg Score: -68.99 |\n",
"| Experiment: 3 | Episode: 1726 | Score: -109.85 | Avg Score: -69.64 |\n",
"| Experiment: 3 | Episode: 1727 | Score: -99.47 | Avg Score: -70.47 |\n",
"| Experiment: 3 | Episode: 1728 | Score: -62.86 | Avg Score: -70.39 |\n",
"| Experiment: 3 | Episode: 1729 | Score: -22.29 | Avg Score: -69.78 |\n",
"| Experiment: 3 | Episode: 1730 | Score: -191.51 | Avg Score: -70.44 |\n",
"| Experiment: 3 | Episode: 1731 | Score: -7.38 | Avg Score: -70.07 |\n",
"| Experiment: 3 | Episode: 1732 | Score: -64.56 | Avg Score: -69.51 |\n",
"| Experiment: 3 | Episode: 1733 | Score: -96.64 | Avg Score: -69.55 |\n",
"| Experiment: 3 | Episode: 1734 | Score: -43.15 | Avg Score: -68.71 |\n",
"| Experiment: 3 | Episode: 1735 | Score: -83.37 | Avg Score: -68.98 |\n",
"| Experiment: 3 | Episode: 1736 | Score: -23.56 | Avg Score: -68.39 |\n",
"| Experiment: 3 | Episode: 1737 | Score: -67.65 | Avg Score: -68.12 |\n",
"| Experiment: 3 | Episode: 1738 | Score: -82.13 | Avg Score: -68.34 |\n",
"| Experiment: 3 | Episode: 1739 | Score: 20.5 | Avg Score: -67.24 |\n",
"| Experiment: 3 | Episode: 1740 | Score: -61.89 | Avg Score: -65.88 |\n",
"| Experiment: 3 | Episode: 1741 | Score: -199.62 | Avg Score: -67.26 |\n",
"| Experiment: 3 | Episode: 1742 | Score: -74.08 | Avg Score: -67.48 |\n",
"| Experiment: 3 | Episode: 1743 | Score: -30.58 | Avg Score: -67.55 |\n",
"| Experiment: 3 | Episode: 1744 | Score: -21.78 | Avg Score: -67.06 |\n",
"| Experiment: 3 | Episode: 1745 | Score: -37.13 | Avg Score: -66.54 |\n",
"| Experiment: 3 | Episode: 1746 | Score: -84.44 | Avg Score: -66.47 |\n",
"| Experiment: 3 | Episode: 1747 | Score: -72.35 | Avg Score: -66.69 |\n",
"| Experiment: 3 | Episode: 1748 | Score: -47.76 | Avg Score: -66.26 |\n",
"| Experiment: 3 | Episode: 1749 | Score: -58.76 | Avg Score: -66.52 |\n",
"| Experiment: 3 | Episode: 1750 | Score: -50.8 | Avg Score: -66.3 |\n",
"| Experiment: 3 | Episode: 1751 | Score: -43.35 | Avg Score: -66.27 |\n",
"| Experiment: 3 | Episode: 1752 | Score: -44.15 | Avg Score: -66.37 |\n",
"| Experiment: 3 | Episode: 1753 | Score: -55.97 | Avg Score: -66.32 |\n",
"| Experiment: 3 | Episode: 1754 | Score: -55.93 | Avg Score: -66.23 |\n",
"| Experiment: 3 | Episode: 1755 | Score: -74.38 | Avg Score: -66.11 |\n",
"| Experiment: 3 | Episode: 1756 | Score: -73.76 | Avg Score: -65.57 |\n",
"| Experiment: 3 | Episode: 1757 | Score: -72.1 | Avg Score: -64.84 |\n",
"| Experiment: 3 | Episode: 1758 | Score: -91.15 | Avg Score: -65.75 |\n",
"| Experiment: 3 | Episode: 1759 | Score: -49.92 | Avg Score: -65.41 |\n",
"| Experiment: 3 | Episode: 1760 | Score: -47.32 | Avg Score: -65.31 |\n",
"| Experiment: 3 | Episode: 1761 | Score: -170.52 | Avg Score: -66.15 |\n",
"| Experiment: 3 | Episode: 1762 | Score: -23.49 | Avg Score: -65.64 |\n",
"| Experiment: 3 | Episode: 1763 | Score: -36.96 | Avg Score: -65.41 |\n",
"| Experiment: 3 | Episode: 1764 | Score: -128.12 | Avg Score: -66.01 |\n",
"| Experiment: 3 | Episode: 1765 | Score: -87.07 | Avg Score: -66.34 |\n",
"| Experiment: 3 | Episode: 1766 | Score: -148.08 | Avg Score: -67.32 |\n",
"| Experiment: 3 | Episode: 1767 | Score: -81.89 | Avg Score: -67.81 |\n",
"| Experiment: 3 | Episode: 1768 | Score: 2.39 | Avg Score: -67.52 |\n",
"| Experiment: 3 | Episode: 1769 | Score: -79.69 | Avg Score: -68.21 |\n",
"| Experiment: 3 | Episode: 1770 | Score: -120.44 | Avg Score: -68.67 |\n",
"| Experiment: 3 | Episode: 1771 | Score: -227.41 | Avg Score: -70.63 |\n",
"| Experiment: 3 | Episode: 1772 | Score: -151.84 | Avg Score: -71.51 |\n",
"| Experiment: 3 | Episode: 1773 | Score: -27.66 | Avg Score: -70.85 |\n",
"| Experiment: 3 | Episode: 1774 | Score: -82.65 | Avg Score: -70.88 |\n",
"| Experiment: 3 | Episode: 1775 | Score: -128.16 | Avg Score: -71.43 |\n",
"| Experiment: 3 | Episode: 1776 | Score: -59.78 | Avg Score: -71.6 |\n",
"| Experiment: 3 | Episode: 1777 | Score: -45.75 | Avg Score: -71.64 |\n",
"| Experiment: 3 | Episode: 1778 | Score: -117.29 | Avg Score: -71.86 |\n",
"| Experiment: 3 | Episode: 1779 | Score: -131.15 | Avg Score: -72.82 |\n",
"| Experiment: 3 | Episode: 1780 | Score: -54.34 | Avg Score: -72.46 |\n",
"| Experiment: 3 | Episode: 1781 | Score: -99.73 | Avg Score: -72.14 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 3 | Episode: 1782 | Score: -17.88 | Avg Score: -72.25 |\n",
"| Experiment: 3 | Episode: 1783 | Score: -93.09 | Avg Score: -72.56 |\n",
"| Experiment: 3 | Episode: 1784 | Score: -88.05 | Avg Score: -72.53 |\n",
"| Experiment: 3 | Episode: 1785 | Score: -54.53 | Avg Score: -72.12 |\n",
"| Experiment: 3 | Episode: 1786 | Score: -31.75 | Avg Score: -71.47 |\n",
"| Experiment: 3 | Episode: 1787 | Score: -67.49 | Avg Score: -71.56 |\n",
"| Experiment: 3 | Episode: 1788 | Score: -76.41 | Avg Score: -72.1 |\n",
"| Experiment: 3 | Episode: 1789 | Score: -75.73 | Avg Score: -71.56 |\n",
"| Experiment: 3 | Episode: 1790 | Score: -69.99 | Avg Score: -71.31 |\n",
"| Experiment: 3 | Episode: 1791 | Score: -97.32 | Avg Score: -72.11 |\n",
"| Experiment: 3 | Episode: 1792 | Score: -115.96 | Avg Score: -72.7 |\n",
"| Experiment: 3 | Episode: 1793 | Score: -133.93 | Avg Score: -73.87 |\n",
"| Experiment: 3 | Episode: 1794 | Score: -72.54 | Avg Score: -73.66 |\n",
"| Experiment: 3 | Episode: 1795 | Score: -42.96 | Avg Score: -72.67 |\n",
"| Experiment: 3 | Episode: 1796 | Score: -167.69 | Avg Score: -73.86 |\n",
"| Experiment: 3 | Episode: 1797 | Score: -40.62 | Avg Score: -73.84 |\n",
"| Experiment: 3 | Episode: 1798 | Score: -88.61 | Avg Score: -74.44 |\n",
"| Experiment: 3 | Episode: 1799 | Score: -55.1 | Avg Score: -74.63 |\n",
"| Experiment: 3 | Episode: 1800 | Score: -150.34 | Avg Score: -75.67 |\n",
"| Experiment: 3 | Episode: 1801 | Score: -48.88 | Avg Score: -75.58 |\n",
"| Experiment: 3 | Episode: 1802 | Score: -81.2 | Avg Score: -76.31 |\n",
"| Experiment: 3 | Episode: 1803 | Score: -12.77 | Avg Score: -76.03 |\n",
"| Experiment: 3 | Episode: 1804 | Score: -85.15 | Avg Score: -76.19 |\n",
"| Experiment: 3 | Episode: 1805 | Score: -62.08 | Avg Score: -75.72 |\n",
"| Experiment: 3 | Episode: 1806 | Score: -25.78 | Avg Score: -75.39 |\n",
"| Experiment: 3 | Episode: 1807 | Score: -101.44 | Avg Score: -75.82 |\n",
"| Experiment: 3 | Episode: 1808 | Score: -27.49 | Avg Score: -75.64 |\n",
"| Experiment: 3 | Episode: 1809 | Score: -56.34 | Avg Score: -75.25 |\n",
"| Experiment: 3 | Episode: 1810 | Score: 22.13 | Avg Score: -74.82 |\n",
"| Experiment: 3 | Episode: 1811 | Score: -170.26 | Avg Score: -76.09 |\n",
"| Experiment: 3 | Episode: 1812 | Score: -35.41 | Avg Score: -75.52 |\n",
"| Experiment: 3 | Episode: 1813 | Score: -51.53 | Avg Score: -75.53 |\n",
"| Experiment: 3 | Episode: 1814 | Score: 2.03 | Avg Score: -75.23 |\n",
"| Experiment: 3 | Episode: 1815 | Score: -80.99 | Avg Score: -75.24 |\n",
"| Experiment: 3 | Episode: 1816 | Score: -56.8 | Avg Score: -73.81 |\n",
"| Experiment: 3 | Episode: 1817 | Score: -52.51 | Avg Score: -73.7 |\n",
"| Experiment: 3 | Episode: 1818 | Score: 5.36 | Avg Score: -73.02 |\n",
"| Experiment: 3 | Episode: 1819 | Score: -78.69 | Avg Score: -71.55 |\n",
"| Experiment: 3 | Episode: 1820 | Score: -223.8 | Avg Score: -73.22 |\n",
"| Experiment: 3 | Episode: 1821 | Score: -55.82 | Avg Score: -73.1 |\n",
"| Experiment: 3 | Episode: 1822 | Score: -89.35 | Avg Score: -73.47 |\n",
"| Experiment: 3 | Episode: 1823 | Score: -70.24 | Avg Score: -72.97 |\n",
"| Experiment: 3 | Episode: 1824 | Score: -120.59 | Avg Score: -73.99 |\n",
"| Experiment: 3 | Episode: 1825 | Score: -33.37 | Avg Score: -74.12 |\n",
"| Experiment: 3 | Episode: 1826 | Score: -74.67 | Avg Score: -73.77 |\n",
"| Experiment: 3 | Episode: 1827 | Score: -28.7 | Avg Score: -73.06 |\n",
"| Experiment: 3 | Episode: 1828 | Score: -92.5 | Avg Score: -73.35 |\n",
"| Experiment: 3 | Episode: 1829 | Score: -60.76 | Avg Score: -73.74 |\n",
"| Experiment: 3 | Episode: 1830 | Score: -81.42 | Avg Score: -72.64 |\n",
"| Experiment: 3 | Episode: 1831 | Score: -70.77 | Avg Score: -73.27 |\n",
"| Experiment: 3 | Episode: 1832 | Score: -74.78 | Avg Score: -73.37 |\n",
"| Experiment: 3 | Episode: 1833 | Score: -166.24 | Avg Score: -74.07 |\n",
"| Experiment: 3 | Episode: 1834 | Score: -95.93 | Avg Score: -74.6 |\n",
"| Experiment: 3 | Episode: 1835 | Score: -16.11 | Avg Score: -73.93 |\n",
"| Experiment: 3 | Episode: 1836 | Score: 7.7 | Avg Score: -73.61 |\n",
"| Experiment: 3 | Episode: 1837 | Score: -40.81 | Avg Score: -73.34 |\n",
"| Experiment: 3 | Episode: 1838 | Score: -58.16 | Avg Score: -73.1 |\n",
"| Experiment: 3 | Episode: 1839 | Score: -39.28 | Avg Score: -73.7 |\n",
"| Experiment: 3 | Episode: 1840 | Score: -69.99 | Avg Score: -73.78 |\n",
"| Experiment: 3 | Episode: 1841 | Score: -86.42 | Avg Score: -72.65 |\n",
"| Experiment: 3 | Episode: 1842 | Score: -40.46 | Avg Score: -72.32 |\n",
"| Experiment: 3 | Episode: 1843 | Score: -22.37 | Avg Score: -72.23 |\n",
"| Experiment: 3 | Episode: 1844 | Score: -257.64 | Avg Score: -74.59 |\n",
"| Experiment: 3 | Episode: 1845 | Score: -23.3 | Avg Score: -74.45 |\n",
"| Experiment: 3 | Episode: 1846 | Score: -197.72 | Avg Score: -75.59 |\n",
"| Experiment: 3 | Episode: 1847 | Score: -101.57 | Avg Score: -75.88 |\n",
"| Experiment: 3 | Episode: 1848 | Score: -41.97 | Avg Score: -75.82 |\n",
"| Experiment: 3 | Episode: 1849 | Score: -22.77 | Avg Score: -75.46 |\n",
"| Experiment: 3 | Episode: 1850 | Score: 8.93 | Avg Score: -74.86 |\n",
"| Experiment: 3 | Episode: 1851 | Score: -60.4 | Avg Score: -75.03 |\n",
"| Experiment: 3 | Episode: 1852 | Score: -26.17 | Avg Score: -74.85 |\n",
"| Experiment: 3 | Episode: 1853 | Score: -19.12 | Avg Score: -74.49 |\n",
"| Experiment: 3 | Episode: 1854 | Score: -65.59 | Avg Score: -74.58 |\n",
"| Experiment: 3 | Episode: 1855 | Score: -41.67 | Avg Score: -74.25 |\n",
"| Experiment: 3 | Episode: 1856 | Score: -133.3 | Avg Score: -74.85 |\n",
"| Experiment: 3 | Episode: 1857 | Score: -39.85 | Avg Score: -74.53 |\n",
"| Experiment: 3 | Episode: 1858 | Score: -70.71 | Avg Score: -74.32 |\n",
"| Experiment: 3 | Episode: 1859 | Score: -30.47 | Avg Score: -74.13 |\n",
"| Experiment: 3 | Episode: 1860 | Score: -82.62 | Avg Score: -74.48 |\n",
"| Experiment: 3 | Episode: 1861 | Score: -44.71 | Avg Score: -73.22 |\n",
"| Experiment: 3 | Episode: 1862 | Score: -70.0 | Avg Score: -73.69 |\n",
"| Experiment: 3 | Episode: 1863 | Score: 10.0 | Avg Score: -73.22 |\n",
"| Experiment: 3 | Episode: 1864 | Score: -76.37 | Avg Score: -72.7 |\n",
"| Experiment: 3 | Episode: 1865 | Score: -44.71 | Avg Score: -72.28 |\n",
"| Experiment: 3 | Episode: 1866 | Score: -33.74 | Avg Score: -71.13 |\n",
"| Experiment: 3 | Episode: 1867 | Score: -104.88 | Avg Score: -71.36 |\n",
"| Experiment: 3 | Episode: 1868 | Score: -95.26 | Avg Score: -72.34 |\n",
"| Experiment: 3 | Episode: 1869 | Score: -57.13 | Avg Score: -72.12 |\n",
"| Experiment: 3 | Episode: 1870 | Score: -100.76 | Avg Score: -71.92 |\n",
"| Experiment: 3 | Episode: 1871 | Score: -38.38 | Avg Score: -70.03 |\n",
"| Experiment: 3 | Episode: 1872 | Score: -43.41 | Avg Score: -68.94 |\n",
"| Experiment: 3 | Episode: 1873 | Score: -46.84 | Avg Score: -69.14 |\n",
"| Experiment: 3 | Episode: 1874 | Score: -76.95 | Avg Score: -69.08 |\n",
"| Experiment: 3 | Episode: 1875 | Score: -57.8 | Avg Score: -68.38 |\n",
"| Experiment: 3 | Episode: 1876 | Score: -38.96 | Avg Score: -68.17 |\n",
"| Experiment: 3 | Episode: 1877 | Score: -11.46 | Avg Score: -67.82 |\n",
"| Experiment: 3 | Episode: 1878 | Score: -65.6 | Avg Score: -67.31 |\n",
"| Experiment: 3 | Episode: 1879 | Score: -100.15 | Avg Score: -67.0 |\n",
"| Experiment: 3 | Episode: 1880 | Score: -7.13 | Avg Score: -66.53 |\n",
"| Experiment: 3 | Episode: 1881 | Score: -67.48 | Avg Score: -66.2 |\n",
"| Experiment: 3 | Episode: 1882 | Score: -158.23 | Avg Score: -67.61 |\n",
"| Experiment: 3 | Episode: 1883 | Score: -56.74 | Avg Score: -67.24 |\n",
"| Experiment: 3 | Episode: 1884 | Score: -151.92 | Avg Score: -67.88 |\n",
"| Experiment: 3 | Episode: 1885 | Score: -43.37 | Avg Score: -67.77 |\n",
"| Experiment: 3 | Episode: 1886 | Score: -35.73 | Avg Score: -67.81 |\n",
"| Experiment: 3 | Episode: 1887 | Score: -110.02 | Avg Score: -68.23 |\n",
"| Experiment: 3 | Episode: 1888 | Score: -65.38 | Avg Score: -68.12 |\n",
"| Experiment: 3 | Episode: 1889 | Score: -29.97 | Avg Score: -67.67 |\n",
"| Experiment: 3 | Episode: 1890 | Score: -54.98 | Avg Score: -67.52 |\n",
"| Experiment: 3 | Episode: 1891 | Score: -107.94 | Avg Score: -67.62 |\n",
"| Experiment: 3 | Episode: 1892 | Score: -41.7 | Avg Score: -66.88 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 3 | Episode: 1893 | Score: -71.57 | Avg Score: -66.26 |\n",
"| Experiment: 3 | Episode: 1894 | Score: -96.47 | Avg Score: -66.5 |\n",
"| Experiment: 3 | Episode: 1895 | Score: 13.93 | Avg Score: -65.93 |\n",
"| Experiment: 3 | Episode: 1896 | Score: -78.64 | Avg Score: -65.04 |\n",
"| Experiment: 3 | Episode: 1897 | Score: -23.94 | Avg Score: -64.87 |\n",
"| Experiment: 3 | Episode: 1898 | Score: -2.38 | Avg Score: -64.01 |\n",
"| Experiment: 3 | Episode: 1899 | Score: -11.82 | Avg Score: -63.57 |\n",
"| Experiment: 3 | Episode: 1900 | Score: -132.42 | Avg Score: -63.4 |\n",
"| Experiment: 3 | Episode: 1901 | Score: -35.82 | Avg Score: -63.26 |\n",
"| Experiment: 3 | Episode: 1902 | Score: -68.75 | Avg Score: -63.14 |\n",
"| Experiment: 3 | Episode: 1903 | Score: -100.59 | Avg Score: -64.02 |\n",
"| Experiment: 3 | Episode: 1904 | Score: -52.95 | Avg Score: -63.7 |\n",
"| Experiment: 3 | Episode: 1905 | Score: -174.61 | Avg Score: -64.82 |\n",
"| Experiment: 3 | Episode: 1906 | Score: -50.62 | Avg Score: -65.07 |\n",
"| Experiment: 3 | Episode: 1907 | Score: -40.02 | Avg Score: -64.46 |\n",
"| Experiment: 3 | Episode: 1908 | Score: -38.26 | Avg Score: -64.56 |\n",
"| Experiment: 3 | Episode: 1909 | Score: -73.42 | Avg Score: -64.73 |\n",
"| Experiment: 3 | Episode: 1910 | Score: -68.81 | Avg Score: -65.64 |\n",
"| Experiment: 3 | Episode: 1911 | Score: -20.74 | Avg Score: -64.15 |\n",
"| Experiment: 3 | Episode: 1912 | Score: -88.82 | Avg Score: -64.68 |\n",
"| Experiment: 3 | Episode: 1913 | Score: -78.91 | Avg Score: -64.96 |\n",
"| Experiment: 3 | Episode: 1914 | Score: -63.91 | Avg Score: -65.62 |\n",
"| Experiment: 3 | Episode: 1915 | Score: -90.0 | Avg Score: -65.71 |\n",
"| Experiment: 3 | Episode: 1916 | Score: -116.05 | Avg Score: -66.3 |\n",
"| Experiment: 3 | Episode: 1917 | Score: -64.18 | Avg Score: -66.42 |\n",
"| Experiment: 3 | Episode: 1918 | Score: 8.57 | Avg Score: -66.38 |\n",
"| Experiment: 3 | Episode: 1919 | Score: -49.72 | Avg Score: -66.09 |\n",
"| Experiment: 3 | Episode: 1920 | Score: -56.1 | Avg Score: -64.42 |\n",
"| Experiment: 3 | Episode: 1921 | Score: -59.73 | Avg Score: -64.46 |\n",
"| Experiment: 3 | Episode: 1922 | Score: -236.9 | Avg Score: -65.93 |\n",
"| Experiment: 3 | Episode: 1923 | Score: -24.67 | Avg Score: -65.48 |\n",
"| Experiment: 3 | Episode: 1924 | Score: -83.21 | Avg Score: -65.1 |\n",
"| Experiment: 3 | Episode: 1925 | Score: -138.76 | Avg Score: -66.16 |\n",
"| Experiment: 3 | Episode: 1926 | Score: -90.18 | Avg Score: -66.31 |\n",
"| Experiment: 3 | Episode: 1927 | Score: -138.29 | Avg Score: -67.41 |\n",
"| Experiment: 3 | Episode: 1928 | Score: -198.26 | Avg Score: -68.46 |\n",
"| Experiment: 3 | Episode: 1929 | Score: -69.71 | Avg Score: -68.55 |\n",
"| Experiment: 3 | Episode: 1930 | Score: -65.94 | Avg Score: -68.4 |\n",
"| Experiment: 3 | Episode: 1931 | Score: -103.31 | Avg Score: -68.72 |\n",
"| Experiment: 3 | Episode: 1932 | Score: -54.12 | Avg Score: -68.52 |\n",
"| Experiment: 3 | Episode: 1933 | Score: -72.07 | Avg Score: -67.58 |\n",
"| Experiment: 3 | Episode: 1934 | Score: -64.57 | Avg Score: -67.26 |\n",
"| Experiment: 3 | Episode: 1935 | Score: -88.9 | Avg Score: -67.99 |\n",
"| Experiment: 3 | Episode: 1936 | Score: -55.56 | Avg Score: -68.62 |\n",
"| Experiment: 3 | Episode: 1937 | Score: -14.84 | Avg Score: -68.36 |\n",
"| Experiment: 3 | Episode: 1938 | Score: -60.38 | Avg Score: -68.39 |\n",
"| Experiment: 3 | Episode: 1939 | Score: -10.87 | Avg Score: -68.1 |\n",
"| Experiment: 3 | Episode: 1940 | Score: -70.57 | Avg Score: -68.11 |\n",
"| Experiment: 3 | Episode: 1941 | Score: -19.26 | Avg Score: -67.44 |\n",
"| Experiment: 3 | Episode: 1942 | Score: -121.34 | Avg Score: -68.24 |\n",
"| Experiment: 3 | Episode: 1943 | Score: -55.74 | Avg Score: -68.58 |\n",
"| Experiment: 3 | Episode: 1944 | Score: -121.84 | Avg Score: -67.22 |\n",
"| Experiment: 3 | Episode: 1945 | Score: -5.4 | Avg Score: -67.04 |\n",
"| Experiment: 3 | Episode: 1946 | Score: -115.45 | Avg Score: -66.22 |\n",
"| Experiment: 3 | Episode: 1947 | Score: -20.29 | Avg Score: -65.41 |\n",
"| Experiment: 3 | Episode: 1948 | Score: -71.43 | Avg Score: -65.7 |\n",
"| Experiment: 3 | Episode: 1949 | Score: -83.61 | Avg Score: -66.31 |\n",
"| Experiment: 3 | Episode: 1950 | Score: -98.72 | Avg Score: -67.38 |\n",
"| Experiment: 3 | Episode: 1951 | Score: -73.37 | Avg Score: -67.51 |\n",
"| Experiment: 3 | Episode: 1952 | Score: -112.06 | Avg Score: -68.37 |\n",
"| Experiment: 3 | Episode: 1953 | Score: -79.39 | Avg Score: -68.98 |\n",
"| Experiment: 3 | Episode: 1954 | Score: -72.73 | Avg Score: -69.05 |\n",
"| Experiment: 3 | Episode: 1955 | Score: -35.68 | Avg Score: -68.99 |\n",
"| Experiment: 3 | Episode: 1956 | Score: -38.29 | Avg Score: -68.04 |\n",
"| Experiment: 3 | Episode: 1957 | Score: -96.8 | Avg Score: -68.61 |\n",
"| Experiment: 3 | Episode: 1958 | Score: -17.42 | Avg Score: -68.07 |\n",
"| Experiment: 3 | Episode: 1959 | Score: -29.5 | Avg Score: -68.06 |\n",
"| Experiment: 3 | Episode: 1960 | Score: -8.35 | Avg Score: -67.32 |\n",
"| Experiment: 3 | Episode: 1961 | Score: -88.06 | Avg Score: -67.76 |\n",
"| Experiment: 3 | Episode: 1962 | Score: -56.56 | Avg Score: -67.62 |\n",
"| Experiment: 3 | Episode: 1963 | Score: -2.3 | Avg Score: -67.74 |\n",
"| Experiment: 3 | Episode: 1964 | Score: -12.15 | Avg Score: -67.1 |\n",
"| Experiment: 3 | Episode: 1965 | Score: -33.73 | Avg Score: -66.99 |\n",
"| Experiment: 3 | Episode: 1966 | Score: -69.3 | Avg Score: -67.35 |\n",
"| Experiment: 3 | Episode: 1967 | Score: -32.59 | Avg Score: -66.62 |\n",
"| Experiment: 3 | Episode: 1968 | Score: -52.23 | Avg Score: -66.19 |\n",
"| Experiment: 3 | Episode: 1969 | Score: -97.46 | Avg Score: -66.6 |\n",
"| Experiment: 3 | Episode: 1970 | Score: -63.48 | Avg Score: -66.22 |\n",
"| Experiment: 3 | Episode: 1971 | Score: -171.05 | Avg Score: -67.55 |\n",
"| Experiment: 3 | Episode: 1972 | Score: -51.59 | Avg Score: -67.63 |\n",
"| Experiment: 3 | Episode: 1973 | Score: -86.12 | Avg Score: -68.03 |\n",
"| Experiment: 3 | Episode: 1974 | Score: -61.49 | Avg Score: -67.87 |\n",
"| Experiment: 3 | Episode: 1975 | Score: -50.96 | Avg Score: -67.8 |\n",
"| Experiment: 3 | Episode: 1976 | Score: -107.36 | Avg Score: -68.49 |\n",
"| Experiment: 3 | Episode: 1977 | Score: -16.92 | Avg Score: -68.54 |\n",
"| Experiment: 3 | Episode: 1978 | Score: -26.09 | Avg Score: -68.15 |\n",
"| Experiment: 3 | Episode: 1979 | Score: -37.66 | Avg Score: -67.52 |\n",
"| Experiment: 3 | Episode: 1980 | Score: -40.11 | Avg Score: -67.85 |\n",
"| Experiment: 3 | Episode: 1981 | Score: -95.1 | Avg Score: -68.13 |\n",
"| Experiment: 3 | Episode: 1982 | Score: -79.68 | Avg Score: -67.34 |\n",
"| Experiment: 3 | Episode: 1983 | Score: -96.77 | Avg Score: -67.74 |\n",
"| Experiment: 3 | Episode: 1984 | Score: -224.04 | Avg Score: -68.46 |\n",
"| Experiment: 3 | Episode: 1985 | Score: -47.08 | Avg Score: -68.5 |\n",
"| Experiment: 3 | Episode: 1986 | Score: -96.41 | Avg Score: -69.11 |\n",
"| Experiment: 3 | Episode: 1987 | Score: -85.9 | Avg Score: -68.87 |\n",
"| Experiment: 3 | Episode: 1988 | Score: -28.79 | Avg Score: -68.5 |\n",
"| Experiment: 3 | Episode: 1989 | Score: -58.31 | Avg Score: -68.78 |\n",
"| Experiment: 3 | Episode: 1990 | Score: -92.27 | Avg Score: -69.16 |\n",
"| Experiment: 3 | Episode: 1991 | Score: -43.37 | Avg Score: -68.51 |\n",
"| Experiment: 3 | Episode: 1992 | Score: -102.37 | Avg Score: -69.12 |\n",
"| Experiment: 3 | Episode: 1993 | Score: -87.25 | Avg Score: -69.27 |\n",
"| Experiment: 3 | Episode: 1994 | Score: -40.51 | Avg Score: -68.72 |\n",
"| Experiment: 3 | Episode: 1995 | Score: -51.16 | Avg Score: -69.37 |\n",
"| Experiment: 3 | Episode: 1996 | Score: 21.97 | Avg Score: -68.36 |\n",
"| Experiment: 3 | Episode: 1997 | Score: 36.98 | Avg Score: -67.75 |\n",
"| Experiment: 3 | Episode: 1998 | Score: -48.32 | Avg Score: -68.21 |\n",
"| Experiment: 3 | Episode: 1999 | Score: -56.64 | Avg Score: -68.66 |\n",
"| Experiment: 4 | Episode: 0 | Score: -190.01 | Avg Score: -190.01 |\n",
"| Experiment: 4 | Episode: 1 | Score: -55.75 | Avg Score: -122.88 |\n",
"| Experiment: 4 | Episode: 2 | Score: -97.26 | Avg Score: -114.34 |\n",
"| Experiment: 4 | Episode: 3 | Score: -151.75 | Avg Score: -123.69 |\n",
"| Experiment: 4 | Episode: 4 | Score: -74.92 | Avg Score: -113.94 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 4 | Episode: 5 | Score: -78.04 | Avg Score: -107.96 |\n",
"| Experiment: 4 | Episode: 6 | Score: -286.45 | Avg Score: -133.46 |\n",
"| Experiment: 4 | Episode: 7 | Score: -394.74 | Avg Score: -166.12 |\n",
"| Experiment: 4 | Episode: 8 | Score: -381.93 | Avg Score: -190.1 |\n",
"| Experiment: 4 | Episode: 9 | Score: -233.17 | Avg Score: -194.4 |\n",
"| Experiment: 4 | Episode: 10 | Score: -93.86 | Avg Score: -185.26 |\n",
"| Experiment: 4 | Episode: 11 | Score: -263.04 | Avg Score: -191.74 |\n",
"| Experiment: 4 | Episode: 12 | Score: -349.63 | Avg Score: -203.89 |\n",
"| Experiment: 4 | Episode: 13 | Score: -48.65 | Avg Score: -192.8 |\n",
"| Experiment: 4 | Episode: 14 | Score: -343.36 | Avg Score: -202.84 |\n",
"| Experiment: 4 | Episode: 15 | Score: -69.05 | Avg Score: -194.48 |\n",
"| Experiment: 4 | Episode: 16 | Score: -33.33 | Avg Score: -185.0 |\n",
"| Experiment: 4 | Episode: 17 | Score: -60.02 | Avg Score: -178.05 |\n",
"| Experiment: 4 | Episode: 18 | Score: -125.79 | Avg Score: -175.3 |\n",
"| Experiment: 4 | Episode: 19 | Score: -50.46 | Avg Score: -169.06 |\n",
"| Experiment: 4 | Episode: 20 | Score: -621.22 | Avg Score: -190.59 |\n",
"| Experiment: 4 | Episode: 21 | Score: -148.99 | Avg Score: -188.7 |\n",
"| Experiment: 4 | Episode: 22 | Score: -302.06 | Avg Score: -193.63 |\n",
"| Experiment: 4 | Episode: 23 | Score: -433.75 | Avg Score: -203.64 |\n",
"| Experiment: 4 | Episode: 24 | Score: -212.8 | Avg Score: -204.0 |\n",
"| Experiment: 4 | Episode: 25 | Score: -173.78 | Avg Score: -202.84 |\n",
"| Experiment: 4 | Episode: 26 | Score: -149.4 | Avg Score: -200.86 |\n",
"| Experiment: 4 | Episode: 27 | Score: -309.53 | Avg Score: -204.74 |\n",
"| Experiment: 4 | Episode: 28 | Score: -254.06 | Avg Score: -206.44 |\n",
"| Experiment: 4 | Episode: 29 | Score: -208.83 | Avg Score: -206.52 |\n",
"| Experiment: 4 | Episode: 30 | Score: -302.73 | Avg Score: -209.63 |\n",
"| Experiment: 4 | Episode: 31 | Score: -260.48 | Avg Score: -211.21 |\n",
"| Experiment: 4 | Episode: 32 | Score: -145.04 | Avg Score: -209.21 |\n",
"| Experiment: 4 | Episode: 33 | Score: -145.56 | Avg Score: -207.34 |\n",
"| Experiment: 4 | Episode: 34 | Score: -240.44 | Avg Score: -208.28 |\n",
"| Experiment: 4 | Episode: 35 | Score: -268.33 | Avg Score: -209.95 |\n",
"| Experiment: 4 | Episode: 36 | Score: -123.08 | Avg Score: -207.6 |\n",
"| Experiment: 4 | Episode: 37 | Score: -84.31 | Avg Score: -204.36 |\n",
"| Experiment: 4 | Episode: 38 | Score: -172.71 | Avg Score: -203.55 |\n",
"| Experiment: 4 | Episode: 39 | Score: -355.19 | Avg Score: -207.34 |\n",
"| Experiment: 4 | Episode: 40 | Score: -440.55 | Avg Score: -213.03 |\n",
"| Experiment: 4 | Episode: 41 | Score: -256.02 | Avg Score: -214.05 |\n",
"| Experiment: 4 | Episode: 42 | Score: -122.14 | Avg Score: -211.91 |\n",
"| Experiment: 4 | Episode: 43 | Score: -62.73 | Avg Score: -208.52 |\n",
"| Experiment: 4 | Episode: 44 | Score: -94.8 | Avg Score: -205.99 |\n",
"| Experiment: 4 | Episode: 45 | Score: -279.37 | Avg Score: -207.59 |\n",
"| Experiment: 4 | Episode: 46 | Score: -124.44 | Avg Score: -205.82 |\n",
"| Experiment: 4 | Episode: 47 | Score: -85.14 | Avg Score: -203.31 |\n",
"| Experiment: 4 | Episode: 48 | Score: -185.24 | Avg Score: -202.94 |\n",
"| Experiment: 4 | Episode: 49 | Score: -203.38 | Avg Score: -202.95 |\n",
"| Experiment: 4 | Episode: 50 | Score: -298.07 | Avg Score: -204.81 |\n",
"| Experiment: 4 | Episode: 51 | Score: -390.31 | Avg Score: -208.38 |\n",
"| Experiment: 4 | Episode: 52 | Score: -62.96 | Avg Score: -205.64 |\n",
"| Experiment: 4 | Episode: 53 | Score: -103.86 | Avg Score: -203.75 |\n",
"| Experiment: 4 | Episode: 54 | Score: -155.52 | Avg Score: -202.87 |\n",
"| Experiment: 4 | Episode: 55 | Score: -44.84 | Avg Score: -200.05 |\n",
"| Experiment: 4 | Episode: 56 | Score: -257.84 | Avg Score: -201.07 |\n",
"| Experiment: 4 | Episode: 57 | Score: -188.91 | Avg Score: -200.86 |\n",
"| Experiment: 4 | Episode: 58 | Score: -337.11 | Avg Score: -203.17 |\n",
"| Experiment: 4 | Episode: 59 | Score: -63.12 | Avg Score: -200.83 |\n",
"| Experiment: 4 | Episode: 60 | Score: -313.96 | Avg Score: -202.69 |\n",
"| Experiment: 4 | Episode: 61 | Score: -358.71 | Avg Score: -205.2 |\n",
"| Experiment: 4 | Episode: 62 | Score: -142.48 | Avg Score: -204.21 |\n",
"| Experiment: 4 | Episode: 63 | Score: -91.54 | Avg Score: -202.45 |\n",
"| Experiment: 4 | Episode: 64 | Score: -278.63 | Avg Score: -203.62 |\n",
"| Experiment: 4 | Episode: 65 | Score: -350.68 | Avg Score: -205.85 |\n",
"| Experiment: 4 | Episode: 66 | Score: -114.82 | Avg Score: -204.49 |\n",
"| Experiment: 4 | Episode: 67 | Score: 11.54 | Avg Score: -201.31 |\n",
"| Experiment: 4 | Episode: 68 | Score: -390.97 | Avg Score: -204.06 |\n",
"| Experiment: 4 | Episode: 69 | Score: -192.51 | Avg Score: -203.9 |\n",
"| Experiment: 4 | Episode: 70 | Score: -102.42 | Avg Score: -202.47 |\n",
"| Experiment: 4 | Episode: 71 | Score: -280.62 | Avg Score: -203.55 |\n",
"| Experiment: 4 | Episode: 72 | Score: -495.54 | Avg Score: -207.55 |\n",
"| Experiment: 4 | Episode: 73 | Score: -293.05 | Avg Score: -208.71 |\n",
"| Experiment: 4 | Episode: 74 | Score: -125.74 | Avg Score: -207.6 |\n",
"| Experiment: 4 | Episode: 75 | Score: -159.02 | Avg Score: -206.96 |\n",
"| Experiment: 4 | Episode: 76 | Score: -119.59 | Avg Score: -205.83 |\n",
"| Experiment: 4 | Episode: 77 | Score: -84.03 | Avg Score: -204.26 |\n",
"| Experiment: 4 | Episode: 78 | Score: -257.08 | Avg Score: -204.93 |\n",
"| Experiment: 4 | Episode: 79 | Score: -351.35 | Avg Score: -206.76 |\n",
"| Experiment: 4 | Episode: 80 | Score: -206.62 | Avg Score: -206.76 |\n",
"| Experiment: 4 | Episode: 81 | Score: -236.73 | Avg Score: -207.13 |\n",
"| Experiment: 4 | Episode: 82 | Score: -259.87 | Avg Score: -207.76 |\n",
"| Experiment: 4 | Episode: 83 | Score: -105.04 | Avg Score: -206.54 |\n",
"| Experiment: 4 | Episode: 84 | Score: -185.96 | Avg Score: -206.3 |\n",
"| Experiment: 4 | Episode: 85 | Score: -502.66 | Avg Score: -209.74 |\n",
"| Experiment: 4 | Episode: 86 | Score: -94.24 | Avg Score: -208.42 |\n",
"| Experiment: 4 | Episode: 87 | Score: -232.8 | Avg Score: -208.69 |\n",
"| Experiment: 4 | Episode: 88 | Score: -194.91 | Avg Score: -208.54 |\n",
"| Experiment: 4 | Episode: 89 | Score: -72.11 | Avg Score: -207.02 |\n",
"| Experiment: 4 | Episode: 90 | Score: -249.94 | Avg Score: -207.49 |\n",
"| Experiment: 4 | Episode: 91 | Score: -253.34 | Avg Score: -207.99 |\n",
"| Experiment: 4 | Episode: 92 | Score: 1.27 | Avg Score: -205.74 |\n",
"| Experiment: 4 | Episode: 93 | Score: -134.46 | Avg Score: -204.98 |\n",
"| Experiment: 4 | Episode: 94 | Score: -167.12 | Avg Score: -204.59 |\n",
"| Experiment: 4 | Episode: 95 | Score: -194.31 | Avg Score: -204.48 |\n",
"| Experiment: 4 | Episode: 96 | Score: -267.05 | Avg Score: -205.12 |\n",
"| Experiment: 4 | Episode: 97 | Score: -427.85 | Avg Score: -207.4 |\n",
"| Experiment: 4 | Episode: 98 | Score: -59.75 | Avg Score: -205.9 |\n",
"| Experiment: 4 | Episode: 99 | Score: -56.96 | Avg Score: -204.42 |\n",
"| Experiment: 4 | Episode: 100 | Score: -337.61 | Avg Score: -205.89 |\n",
"| Experiment: 4 | Episode: 101 | Score: -95.41 | Avg Score: -206.29 |\n",
"| Experiment: 4 | Episode: 102 | Score: -168.91 | Avg Score: -207.0 |\n",
"| Experiment: 4 | Episode: 103 | Score: -155.76 | Avg Score: -207.04 |\n",
"| Experiment: 4 | Episode: 104 | Score: -174.42 | Avg Score: -208.04 |\n",
"| Experiment: 4 | Episode: 105 | Score: -361.45 | Avg Score: -210.87 |\n",
"| Experiment: 4 | Episode: 106 | Score: -207.68 | Avg Score: -210.09 |\n",
"| Experiment: 4 | Episode: 107 | Score: -373.38 | Avg Score: -209.87 |\n",
"| Experiment: 4 | Episode: 108 | Score: 119.02 | Avg Score: -204.86 |\n",
"| Experiment: 4 | Episode: 109 | Score: -362.51 | Avg Score: -206.16 |\n",
"| Experiment: 4 | Episode: 110 | Score: -159.1 | Avg Score: -206.81 |\n",
"| Experiment: 4 | Episode: 111 | Score: -186.65 | Avg Score: -206.04 |\n",
"| Experiment: 4 | Episode: 112 | Score: -130.37 | Avg Score: -203.85 |\n",
"| Experiment: 4 | Episode: 113 | Score: -326.47 | Avg Score: -206.63 |\n",
"| Experiment: 4 | Episode: 114 | Score: -295.41 | Avg Score: -206.15 |\n",
"| Experiment: 4 | Episode: 115 | Score: -460.56 | Avg Score: -210.07 |\n",
"| Experiment: 4 | Episode: 116 | Score: -376.34 | Avg Score: -213.5 |\n",
"| Experiment: 4 | Episode: 117 | Score: -265.47 | Avg Score: -215.55 |\n",
"| Experiment: 4 | Episode: 118 | Score: -197.07 | Avg Score: -216.26 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 4 | Episode: 119 | Score: -293.31 | Avg Score: -218.69 |\n",
"| Experiment: 4 | Episode: 120 | Score: -336.63 | Avg Score: -215.85 |\n",
"| Experiment: 4 | Episode: 121 | Score: -142.15 | Avg Score: -215.78 |\n",
"| Experiment: 4 | Episode: 122 | Score: -103.03 | Avg Score: -213.79 |\n",
"| Experiment: 4 | Episode: 123 | Score: -212.36 | Avg Score: -211.57 |\n",
"| Experiment: 4 | Episode: 124 | Score: -178.29 | Avg Score: -211.23 |\n",
"| Experiment: 4 | Episode: 125 | Score: -94.86 | Avg Score: -210.44 |\n",
"| Experiment: 4 | Episode: 126 | Score: -208.15 | Avg Score: -211.03 |\n",
"| Experiment: 4 | Episode: 127 | Score: -326.18 | Avg Score: -211.19 |\n",
"| Experiment: 4 | Episode: 128 | Score: -162.89 | Avg Score: -210.28 |\n",
"| Experiment: 4 | Episode: 129 | Score: -47.01 | Avg Score: -208.66 |\n",
"| Experiment: 4 | Episode: 130 | Score: -321.87 | Avg Score: -208.85 |\n",
"| Experiment: 4 | Episode: 131 | Score: -119.82 | Avg Score: -207.45 |\n",
"| Experiment: 4 | Episode: 132 | Score: -69.54 | Avg Score: -206.69 |\n",
"| Experiment: 4 | Episode: 133 | Score: -213.62 | Avg Score: -207.37 |\n",
"| Experiment: 4 | Episode: 134 | Score: -338.33 | Avg Score: -208.35 |\n",
"| Experiment: 4 | Episode: 135 | Score: -88.59 | Avg Score: -206.55 |\n",
"| Experiment: 4 | Episode: 136 | Score: -9.73 | Avg Score: -205.42 |\n",
"| Experiment: 4 | Episode: 137 | Score: -59.47 | Avg Score: -205.17 |\n",
"| Experiment: 4 | Episode: 138 | Score: -108.16 | Avg Score: -204.53 |\n",
"| Experiment: 4 | Episode: 139 | Score: -368.47 | Avg Score: -204.66 |\n",
"| Experiment: 4 | Episode: 140 | Score: -305.05 | Avg Score: -203.31 |\n",
"| Experiment: 4 | Episode: 141 | Score: -261.03 | Avg Score: -203.36 |\n",
"| Experiment: 4 | Episode: 142 | Score: -66.43 | Avg Score: -202.8 |\n",
"| Experiment: 4 | Episode: 143 | Score: -137.41 | Avg Score: -203.55 |\n",
"| Experiment: 4 | Episode: 144 | Score: -65.17 | Avg Score: -203.25 |\n",
"| Experiment: 4 | Episode: 145 | Score: -22.78 | Avg Score: -200.68 |\n",
"| Experiment: 4 | Episode: 146 | Score: -120.64 | Avg Score: -200.65 |\n",
"| Experiment: 4 | Episode: 147 | Score: -146.3 | Avg Score: -201.26 |\n",
"| Experiment: 4 | Episode: 148 | Score: -351.42 | Avg Score: -202.92 |\n",
"| Experiment: 4 | Episode: 149 | Score: -160.52 | Avg Score: -202.49 |\n",
"| Experiment: 4 | Episode: 150 | Score: -33.88 | Avg Score: -199.85 |\n",
"| Experiment: 4 | Episode: 151 | Score: -294.77 | Avg Score: -198.89 |\n",
"| Experiment: 4 | Episode: 152 | Score: -122.67 | Avg Score: -199.49 |\n",
"| Experiment: 4 | Episode: 153 | Score: -126.31 | Avg Score: -199.71 |\n",
"| Experiment: 4 | Episode: 154 | Score: -117.43 | Avg Score: -199.33 |\n",
"| Experiment: 4 | Episode: 155 | Score: -48.54 | Avg Score: -199.37 |\n",
"| Experiment: 4 | Episode: 156 | Score: -169.56 | Avg Score: -198.49 |\n",
"| Experiment: 4 | Episode: 157 | Score: -70.76 | Avg Score: -197.31 |\n",
"| Experiment: 4 | Episode: 158 | Score: -137.67 | Avg Score: -195.31 |\n",
"| Experiment: 4 | Episode: 159 | Score: -126.68 | Avg Score: -195.95 |\n",
"| Experiment: 4 | Episode: 160 | Score: -136.17 | Avg Score: -194.17 |\n",
"| Experiment: 4 | Episode: 161 | Score: -253.6 | Avg Score: -193.12 |\n",
"| Experiment: 4 | Episode: 162 | Score: -127.89 | Avg Score: -192.97 |\n",
"| Experiment: 4 | Episode: 163 | Score: -157.93 | Avg Score: -193.64 |\n",
"| Experiment: 4 | Episode: 164 | Score: -100.75 | Avg Score: -191.86 |\n",
"| Experiment: 4 | Episode: 165 | Score: -66.43 | Avg Score: -189.01 |\n",
"| Experiment: 4 | Episode: 166 | Score: -182.3 | Avg Score: -189.69 |\n",
"| Experiment: 4 | Episode: 167 | Score: -101.47 | Avg Score: -190.82 |\n",
"| Experiment: 4 | Episode: 168 | Score: -6.48 | Avg Score: -186.97 |\n",
"| Experiment: 4 | Episode: 169 | Score: -140.3 | Avg Score: -186.45 |\n",
"| Experiment: 4 | Episode: 170 | Score: -161.36 | Avg Score: -187.04 |\n",
"| Experiment: 4 | Episode: 171 | Score: -158.25 | Avg Score: -185.82 |\n",
"| Experiment: 4 | Episode: 172 | Score: -88.14 | Avg Score: -181.74 |\n",
"| Experiment: 4 | Episode: 173 | Score: -238.97 | Avg Score: -181.2 |\n",
"| Experiment: 4 | Episode: 174 | Score: -41.08 | Avg Score: -180.36 |\n",
"| Experiment: 4 | Episode: 175 | Score: -60.63 | Avg Score: -179.37 |\n",
"| Experiment: 4 | Episode: 176 | Score: -160.01 | Avg Score: -179.78 |\n",
"| Experiment: 4 | Episode: 177 | Score: -333.45 | Avg Score: -182.27 |\n",
"| Experiment: 4 | Episode: 178 | Score: -109.1 | Avg Score: -180.79 |\n",
"| Experiment: 4 | Episode: 179 | Score: -78.77 | Avg Score: -178.07 |\n",
"| Experiment: 4 | Episode: 180 | Score: -268.43 | Avg Score: -178.68 |\n",
"| Experiment: 4 | Episode: 181 | Score: -323.72 | Avg Score: -179.55 |\n",
"| Experiment: 4 | Episode: 182 | Score: -68.87 | Avg Score: -177.64 |\n",
"| Experiment: 4 | Episode: 183 | Score: -53.16 | Avg Score: -177.13 |\n",
"| Experiment: 4 | Episode: 184 | Score: -61.7 | Avg Score: -175.88 |\n",
"| Experiment: 4 | Episode: 185 | Score: -34.84 | Avg Score: -171.2 |\n",
"| Experiment: 4 | Episode: 186 | Score: -499.72 | Avg Score: -175.26 |\n",
"| Experiment: 4 | Episode: 187 | Score: -66.67 | Avg Score: -173.6 |\n",
"| Experiment: 4 | Episode: 188 | Score: -231.83 | Avg Score: -173.97 |\n",
"| Experiment: 4 | Episode: 189 | Score: -208.89 | Avg Score: -175.33 |\n",
"| Experiment: 4 | Episode: 190 | Score: -118.78 | Avg Score: -174.02 |\n",
"| Experiment: 4 | Episode: 191 | Score: -250.76 | Avg Score: -174.0 |\n",
"| Experiment: 4 | Episode: 192 | Score: -85.9 | Avg Score: -174.87 |\n",
"| Experiment: 4 | Episode: 193 | Score: -338.55 | Avg Score: -176.91 |\n",
"| Experiment: 4 | Episode: 194 | Score: -155.91 | Avg Score: -176.8 |\n",
"| Experiment: 4 | Episode: 195 | Score: -47.24 | Avg Score: -175.33 |\n",
"| Experiment: 4 | Episode: 196 | Score: -103.07 | Avg Score: -173.69 |\n",
"| Experiment: 4 | Episode: 197 | Score: -164.41 | Avg Score: -171.05 |\n",
"| Experiment: 4 | Episode: 198 | Score: -59.07 | Avg Score: -171.05 |\n",
"| Experiment: 4 | Episode: 199 | Score: -139.62 | Avg Score: -171.87 |\n",
"| Experiment: 4 | Episode: 200 | Score: -110.25 | Avg Score: -169.6 |\n",
"| Experiment: 4 | Episode: 201 | Score: -194.68 | Avg Score: -170.59 |\n",
"| Experiment: 4 | Episode: 202 | Score: -276.91 | Avg Score: -171.67 |\n",
"| Experiment: 4 | Episode: 203 | Score: -63.05 | Avg Score: -170.74 |\n",
"| Experiment: 4 | Episode: 204 | Score: -92.9 | Avg Score: -169.93 |\n",
"| Experiment: 4 | Episode: 205 | Score: -183.26 | Avg Score: -168.15 |\n",
"| Experiment: 4 | Episode: 206 | Score: -226.1 | Avg Score: -168.33 |\n",
"| Experiment: 4 | Episode: 207 | Score: -190.76 | Avg Score: -166.51 |\n",
"| Experiment: 4 | Episode: 208 | Score: -86.66 | Avg Score: -168.56 |\n",
"| Experiment: 4 | Episode: 209 | Score: -99.82 | Avg Score: -165.94 |\n",
"| Experiment: 4 | Episode: 210 | Score: -297.95 | Avg Score: -167.32 |\n",
"| Experiment: 4 | Episode: 211 | Score: -104.71 | Avg Score: -166.5 |\n",
"| Experiment: 4 | Episode: 212 | Score: -87.5 | Avg Score: -166.08 |\n",
"| Experiment: 4 | Episode: 213 | Score: -95.57 | Avg Score: -163.77 |\n",
"| Experiment: 4 | Episode: 214 | Score: -209.93 | Avg Score: -162.91 |\n",
"| Experiment: 4 | Episode: 215 | Score: -77.75 | Avg Score: -159.08 |\n",
"| Experiment: 4 | Episode: 216 | Score: -184.62 | Avg Score: -157.17 |\n",
"| Experiment: 4 | Episode: 217 | Score: -205.57 | Avg Score: -156.57 |\n",
"| Experiment: 4 | Episode: 218 | Score: -237.54 | Avg Score: -156.97 |\n",
"| Experiment: 4 | Episode: 219 | Score: -125.16 | Avg Score: -155.29 |\n",
"| Experiment: 4 | Episode: 220 | Score: -190.02 | Avg Score: -153.82 |\n",
"| Experiment: 4 | Episode: 221 | Score: -241.04 | Avg Score: -154.81 |\n",
"| Experiment: 4 | Episode: 222 | Score: -101.45 | Avg Score: -154.8 |\n",
"| Experiment: 4 | Episode: 223 | Score: -38.17 | Avg Score: -153.06 |\n",
"| Experiment: 4 | Episode: 224 | Score: -69.6 | Avg Score: -151.97 |\n",
"| Experiment: 4 | Episode: 225 | Score: -21.94 | Avg Score: -151.24 |\n",
"| Experiment: 4 | Episode: 226 | Score: -93.38 | Avg Score: -150.09 |\n",
"| Experiment: 4 | Episode: 227 | Score: -227.67 | Avg Score: -149.11 |\n",
"| Experiment: 4 | Episode: 228 | Score: -108.11 | Avg Score: -148.56 |\n",
"| Experiment: 4 | Episode: 229 | Score: -107.61 | Avg Score: -149.17 |\n",
"| Experiment: 4 | Episode: 230 | Score: -63.33 | Avg Score: -146.58 |\n",
"| Experiment: 4 | Episode: 231 | Score: -284.2 | Avg Score: -148.22 |\n",
"| Experiment: 4 | Episode: 232 | Score: 4.13 | Avg Score: -147.49 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 4 | Episode: 233 | Score: -67.05 | Avg Score: -146.02 |\n",
"| Experiment: 4 | Episode: 234 | Score: -101.64 | Avg Score: -143.65 |\n",
"| Experiment: 4 | Episode: 235 | Score: -59.27 | Avg Score: -143.36 |\n",
"| Experiment: 4 | Episode: 236 | Score: -259.71 | Avg Score: -145.86 |\n",
"| Experiment: 4 | Episode: 237 | Score: -121.77 | Avg Score: -146.48 |\n",
"| Experiment: 4 | Episode: 238 | Score: -291.97 | Avg Score: -148.32 |\n",
"| Experiment: 4 | Episode: 239 | Score: -104.78 | Avg Score: -145.69 |\n",
"| Experiment: 4 | Episode: 240 | Score: -336.32 | Avg Score: -146.0 |\n",
"| Experiment: 4 | Episode: 241 | Score: -260.22 | Avg Score: -145.99 |\n",
"| Experiment: 4 | Episode: 242 | Score: -120.28 | Avg Score: -146.53 |\n",
"| Experiment: 4 | Episode: 243 | Score: -74.77 | Avg Score: -145.9 |\n",
"| Experiment: 4 | Episode: 244 | Score: -360.24 | Avg Score: -148.85 |\n",
"| Experiment: 4 | Episode: 245 | Score: -82.91 | Avg Score: -149.45 |\n",
"| Experiment: 4 | Episode: 246 | Score: -221.64 | Avg Score: -150.46 |\n",
"| Experiment: 4 | Episode: 247 | Score: -379.04 | Avg Score: -152.79 |\n",
"| Experiment: 4 | Episode: 248 | Score: -118.2 | Avg Score: -150.46 |\n",
"| Experiment: 4 | Episode: 249 | Score: -124.04 | Avg Score: -150.09 |\n",
"| Experiment: 4 | Episode: 250 | Score: -77.8 | Avg Score: -150.53 |\n",
"| Experiment: 4 | Episode: 251 | Score: -160.2 | Avg Score: -149.19 |\n",
"| Experiment: 4 | Episode: 252 | Score: -308.06 | Avg Score: -151.04 |\n",
"| Experiment: 4 | Episode: 253 | Score: -155.22 | Avg Score: -151.33 |\n",
"| Experiment: 4 | Episode: 254 | Score: 34.66 | Avg Score: -149.81 |\n",
"| Experiment: 4 | Episode: 255 | Score: -357.0 | Avg Score: -152.89 |\n",
"| Experiment: 4 | Episode: 256 | Score: -68.81 | Avg Score: -151.89 |\n",
"| Experiment: 4 | Episode: 257 | Score: -120.81 | Avg Score: -152.39 |\n",
"| Experiment: 4 | Episode: 258 | Score: -208.07 | Avg Score: -153.09 |\n",
"| Experiment: 4 | Episode: 259 | Score: -92.56 | Avg Score: -152.75 |\n",
"| Experiment: 4 | Episode: 260 | Score: -115.44 | Avg Score: -152.54 |\n",
"| Experiment: 4 | Episode: 261 | Score: -110.5 | Avg Score: -151.11 |\n",
"| Experiment: 4 | Episode: 262 | Score: -88.4 | Avg Score: -150.72 |\n",
"| Experiment: 4 | Episode: 263 | Score: -396.73 | Avg Score: -153.11 |\n",
"| Experiment: 4 | Episode: 264 | Score: -251.7 | Avg Score: -154.61 |\n",
"| Experiment: 4 | Episode: 265 | Score: -79.09 | Avg Score: -154.74 |\n",
"| Experiment: 4 | Episode: 266 | Score: -106.83 | Avg Score: -153.99 |\n",
"| Experiment: 4 | Episode: 267 | Score: -95.36 | Avg Score: -153.93 |\n",
"| Experiment: 4 | Episode: 268 | Score: -68.71 | Avg Score: -154.55 |\n",
"| Experiment: 4 | Episode: 269 | Score: -109.0 | Avg Score: -154.23 |\n",
"| Experiment: 4 | Episode: 270 | Score: -260.78 | Avg Score: -155.23 |\n",
"| Experiment: 4 | Episode: 271 | Score: -164.82 | Avg Score: -155.29 |\n",
"| Experiment: 4 | Episode: 272 | Score: -236.18 | Avg Score: -156.77 |\n",
"| Experiment: 4 | Episode: 273 | Score: -141.43 | Avg Score: -155.8 |\n",
"| Experiment: 4 | Episode: 274 | Score: -182.5 | Avg Score: -157.21 |\n",
"| Experiment: 4 | Episode: 275 | Score: -235.48 | Avg Score: -158.96 |\n",
"| Experiment: 4 | Episode: 276 | Score: -289.85 | Avg Score: -160.26 |\n",
"| Experiment: 4 | Episode: 277 | Score: -115.72 | Avg Score: -158.08 |\n",
"| Experiment: 4 | Episode: 278 | Score: -105.63 | Avg Score: -158.05 |\n",
"| Experiment: 4 | Episode: 279 | Score: -38.95 | Avg Score: -157.65 |\n",
"| Experiment: 4 | Episode: 280 | Score: -64.69 | Avg Score: -155.61 |\n",
"| Experiment: 4 | Episode: 281 | Score: -183.52 | Avg Score: -154.21 |\n",
"| Experiment: 4 | Episode: 282 | Score: -110.29 | Avg Score: -154.63 |\n",
"| Experiment: 4 | Episode: 283 | Score: -67.82 | Avg Score: -154.77 |\n",
"| Experiment: 4 | Episode: 284 | Score: -84.34 | Avg Score: -155.0 |\n",
"| Experiment: 4 | Episode: 285 | Score: -85.36 | Avg Score: -155.5 |\n",
"| Experiment: 4 | Episode: 286 | Score: -78.34 | Avg Score: -151.29 |\n",
"| Experiment: 4 | Episode: 287 | Score: -84.92 | Avg Score: -151.47 |\n",
"| Experiment: 4 | Episode: 288 | Score: -193.84 | Avg Score: -151.09 |\n",
"| Experiment: 4 | Episode: 289 | Score: -97.2 | Avg Score: -149.98 |\n",
"| Experiment: 4 | Episode: 290 | Score: -55.75 | Avg Score: -149.35 |\n",
"| Experiment: 4 | Episode: 291 | Score: -185.17 | Avg Score: -148.69 |\n",
"| Experiment: 4 | Episode: 292 | Score: -225.21 | Avg Score: -150.08 |\n",
"| Experiment: 4 | Episode: 293 | Score: -211.1 | Avg Score: -148.81 |\n",
"| Experiment: 4 | Episode: 294 | Score: -150.63 | Avg Score: -148.76 |\n",
"| Experiment: 4 | Episode: 295 | Score: -193.1 | Avg Score: -150.21 |\n",
"| Experiment: 4 | Episode: 296 | Score: -72.29 | Avg Score: -149.91 |\n",
"| Experiment: 4 | Episode: 297 | Score: -275.24 | Avg Score: -151.01 |\n",
"| Experiment: 4 | Episode: 298 | Score: -401.34 | Avg Score: -154.44 |\n",
"| Experiment: 4 | Episode: 299 | Score: -118.53 | Avg Score: -154.23 |\n",
"| Experiment: 4 | Episode: 300 | Score: 2.2 | Avg Score: -153.1 |\n",
"| Experiment: 4 | Episode: 301 | Score: -157.06 | Avg Score: -152.73 |\n",
"| Experiment: 4 | Episode: 302 | Score: -113.55 | Avg Score: -151.09 |\n",
"| Experiment: 4 | Episode: 303 | Score: -103.49 | Avg Score: -151.5 |\n",
"| Experiment: 4 | Episode: 304 | Score: -277.77 | Avg Score: -153.34 |\n",
"| Experiment: 4 | Episode: 305 | Score: -251.08 | Avg Score: -154.02 |\n",
"| Experiment: 4 | Episode: 306 | Score: -233.44 | Avg Score: -154.1 |\n",
"| Experiment: 4 | Episode: 307 | Score: -123.78 | Avg Score: -153.43 |\n",
"| Experiment: 4 | Episode: 308 | Score: -105.26 | Avg Score: -153.61 |\n",
"| Experiment: 4 | Episode: 309 | Score: -196.56 | Avg Score: -154.58 |\n",
"| Experiment: 4 | Episode: 310 | Score: -203.91 | Avg Score: -153.64 |\n",
"| Experiment: 4 | Episode: 311 | Score: -104.13 | Avg Score: -153.63 |\n",
"| Experiment: 4 | Episode: 312 | Score: -121.23 | Avg Score: -153.97 |\n",
"| Experiment: 4 | Episode: 313 | Score: -234.59 | Avg Score: -155.36 |\n",
"| Experiment: 4 | Episode: 314 | Score: -151.09 | Avg Score: -154.77 |\n",
"| Experiment: 4 | Episode: 315 | Score: -105.75 | Avg Score: -155.05 |\n",
"| Experiment: 4 | Episode: 316 | Score: -97.37 | Avg Score: -154.18 |\n",
"| Experiment: 4 | Episode: 317 | Score: -84.5 | Avg Score: -152.97 |\n",
"| Experiment: 4 | Episode: 318 | Score: -112.31 | Avg Score: -151.72 |\n",
"| Experiment: 4 | Episode: 319 | Score: -63.93 | Avg Score: -151.11 |\n",
"| Experiment: 4 | Episode: 320 | Score: -263.55 | Avg Score: -151.84 |\n",
"| Experiment: 4 | Episode: 321 | Score: -104.57 | Avg Score: -150.48 |\n",
"| Experiment: 4 | Episode: 322 | Score: -76.33 | Avg Score: -150.22 |\n",
"| Experiment: 4 | Episode: 323 | Score: -84.47 | Avg Score: -150.69 |\n",
"| Experiment: 4 | Episode: 324 | Score: -137.88 | Avg Score: -151.37 |\n",
"| Experiment: 4 | Episode: 325 | Score: -76.44 | Avg Score: -151.92 |\n",
"| Experiment: 4 | Episode: 326 | Score: -115.2 | Avg Score: -152.13 |\n",
"| Experiment: 4 | Episode: 327 | Score: -90.51 | Avg Score: -150.76 |\n",
"| Experiment: 4 | Episode: 328 | Score: -85.18 | Avg Score: -150.53 |\n",
"| Experiment: 4 | Episode: 329 | Score: -69.18 | Avg Score: -150.15 |\n",
"| Experiment: 4 | Episode: 330 | Score: -71.61 | Avg Score: -150.23 |\n",
"| Experiment: 4 | Episode: 331 | Score: -164.98 | Avg Score: -149.04 |\n",
"| Experiment: 4 | Episode: 332 | Score: -61.5 | Avg Score: -149.7 |\n",
"| Experiment: 4 | Episode: 333 | Score: -74.29 | Avg Score: -149.77 |\n",
"| Experiment: 4 | Episode: 334 | Score: -129.59 | Avg Score: -150.05 |\n",
"| Experiment: 4 | Episode: 335 | Score: -141.84 | Avg Score: -150.87 |\n",
"| Experiment: 4 | Episode: 336 | Score: -106.94 | Avg Score: -149.35 |\n",
"| Experiment: 4 | Episode: 337 | Score: -80.07 | Avg Score: -148.93 |\n",
"| Experiment: 4 | Episode: 338 | Score: -72.89 | Avg Score: -146.74 |\n",
"| Experiment: 4 | Episode: 339 | Score: -76.79 | Avg Score: -146.46 |\n",
"| Experiment: 4 | Episode: 340 | Score: -170.46 | Avg Score: -144.8 |\n",
"| Experiment: 4 | Episode: 341 | Score: -75.61 | Avg Score: -142.95 |\n",
"| Experiment: 4 | Episode: 342 | Score: -79.56 | Avg Score: -142.55 |\n",
"| Experiment: 4 | Episode: 343 | Score: 14.07 | Avg Score: -141.66 |\n",
"| Experiment: 4 | Episode: 344 | Score: -93.14 | Avg Score: -138.99 |\n",
"| Experiment: 4 | Episode: 345 | Score: -118.82 | Avg Score: -139.35 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 4 | Episode: 346 | Score: -132.32 | Avg Score: -138.45 |\n",
"| Experiment: 4 | Episode: 347 | Score: -138.43 | Avg Score: -136.05 |\n",
"| Experiment: 4 | Episode: 348 | Score: -68.13 | Avg Score: -135.55 |\n",
"| Experiment: 4 | Episode: 349 | Score: -48.39 | Avg Score: -134.79 |\n",
"| Experiment: 4 | Episode: 350 | Score: -195.22 | Avg Score: -135.96 |\n",
"| Experiment: 4 | Episode: 351 | Score: -193.93 | Avg Score: -136.3 |\n",
"| Experiment: 4 | Episode: 352 | Score: -51.65 | Avg Score: -133.74 |\n",
"| Experiment: 4 | Episode: 353 | Score: -111.59 | Avg Score: -133.3 |\n",
"| Experiment: 4 | Episode: 354 | Score: 11.97 | Avg Score: -133.53 |\n",
"| Experiment: 4 | Episode: 355 | Score: -58.94 | Avg Score: -130.55 |\n",
"| Experiment: 4 | Episode: 356 | Score: -100.4 | Avg Score: -130.86 |\n",
"| Experiment: 4 | Episode: 357 | Score: -60.29 | Avg Score: -130.26 |\n",
"| Experiment: 4 | Episode: 358 | Score: -87.86 | Avg Score: -129.05 |\n",
"| Experiment: 4 | Episode: 359 | Score: -119.63 | Avg Score: -129.33 |\n",
"| Experiment: 4 | Episode: 360 | Score: -164.7 | Avg Score: -129.82 |\n",
"| Experiment: 4 | Episode: 361 | Score: -194.08 | Avg Score: -130.65 |\n",
"| Experiment: 4 | Episode: 362 | Score: -70.24 | Avg Score: -130.47 |\n",
"| Experiment: 4 | Episode: 363 | Score: -238.14 | Avg Score: -128.89 |\n",
"| Experiment: 4 | Episode: 364 | Score: -156.25 | Avg Score: -127.93 |\n",
"| Experiment: 4 | Episode: 365 | Score: -88.44 | Avg Score: -128.03 |\n",
"| Experiment: 4 | Episode: 366 | Score: -341.44 | Avg Score: -130.37 |\n",
"| Experiment: 4 | Episode: 367 | Score: -176.16 | Avg Score: -131.18 |\n",
"| Experiment: 4 | Episode: 368 | Score: -224.85 | Avg Score: -132.74 |\n",
"| Experiment: 4 | Episode: 369 | Score: -183.09 | Avg Score: -133.48 |\n",
"| Experiment: 4 | Episode: 370 | Score: -106.73 | Avg Score: -131.94 |\n",
"| Experiment: 4 | Episode: 371 | Score: -62.86 | Avg Score: -130.92 |\n",
"| Experiment: 4 | Episode: 372 | Score: -210.06 | Avg Score: -130.66 |\n",
"| Experiment: 4 | Episode: 373 | Score: -166.07 | Avg Score: -130.91 |\n",
"| Experiment: 4 | Episode: 374 | Score: -179.4 | Avg Score: -130.88 |\n",
"| Experiment: 4 | Episode: 375 | Score: -284.61 | Avg Score: -131.37 |\n",
"| Experiment: 4 | Episode: 376 | Score: -66.38 | Avg Score: -129.13 |\n",
"| Experiment: 4 | Episode: 377 | Score: -338.18 | Avg Score: -131.36 |\n",
"| Experiment: 4 | Episode: 378 | Score: -277.57 | Avg Score: -133.08 |\n",
"| Experiment: 4 | Episode: 379 | Score: -193.37 | Avg Score: -134.62 |\n",
"| Experiment: 4 | Episode: 380 | Score: -140.15 | Avg Score: -135.38 |\n",
"| Experiment: 4 | Episode: 381 | Score: 10.53 | Avg Score: -133.43 |\n",
"| Experiment: 4 | Episode: 382 | Score: -129.98 | Avg Score: -133.63 |\n",
"| Experiment: 4 | Episode: 383 | Score: -61.43 | Avg Score: -133.57 |\n",
"| Experiment: 4 | Episode: 384 | Score: -149.95 | Avg Score: -134.22 |\n",
"| Experiment: 4 | Episode: 385 | Score: -189.45 | Avg Score: -135.26 |\n",
"| Experiment: 4 | Episode: 386 | Score: -109.58 | Avg Score: -135.58 |\n",
"| Experiment: 4 | Episode: 387 | Score: -80.14 | Avg Score: -135.53 |\n",
"| Experiment: 4 | Episode: 388 | Score: -344.9 | Avg Score: -137.04 |\n",
"| Experiment: 4 | Episode: 389 | Score: -82.42 | Avg Score: -136.89 |\n",
"| Experiment: 4 | Episode: 390 | Score: -93.74 | Avg Score: -137.27 |\n",
"| Experiment: 4 | Episode: 391 | Score: -175.71 | Avg Score: -137.18 |\n",
"| Experiment: 4 | Episode: 392 | Score: -237.64 | Avg Score: -137.3 |\n",
"| Experiment: 4 | Episode: 393 | Score: -152.18 | Avg Score: -136.71 |\n",
"| Experiment: 4 | Episode: 394 | Score: -79.05 | Avg Score: -136.0 |\n",
"| Experiment: 4 | Episode: 395 | Score: -73.34 | Avg Score: -134.8 |\n",
"| Experiment: 4 | Episode: 396 | Score: -201.45 | Avg Score: -136.09 |\n",
"| Experiment: 4 | Episode: 397 | Score: -219.6 | Avg Score: -135.53 |\n",
"| Experiment: 4 | Episode: 398 | Score: -91.09 | Avg Score: -132.43 |\n",
"| Experiment: 4 | Episode: 399 | Score: -253.6 | Avg Score: -133.78 |\n",
"| Experiment: 4 | Episode: 400 | Score: -179.28 | Avg Score: -135.6 |\n",
"| Experiment: 4 | Episode: 401 | Score: -26.81 | Avg Score: -134.29 |\n",
"| Experiment: 4 | Episode: 402 | Score: -106.49 | Avg Score: -134.22 |\n",
"| Experiment: 4 | Episode: 403 | Score: -253.53 | Avg Score: -135.72 |\n",
"| Experiment: 4 | Episode: 404 | Score: -63.73 | Avg Score: -133.58 |\n",
"| Experiment: 4 | Episode: 405 | Score: -122.96 | Avg Score: -132.3 |\n",
"| Experiment: 4 | Episode: 406 | Score: -143.4 | Avg Score: -131.4 |\n",
"| Experiment: 4 | Episode: 407 | Score: -111.54 | Avg Score: -131.28 |\n",
"| Experiment: 4 | Episode: 408 | Score: -93.02 | Avg Score: -131.16 |\n",
"| Experiment: 4 | Episode: 409 | Score: -121.82 | Avg Score: -130.41 |\n",
"| Experiment: 4 | Episode: 410 | Score: -114.49 | Avg Score: -129.52 |\n",
"| Experiment: 4 | Episode: 411 | Score: -74.94 | Avg Score: -129.22 |\n",
"| Experiment: 4 | Episode: 412 | Score: -102.23 | Avg Score: -129.03 |\n",
"| Experiment: 4 | Episode: 413 | Score: -147.56 | Avg Score: -128.16 |\n",
"| Experiment: 4 | Episode: 414 | Score: -281.75 | Avg Score: -129.47 |\n",
"| Experiment: 4 | Episode: 415 | Score: -73.9 | Avg Score: -129.15 |\n",
"| Experiment: 4 | Episode: 416 | Score: -56.21 | Avg Score: -128.74 |\n",
"| Experiment: 4 | Episode: 417 | Score: -120.77 | Avg Score: -129.1 |\n",
"| Experiment: 4 | Episode: 418 | Score: -116.06 | Avg Score: -129.14 |\n",
"| Experiment: 4 | Episode: 419 | Score: -80.91 | Avg Score: -129.31 |\n",
"| Experiment: 4 | Episode: 420 | Score: -50.59 | Avg Score: -127.18 |\n",
"| Experiment: 4 | Episode: 421 | Score: -113.08 | Avg Score: -127.27 |\n",
"| Experiment: 4 | Episode: 422 | Score: -34.21 | Avg Score: -126.84 |\n",
"| Experiment: 4 | Episode: 423 | Score: -73.24 | Avg Score: -126.73 |\n",
"| Experiment: 4 | Episode: 424 | Score: -89.43 | Avg Score: -126.25 |\n",
"| Experiment: 4 | Episode: 425 | Score: -212.28 | Avg Score: -127.61 |\n",
"| Experiment: 4 | Episode: 426 | Score: -110.44 | Avg Score: -127.56 |\n",
"| Experiment: 4 | Episode: 427 | Score: -202.01 | Avg Score: -128.67 |\n",
"| Experiment: 4 | Episode: 428 | Score: -176.65 | Avg Score: -129.59 |\n",
"| Experiment: 4 | Episode: 429 | Score: -55.73 | Avg Score: -129.45 |\n",
"| Experiment: 4 | Episode: 430 | Score: -97.55 | Avg Score: -129.71 |\n",
"| Experiment: 4 | Episode: 431 | Score: -74.72 | Avg Score: -128.81 |\n",
"| Experiment: 4 | Episode: 432 | Score: -45.62 | Avg Score: -128.65 |\n",
"| Experiment: 4 | Episode: 433 | Score: -180.58 | Avg Score: -129.71 |\n",
"| Experiment: 4 | Episode: 434 | Score: -147.17 | Avg Score: -129.89 |\n",
"| Experiment: 4 | Episode: 435 | Score: -180.68 | Avg Score: -130.28 |\n",
"| Experiment: 4 | Episode: 436 | Score: -222.29 | Avg Score: -131.43 |\n",
"| Experiment: 4 | Episode: 437 | Score: -264.26 | Avg Score: -133.27 |\n",
"| Experiment: 4 | Episode: 438 | Score: -76.87 | Avg Score: -133.31 |\n",
"| Experiment: 4 | Episode: 439 | Score: -456.13 | Avg Score: -137.11 |\n",
"| Experiment: 4 | Episode: 440 | Score: -196.94 | Avg Score: -137.37 |\n",
"| Experiment: 4 | Episode: 441 | Score: -139.63 | Avg Score: -138.01 |\n",
"| Experiment: 4 | Episode: 442 | Score: -14.7 | Avg Score: -137.36 |\n",
"| Experiment: 4 | Episode: 443 | Score: -103.42 | Avg Score: -138.54 |\n",
"| Experiment: 4 | Episode: 444 | Score: -95.35 | Avg Score: -138.56 |\n",
"| Experiment: 4 | Episode: 445 | Score: -105.52 | Avg Score: -138.43 |\n",
"| Experiment: 4 | Episode: 446 | Score: -196.95 | Avg Score: -139.07 |\n",
"| Experiment: 4 | Episode: 447 | Score: -77.23 | Avg Score: -138.46 |\n",
"| Experiment: 4 | Episode: 448 | Score: -193.69 | Avg Score: -139.72 |\n",
"| Experiment: 4 | Episode: 449 | Score: -86.23 | Avg Score: -140.1 |\n",
"| Experiment: 4 | Episode: 450 | Score: -104.02 | Avg Score: -139.18 |\n",
"| Experiment: 4 | Episode: 451 | Score: -175.89 | Avg Score: -139.0 |\n",
"| Experiment: 4 | Episode: 452 | Score: -174.31 | Avg Score: -140.23 |\n",
"| Experiment: 4 | Episode: 453 | Score: -113.26 | Avg Score: -140.25 |\n",
"| Experiment: 4 | Episode: 454 | Score: -227.1 | Avg Score: -142.64 |\n",
"| Experiment: 4 | Episode: 455 | Score: -119.08 | Avg Score: -143.24 |\n",
"| Experiment: 4 | Episode: 456 | Score: -1.97 | Avg Score: -142.25 |\n",
"| Experiment: 4 | Episode: 457 | Score: -74.58 | Avg Score: -142.4 |\n",
"| Experiment: 4 | Episode: 458 | Score: -124.78 | Avg Score: -142.77 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 4 | Episode: 459 | Score: -175.63 | Avg Score: -143.33 |\n",
"| Experiment: 4 | Episode: 460 | Score: -27.74 | Avg Score: -141.96 |\n",
"| Experiment: 4 | Episode: 461 | Score: -92.04 | Avg Score: -140.94 |\n",
"| Experiment: 4 | Episode: 462 | Score: -121.04 | Avg Score: -141.44 |\n",
"| Experiment: 4 | Episode: 463 | Score: -174.38 | Avg Score: -140.81 |\n",
"| Experiment: 4 | Episode: 464 | Score: -215.26 | Avg Score: -141.4 |\n",
"| Experiment: 4 | Episode: 465 | Score: -70.52 | Avg Score: -141.22 |\n",
"| Experiment: 4 | Episode: 466 | Score: -102.41 | Avg Score: -138.83 |\n",
"| Experiment: 4 | Episode: 467 | Score: -154.21 | Avg Score: -138.61 |\n",
"| Experiment: 4 | Episode: 468 | Score: -116.37 | Avg Score: -137.52 |\n",
"| Experiment: 4 | Episode: 469 | Score: -111.53 | Avg Score: -136.81 |\n",
"| Experiment: 4 | Episode: 470 | Score: -99.91 | Avg Score: -136.74 |\n",
"| Experiment: 4 | Episode: 471 | Score: -87.84 | Avg Score: -136.99 |\n",
"| Experiment: 4 | Episode: 472 | Score: -106.13 | Avg Score: -135.95 |\n",
"| Experiment: 4 | Episode: 473 | Score: -157.47 | Avg Score: -135.86 |\n",
"| Experiment: 4 | Episode: 474 | Score: -180.97 | Avg Score: -135.88 |\n",
"| Experiment: 4 | Episode: 475 | Score: -103.79 | Avg Score: -134.07 |\n",
"| Experiment: 4 | Episode: 476 | Score: -160.48 | Avg Score: -135.01 |\n",
"| Experiment: 4 | Episode: 477 | Score: -89.12 | Avg Score: -132.52 |\n",
"| Experiment: 4 | Episode: 478 | Score: -81.26 | Avg Score: -130.56 |\n",
"| Experiment: 4 | Episode: 479 | Score: -153.94 | Avg Score: -130.16 |\n",
"| Experiment: 4 | Episode: 480 | Score: -148.9 | Avg Score: -130.25 |\n",
"| Experiment: 4 | Episode: 481 | Score: -125.85 | Avg Score: -131.62 |\n",
"| Experiment: 4 | Episode: 482 | Score: -108.96 | Avg Score: -131.41 |\n",
"| Experiment: 4 | Episode: 483 | Score: -191.69 | Avg Score: -132.71 |\n",
"| Experiment: 4 | Episode: 484 | Score: -96.1 | Avg Score: -132.17 |\n",
"| Experiment: 4 | Episode: 485 | Score: -149.63 | Avg Score: -131.77 |\n",
"| Experiment: 4 | Episode: 486 | Score: -139.97 | Avg Score: -132.08 |\n",
"| Experiment: 4 | Episode: 487 | Score: -153.86 | Avg Score: -132.81 |\n",
"| Experiment: 4 | Episode: 488 | Score: -142.48 | Avg Score: -130.79 |\n",
"| Experiment: 4 | Episode: 489 | Score: -111.05 | Avg Score: -131.07 |\n",
"| Experiment: 4 | Episode: 490 | Score: -135.89 | Avg Score: -131.5 |\n",
"| Experiment: 4 | Episode: 491 | Score: -76.31 | Avg Score: -130.5 |\n",
"| Experiment: 4 | Episode: 492 | Score: -92.23 | Avg Score: -129.05 |\n",
"| Experiment: 4 | Episode: 493 | Score: -75.29 | Avg Score: -128.28 |\n",
"| Experiment: 4 | Episode: 494 | Score: -100.1 | Avg Score: -128.49 |\n",
"| Experiment: 4 | Episode: 495 | Score: -267.84 | Avg Score: -130.43 |\n",
"| Experiment: 4 | Episode: 496 | Score: -273.61 | Avg Score: -131.16 |\n",
"| Experiment: 4 | Episode: 497 | Score: -73.89 | Avg Score: -129.7 |\n",
"| Experiment: 4 | Episode: 498 | Score: -109.73 | Avg Score: -129.89 |\n",
"| Experiment: 4 | Episode: 499 | Score: -73.23 | Avg Score: -128.08 |\n",
"| Experiment: 4 | Episode: 500 | Score: -96.64 | Avg Score: -127.26 |\n",
"| Experiment: 4 | Episode: 501 | Score: -138.9 | Avg Score: -128.38 |\n",
"| Experiment: 4 | Episode: 502 | Score: -326.45 | Avg Score: -130.58 |\n",
"| Experiment: 4 | Episode: 503 | Score: -95.17 | Avg Score: -128.99 |\n",
"| Experiment: 4 | Episode: 504 | Score: -189.84 | Avg Score: -130.25 |\n",
"| Experiment: 4 | Episode: 505 | Score: -101.42 | Avg Score: -130.04 |\n",
"| Experiment: 4 | Episode: 506 | Score: -178.63 | Avg Score: -130.39 |\n",
"| Experiment: 4 | Episode: 507 | Score: -64.68 | Avg Score: -129.92 |\n",
"| Experiment: 4 | Episode: 508 | Score: -112.53 | Avg Score: -130.12 |\n",
"| Experiment: 4 | Episode: 509 | Score: -203.75 | Avg Score: -130.94 |\n",
"| Experiment: 4 | Episode: 510 | Score: -76.59 | Avg Score: -130.56 |\n",
"| Experiment: 4 | Episode: 511 | Score: -167.47 | Avg Score: -131.48 |\n",
"| Experiment: 4 | Episode: 512 | Score: -89.29 | Avg Score: -131.35 |\n",
"| Experiment: 4 | Episode: 513 | Score: -103.87 | Avg Score: -130.92 |\n",
"| Experiment: 4 | Episode: 514 | Score: -85.8 | Avg Score: -128.96 |\n",
"| Experiment: 4 | Episode: 515 | Score: 16.63 | Avg Score: -128.05 |\n",
"| Experiment: 4 | Episode: 516 | Score: -83.46 | Avg Score: -128.32 |\n",
"| Experiment: 4 | Episode: 517 | Score: -144.16 | Avg Score: -128.56 |\n",
"| Experiment: 4 | Episode: 518 | Score: -97.97 | Avg Score: -128.38 |\n",
"| Experiment: 4 | Episode: 519 | Score: -122.22 | Avg Score: -128.79 |\n",
"| Experiment: 4 | Episode: 520 | Score: -106.06 | Avg Score: -129.34 |\n",
"| Experiment: 4 | Episode: 521 | Score: -82.22 | Avg Score: -129.04 |\n",
"| Experiment: 4 | Episode: 522 | Score: -94.36 | Avg Score: -129.64 |\n",
"| Experiment: 4 | Episode: 523 | Score: -101.5 | Avg Score: -129.92 |\n",
"| Experiment: 4 | Episode: 524 | Score: -251.02 | Avg Score: -131.54 |\n",
"| Experiment: 4 | Episode: 525 | Score: -385.51 | Avg Score: -133.27 |\n",
"| Experiment: 4 | Episode: 526 | Score: -145.41 | Avg Score: -133.62 |\n",
"| Experiment: 4 | Episode: 527 | Score: -62.48 | Avg Score: -132.22 |\n",
"| Experiment: 4 | Episode: 528 | Score: -96.16 | Avg Score: -131.42 |\n",
"| Experiment: 4 | Episode: 529 | Score: -67.91 | Avg Score: -131.54 |\n",
"| Experiment: 4 | Episode: 530 | Score: -71.75 | Avg Score: -131.28 |\n",
"| Experiment: 4 | Episode: 531 | Score: -152.03 | Avg Score: -132.06 |\n",
"| Experiment: 4 | Episode: 532 | Score: -83.41 | Avg Score: -132.43 |\n",
"| Experiment: 4 | Episode: 533 | Score: -113.68 | Avg Score: -131.76 |\n",
"| Experiment: 4 | Episode: 534 | Score: -169.96 | Avg Score: -131.99 |\n",
"| Experiment: 4 | Episode: 535 | Score: -218.35 | Avg Score: -132.37 |\n",
"| Experiment: 4 | Episode: 536 | Score: 64.61 | Avg Score: -129.5 |\n",
"| Experiment: 4 | Episode: 537 | Score: -93.87 | Avg Score: -127.8 |\n",
"| Experiment: 4 | Episode: 538 | Score: -72.45 | Avg Score: -127.75 |\n",
"| Experiment: 4 | Episode: 539 | Score: -121.63 | Avg Score: -124.41 |\n",
"| Experiment: 4 | Episode: 540 | Score: -86.61 | Avg Score: -123.3 |\n",
"| Experiment: 4 | Episode: 541 | Score: -123.43 | Avg Score: -123.14 |\n",
"| Experiment: 4 | Episode: 542 | Score: -75.05 | Avg Score: -123.74 |\n",
"| Experiment: 4 | Episode: 543 | Score: -86.92 | Avg Score: -123.58 |\n",
"| Experiment: 4 | Episode: 544 | Score: -147.32 | Avg Score: -124.1 |\n",
"| Experiment: 4 | Episode: 545 | Score: -112.61 | Avg Score: -124.17 |\n",
"| Experiment: 4 | Episode: 546 | Score: -100.09 | Avg Score: -123.2 |\n",
"| Experiment: 4 | Episode: 547 | Score: -81.29 | Avg Score: -123.24 |\n",
"| Experiment: 4 | Episode: 548 | Score: -97.51 | Avg Score: -122.28 |\n",
"| Experiment: 4 | Episode: 549 | Score: -142.96 | Avg Score: -122.85 |\n",
"| Experiment: 4 | Episode: 550 | Score: -95.47 | Avg Score: -122.76 |\n",
"| Experiment: 4 | Episode: 551 | Score: -89.3 | Avg Score: -121.9 |\n",
"| Experiment: 4 | Episode: 552 | Score: -37.3 | Avg Score: -120.53 |\n",
"| Experiment: 4 | Episode: 553 | Score: -96.9 | Avg Score: -120.36 |\n",
"| Experiment: 4 | Episode: 554 | Score: -85.82 | Avg Score: -118.95 |\n",
"| Experiment: 4 | Episode: 555 | Score: -85.65 | Avg Score: -118.62 |\n",
"| Experiment: 4 | Episode: 556 | Score: -101.78 | Avg Score: -119.61 |\n",
"| Experiment: 4 | Episode: 557 | Score: -156.86 | Avg Score: -120.44 |\n",
"| Experiment: 4 | Episode: 558 | Score: -123.49 | Avg Score: -120.42 |\n",
"| Experiment: 4 | Episode: 559 | Score: -185.86 | Avg Score: -120.53 |\n",
"| Experiment: 4 | Episode: 560 | Score: -222.74 | Avg Score: -122.48 |\n",
"| Experiment: 4 | Episode: 561 | Score: -63.22 | Avg Score: -122.19 |\n",
"| Experiment: 4 | Episode: 562 | Score: -138.05 | Avg Score: -122.36 |\n",
"| Experiment: 4 | Episode: 563 | Score: -130.98 | Avg Score: -121.92 |\n",
"| Experiment: 4 | Episode: 564 | Score: -133.51 | Avg Score: -121.11 |\n",
"| Experiment: 4 | Episode: 565 | Score: -99.37 | Avg Score: -121.39 |\n",
"| Experiment: 4 | Episode: 566 | Score: -90.33 | Avg Score: -121.27 |\n",
"| Experiment: 4 | Episode: 567 | Score: -223.18 | Avg Score: -121.96 |\n",
"| Experiment: 4 | Episode: 568 | Score: -113.77 | Avg Score: -121.94 |\n",
"| Experiment: 4 | Episode: 569 | Score: -87.16 | Avg Score: -121.69 |\n",
"| Experiment: 4 | Episode: 570 | Score: -74.2 | Avg Score: -121.44 |\n",
"| Experiment: 4 | Episode: 571 | Score: -286.04 | Avg Score: -123.42 |\n",
"| Experiment: 4 | Episode: 572 | Score: -136.55 | Avg Score: -123.72 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 4 | Episode: 573 | Score: -80.12 | Avg Score: -122.95 |\n",
"| Experiment: 4 | Episode: 574 | Score: -135.14 | Avg Score: -122.49 |\n",
"| Experiment: 4 | Episode: 575 | Score: -78.27 | Avg Score: -122.24 |\n",
"| Experiment: 4 | Episode: 576 | Score: -142.24 | Avg Score: -122.05 |\n",
"| Experiment: 4 | Episode: 577 | Score: -47.85 | Avg Score: -121.64 |\n",
"| Experiment: 4 | Episode: 578 | Score: -127.51 | Avg Score: -122.1 |\n",
"| Experiment: 4 | Episode: 579 | Score: -93.68 | Avg Score: -121.5 |\n",
"| Experiment: 4 | Episode: 580 | Score: -74.11 | Avg Score: -120.75 |\n",
"| Experiment: 4 | Episode: 581 | Score: -108.68 | Avg Score: -120.58 |\n",
"| Experiment: 4 | Episode: 582 | Score: -99.61 | Avg Score: -120.49 |\n",
"| Experiment: 4 | Episode: 583 | Score: -82.43 | Avg Score: -119.39 |\n",
"| Experiment: 4 | Episode: 584 | Score: -286.49 | Avg Score: -121.3 |\n",
"| Experiment: 4 | Episode: 585 | Score: -68.57 | Avg Score: -120.49 |\n",
"| Experiment: 4 | Episode: 586 | Score: -64.49 | Avg Score: -119.73 |\n",
"| Experiment: 4 | Episode: 587 | Score: -65.65 | Avg Score: -118.85 |\n",
"| Experiment: 4 | Episode: 588 | Score: -87.4 | Avg Score: -118.3 |\n",
"| Experiment: 4 | Episode: 589 | Score: -175.24 | Avg Score: -118.94 |\n",
"| Experiment: 4 | Episode: 590 | Score: -73.3 | Avg Score: -118.32 |\n",
"| Experiment: 4 | Episode: 591 | Score: -182.04 | Avg Score: -119.37 |\n",
"| Experiment: 4 | Episode: 592 | Score: -333.29 | Avg Score: -121.78 |\n",
"| Experiment: 4 | Episode: 593 | Score: -149.86 | Avg Score: -122.53 |\n",
"| Experiment: 4 | Episode: 594 | Score: -446.88 | Avg Score: -126.0 |\n",
"| Experiment: 4 | Episode: 595 | Score: -310.32 | Avg Score: -126.42 |\n",
"| Experiment: 4 | Episode: 596 | Score: -227.28 | Avg Score: -125.96 |\n",
"| Experiment: 4 | Episode: 597 | Score: -309.26 | Avg Score: -128.31 |\n",
"| Experiment: 4 | Episode: 598 | Score: -87.59 | Avg Score: -128.09 |\n",
"| Experiment: 4 | Episode: 599 | Score: -81.51 | Avg Score: -128.17 |\n",
"| Experiment: 4 | Episode: 600 | Score: -132.92 | Avg Score: -128.54 |\n",
"| Experiment: 4 | Episode: 601 | Score: -60.19 | Avg Score: -127.75 |\n",
"| Experiment: 4 | Episode: 602 | Score: -74.36 | Avg Score: -125.23 |\n",
"| Experiment: 4 | Episode: 603 | Score: -155.45 | Avg Score: -125.83 |\n",
"| Experiment: 4 | Episode: 604 | Score: -308.58 | Avg Score: -127.02 |\n",
"| Experiment: 4 | Episode: 605 | Score: -210.19 | Avg Score: -128.11 |\n",
"| Experiment: 4 | Episode: 606 | Score: -100.52 | Avg Score: -127.33 |\n",
"| Experiment: 4 | Episode: 607 | Score: -89.31 | Avg Score: -127.57 |\n",
"| Experiment: 4 | Episode: 608 | Score: -108.86 | Avg Score: -127.54 |\n",
"| Experiment: 4 | Episode: 609 | Score: -120.15 | Avg Score: -126.7 |\n",
"| Experiment: 4 | Episode: 610 | Score: -25.12 | Avg Score: -126.18 |\n",
"| Experiment: 4 | Episode: 611 | Score: -101.07 | Avg Score: -125.52 |\n",
"| Experiment: 4 | Episode: 612 | Score: -93.66 | Avg Score: -125.56 |\n",
"| Experiment: 4 | Episode: 613 | Score: -264.07 | Avg Score: -127.17 |\n",
"| Experiment: 4 | Episode: 614 | Score: -208.25 | Avg Score: -128.39 |\n",
"| Experiment: 4 | Episode: 615 | Score: -53.93 | Avg Score: -129.1 |\n",
"| Experiment: 4 | Episode: 616 | Score: -345.4 | Avg Score: -131.72 |\n",
"| Experiment: 4 | Episode: 617 | Score: -73.47 | Avg Score: -131.01 |\n",
"| Experiment: 4 | Episode: 618 | Score: -62.31 | Avg Score: -130.65 |\n",
"| Experiment: 4 | Episode: 619 | Score: -149.72 | Avg Score: -130.93 |\n",
"| Experiment: 4 | Episode: 620 | Score: -261.45 | Avg Score: -132.48 |\n",
"| Experiment: 4 | Episode: 621 | Score: -161.99 | Avg Score: -133.28 |\n",
"| Experiment: 4 | Episode: 622 | Score: -122.27 | Avg Score: -133.56 |\n",
"| Experiment: 4 | Episode: 623 | Score: -99.27 | Avg Score: -133.54 |\n",
"| Experiment: 4 | Episode: 624 | Score: -97.02 | Avg Score: -132.0 |\n",
"| Experiment: 4 | Episode: 625 | Score: -146.2 | Avg Score: -129.6 |\n",
"| Experiment: 4 | Episode: 626 | Score: -324.01 | Avg Score: -131.39 |\n",
"| Experiment: 4 | Episode: 627 | Score: -73.21 | Avg Score: -131.5 |\n",
"| Experiment: 4 | Episode: 628 | Score: -56.78 | Avg Score: -131.1 |\n",
"| Experiment: 4 | Episode: 629 | Score: -61.92 | Avg Score: -131.04 |\n",
"| Experiment: 4 | Episode: 630 | Score: -97.13 | Avg Score: -131.3 |\n",
"| Experiment: 4 | Episode: 631 | Score: -94.43 | Avg Score: -130.72 |\n",
"| Experiment: 4 | Episode: 632 | Score: -100.51 | Avg Score: -130.89 |\n",
"| Experiment: 4 | Episode: 633 | Score: -383.9 | Avg Score: -133.59 |\n",
"| Experiment: 4 | Episode: 634 | Score: -98.69 | Avg Score: -132.88 |\n",
"| Experiment: 4 | Episode: 635 | Score: -197.93 | Avg Score: -132.68 |\n",
"| Experiment: 4 | Episode: 636 | Score: -75.77 | Avg Score: -134.08 |\n",
"| Experiment: 4 | Episode: 637 | Score: 24.48 | Avg Score: -132.9 |\n",
"| Experiment: 4 | Episode: 638 | Score: -128.33 | Avg Score: -133.46 |\n",
"| Experiment: 4 | Episode: 639 | Score: -57.72 | Avg Score: -132.82 |\n",
"| Experiment: 4 | Episode: 640 | Score: -115.52 | Avg Score: -133.11 |\n",
"| Experiment: 4 | Episode: 641 | Score: -238.33 | Avg Score: -134.25 |\n",
"| Experiment: 4 | Episode: 642 | Score: -93.67 | Avg Score: -134.44 |\n",
"| Experiment: 4 | Episode: 643 | Score: -68.68 | Avg Score: -134.26 |\n",
"| Experiment: 4 | Episode: 644 | Score: -117.14 | Avg Score: -133.96 |\n",
"| Experiment: 4 | Episode: 645 | Score: -161.51 | Avg Score: -134.45 |\n",
"| Experiment: 4 | Episode: 646 | Score: -253.04 | Avg Score: -135.98 |\n",
"| Experiment: 4 | Episode: 647 | Score: -83.41 | Avg Score: -136.0 |\n",
"| Experiment: 4 | Episode: 648 | Score: -73.79 | Avg Score: -135.76 |\n",
"| Experiment: 4 | Episode: 649 | Score: -98.73 | Avg Score: -135.32 |\n",
"| Experiment: 4 | Episode: 650 | Score: -468.34 | Avg Score: -139.05 |\n",
"| Experiment: 4 | Episode: 651 | Score: -69.57 | Avg Score: -138.85 |\n",
"| Experiment: 4 | Episode: 652 | Score: -107.94 | Avg Score: -139.56 |\n",
"| Experiment: 4 | Episode: 653 | Score: -89.65 | Avg Score: -139.48 |\n",
"| Experiment: 4 | Episode: 654 | Score: -83.15 | Avg Score: -139.46 |\n",
"| Experiment: 4 | Episode: 655 | Score: 9.73 | Avg Score: -138.5 |\n",
"| Experiment: 4 | Episode: 656 | Score: -163.86 | Avg Score: -139.12 |\n",
"| Experiment: 4 | Episode: 657 | Score: -103.81 | Avg Score: -138.59 |\n",
"| Experiment: 4 | Episode: 658 | Score: -127.59 | Avg Score: -138.63 |\n",
"| Experiment: 4 | Episode: 659 | Score: -101.54 | Avg Score: -137.79 |\n",
"| Experiment: 4 | Episode: 660 | Score: -55.62 | Avg Score: -136.12 |\n",
"| Experiment: 4 | Episode: 661 | Score: -237.43 | Avg Score: -137.86 |\n",
"| Experiment: 4 | Episode: 662 | Score: -121.66 | Avg Score: -137.7 |\n",
"| Experiment: 4 | Episode: 663 | Score: -159.71 | Avg Score: -137.98 |\n",
"| Experiment: 4 | Episode: 664 | Score: -121.12 | Avg Score: -137.86 |\n",
"| Experiment: 4 | Episode: 665 | Score: -145.98 | Avg Score: -138.33 |\n",
"| Experiment: 4 | Episode: 666 | Score: -81.54 | Avg Score: -138.24 |\n",
"| Experiment: 4 | Episode: 667 | Score: -153.31 | Avg Score: -137.54 |\n",
"| Experiment: 4 | Episode: 668 | Score: -61.64 | Avg Score: -137.02 |\n",
"| Experiment: 4 | Episode: 669 | Score: -112.99 | Avg Score: -137.28 |\n",
"| Experiment: 4 | Episode: 670 | Score: -211.07 | Avg Score: -138.65 |\n",
"| Experiment: 4 | Episode: 671 | Score: -29.35 | Avg Score: -136.08 |\n",
"| Experiment: 4 | Episode: 672 | Score: -172.93 | Avg Score: -136.44 |\n",
"| Experiment: 4 | Episode: 673 | Score: -212.86 | Avg Score: -137.77 |\n",
"| Experiment: 4 | Episode: 674 | Score: -83.41 | Avg Score: -137.25 |\n",
"| Experiment: 4 | Episode: 675 | Score: -83.04 | Avg Score: -137.3 |\n",
"| Experiment: 4 | Episode: 676 | Score: -91.08 | Avg Score: -136.79 |\n",
"| Experiment: 4 | Episode: 677 | Score: -65.27 | Avg Score: -136.96 |\n",
"| Experiment: 4 | Episode: 678 | Score: -91.18 | Avg Score: -136.6 |\n",
"| Experiment: 4 | Episode: 679 | Score: -106.76 | Avg Score: -136.73 |\n",
"| Experiment: 4 | Episode: 680 | Score: -124.72 | Avg Score: -137.24 |\n",
"| Experiment: 4 | Episode: 681 | Score: -93.92 | Avg Score: -137.09 |\n",
"| Experiment: 4 | Episode: 682 | Score: -116.07 | Avg Score: -137.25 |\n",
"| Experiment: 4 | Episode: 683 | Score: -106.22 | Avg Score: -137.49 |\n",
"| Experiment: 4 | Episode: 684 | Score: -39.38 | Avg Score: -135.02 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 4 | Episode: 685 | Score: -122.98 | Avg Score: -135.56 |\n",
"| Experiment: 4 | Episode: 686 | Score: -239.63 | Avg Score: -137.32 |\n",
"| Experiment: 4 | Episode: 687 | Score: -77.82 | Avg Score: -137.44 |\n",
"| Experiment: 4 | Episode: 688 | Score: -155.97 | Avg Score: -138.12 |\n",
"| Experiment: 4 | Episode: 689 | Score: -48.3 | Avg Score: -136.85 |\n",
"| Experiment: 4 | Episode: 690 | Score: -130.36 | Avg Score: -137.42 |\n",
"| Experiment: 4 | Episode: 691 | Score: -318.7 | Avg Score: -138.79 |\n",
"| Experiment: 4 | Episode: 692 | Score: -173.83 | Avg Score: -137.2 |\n",
"| Experiment: 4 | Episode: 693 | Score: -45.42 | Avg Score: -136.15 |\n",
"| Experiment: 4 | Episode: 694 | Score: -54.85 | Avg Score: -132.23 |\n",
"| Experiment: 4 | Episode: 695 | Score: -51.09 | Avg Score: -129.64 |\n",
"| Experiment: 4 | Episode: 696 | Score: -81.57 | Avg Score: -128.18 |\n",
"| Experiment: 4 | Episode: 697 | Score: -90.28 | Avg Score: -125.99 |\n",
"| Experiment: 4 | Episode: 698 | Score: -105.23 | Avg Score: -126.17 |\n",
"| Experiment: 4 | Episode: 699 | Score: -108.75 | Avg Score: -126.44 |\n",
"| Experiment: 4 | Episode: 700 | Score: -198.44 | Avg Score: -127.1 |\n",
"| Experiment: 4 | Episode: 701 | Score: -106.03 | Avg Score: -127.56 |\n",
"| Experiment: 4 | Episode: 702 | Score: -69.87 | Avg Score: -127.51 |\n",
"| Experiment: 4 | Episode: 703 | Score: -138.01 | Avg Score: -127.34 |\n",
"| Experiment: 4 | Episode: 704 | Score: -128.32 | Avg Score: -125.53 |\n",
"| Experiment: 4 | Episode: 705 | Score: -94.36 | Avg Score: -124.38 |\n",
"| Experiment: 4 | Episode: 706 | Score: -145.97 | Avg Score: -124.83 |\n",
"| Experiment: 4 | Episode: 707 | Score: -60.6 | Avg Score: -124.54 |\n",
"| Experiment: 4 | Episode: 708 | Score: -119.09 | Avg Score: -124.65 |\n",
"| Experiment: 4 | Episode: 709 | Score: -74.58 | Avg Score: -124.19 |\n",
"| Experiment: 4 | Episode: 710 | Score: -57.42 | Avg Score: -124.51 |\n",
"| Experiment: 4 | Episode: 711 | Score: -99.88 | Avg Score: -124.5 |\n",
"| Experiment: 4 | Episode: 712 | Score: -138.94 | Avg Score: -124.95 |\n",
"| Experiment: 4 | Episode: 713 | Score: -51.56 | Avg Score: -122.83 |\n",
"| Experiment: 4 | Episode: 714 | Score: -357.3 | Avg Score: -124.32 |\n",
"| Experiment: 4 | Episode: 715 | Score: -148.06 | Avg Score: -125.26 |\n",
"| Experiment: 4 | Episode: 716 | Score: -82.86 | Avg Score: -122.63 |\n",
"| Experiment: 4 | Episode: 717 | Score: -124.93 | Avg Score: -123.15 |\n",
"| Experiment: 4 | Episode: 718 | Score: -181.37 | Avg Score: -124.34 |\n",
"| Experiment: 4 | Episode: 719 | Score: -133.29 | Avg Score: -124.18 |\n",
"| Experiment: 4 | Episode: 720 | Score: -194.47 | Avg Score: -123.51 |\n",
"| Experiment: 4 | Episode: 721 | Score: -145.76 | Avg Score: -123.34 |\n",
"| Experiment: 4 | Episode: 722 | Score: -72.41 | Avg Score: -122.84 |\n",
"| Experiment: 4 | Episode: 723 | Score: -105.22 | Avg Score: -122.9 |\n",
"| Experiment: 4 | Episode: 724 | Score: -157.88 | Avg Score: -123.51 |\n",
"| Experiment: 4 | Episode: 725 | Score: -135.62 | Avg Score: -123.41 |\n",
"| Experiment: 4 | Episode: 726 | Score: -136.56 | Avg Score: -121.53 |\n",
"| Experiment: 4 | Episode: 727 | Score: -209.15 | Avg Score: -122.89 |\n",
"| Experiment: 4 | Episode: 728 | Score: -56.15 | Avg Score: -122.89 |\n",
"| Experiment: 4 | Episode: 729 | Score: -135.5 | Avg Score: -123.62 |\n",
"| Experiment: 4 | Episode: 730 | Score: -86.42 | Avg Score: -123.51 |\n",
"| Experiment: 4 | Episode: 731 | Score: -93.73 | Avg Score: -123.51 |\n",
"| Experiment: 4 | Episode: 732 | Score: -101.42 | Avg Score: -123.52 |\n",
"| Experiment: 4 | Episode: 733 | Score: -78.3 | Avg Score: -120.46 |\n",
"| Experiment: 4 | Episode: 734 | Score: -124.05 | Avg Score: -120.71 |\n",
"| Experiment: 4 | Episode: 735 | Score: -242.22 | Avg Score: -121.16 |\n",
"| Experiment: 4 | Episode: 736 | Score: -75.85 | Avg Score: -121.16 |\n",
"| Experiment: 4 | Episode: 737 | Score: -235.1 | Avg Score: -123.75 |\n",
"| Experiment: 4 | Episode: 738 | Score: -113.06 | Avg Score: -123.6 |\n",
"| Experiment: 4 | Episode: 739 | Score: -85.07 | Avg Score: -123.87 |\n",
"| Experiment: 4 | Episode: 740 | Score: -94.62 | Avg Score: -123.67 |\n",
"| Experiment: 4 | Episode: 741 | Score: -114.14 | Avg Score: -122.42 |\n",
"| Experiment: 4 | Episode: 742 | Score: -89.77 | Avg Score: -122.38 |\n",
"| Experiment: 4 | Episode: 743 | Score: -100.57 | Avg Score: -122.7 |\n",
"| Experiment: 4 | Episode: 744 | Score: -115.75 | Avg Score: -122.69 |\n",
"| Experiment: 4 | Episode: 745 | Score: -119.91 | Avg Score: -122.27 |\n",
"| Experiment: 4 | Episode: 746 | Score: -50.57 | Avg Score: -120.25 |\n",
"| Experiment: 4 | Episode: 747 | Score: -56.14 | Avg Score: -119.98 |\n",
"| Experiment: 4 | Episode: 748 | Score: -136.09 | Avg Score: -120.6 |\n",
"| Experiment: 4 | Episode: 749 | Score: -71.16 | Avg Score: -120.32 |\n",
"| Experiment: 4 | Episode: 750 | Score: -104.73 | Avg Score: -116.69 |\n",
"| Experiment: 4 | Episode: 751 | Score: -91.55 | Avg Score: -116.91 |\n",
"| Experiment: 4 | Episode: 752 | Score: -70.13 | Avg Score: -116.53 |\n",
"| Experiment: 4 | Episode: 753 | Score: -61.55 | Avg Score: -116.25 |\n",
"| Experiment: 4 | Episode: 754 | Score: -132.12 | Avg Score: -116.74 |\n",
"| Experiment: 4 | Episode: 755 | Score: -78.76 | Avg Score: -117.62 |\n",
"| Experiment: 4 | Episode: 756 | Score: -137.14 | Avg Score: -117.36 |\n",
"| Experiment: 4 | Episode: 757 | Score: -82.64 | Avg Score: -117.14 |\n",
"| Experiment: 4 | Episode: 758 | Score: -69.18 | Avg Score: -116.56 |\n",
"| Experiment: 4 | Episode: 759 | Score: -92.92 | Avg Score: -116.47 |\n",
"| Experiment: 4 | Episode: 760 | Score: -177.83 | Avg Score: -117.69 |\n",
"| Experiment: 4 | Episode: 761 | Score: -80.64 | Avg Score: -116.13 |\n",
"| Experiment: 4 | Episode: 762 | Score: -135.38 | Avg Score: -116.26 |\n",
"| Experiment: 4 | Episode: 763 | Score: -115.39 | Avg Score: -115.82 |\n",
"| Experiment: 4 | Episode: 764 | Score: -195.12 | Avg Score: -116.56 |\n",
"| Experiment: 4 | Episode: 765 | Score: -41.41 | Avg Score: -115.52 |\n",
"| Experiment: 4 | Episode: 766 | Score: -214.58 | Avg Score: -116.85 |\n",
"| Experiment: 4 | Episode: 767 | Score: -71.68 | Avg Score: -116.03 |\n",
"| Experiment: 4 | Episode: 768 | Score: -116.36 | Avg Score: -116.58 |\n",
"| Experiment: 4 | Episode: 769 | Score: -76.58 | Avg Score: -116.21 |\n",
"| Experiment: 4 | Episode: 770 | Score: -89.95 | Avg Score: -115.0 |\n",
"| Experiment: 4 | Episode: 771 | Score: -94.58 | Avg Score: -115.65 |\n",
"| Experiment: 4 | Episode: 772 | Score: -99.95 | Avg Score: -114.92 |\n",
"| Experiment: 4 | Episode: 773 | Score: -115.32 | Avg Score: -113.95 |\n",
"| Experiment: 4 | Episode: 774 | Score: -194.66 | Avg Score: -115.06 |\n",
"| Experiment: 4 | Episode: 775 | Score: -80.67 | Avg Score: -115.04 |\n",
"| Experiment: 4 | Episode: 776 | Score: -67.99 | Avg Score: -114.81 |\n",
"| Experiment: 4 | Episode: 777 | Score: -109.95 | Avg Score: -115.25 |\n",
"| Experiment: 4 | Episode: 778 | Score: -77.63 | Avg Score: -115.12 |\n",
"| Experiment: 4 | Episode: 779 | Score: -68.49 | Avg Score: -114.73 |\n",
"| Experiment: 4 | Episode: 780 | Score: -56.48 | Avg Score: -114.05 |\n",
"| Experiment: 4 | Episode: 781 | Score: -79.9 | Avg Score: -113.91 |\n",
"| Experiment: 4 | Episode: 782 | Score: -176.23 | Avg Score: -114.51 |\n",
"| Experiment: 4 | Episode: 783 | Score: -135.92 | Avg Score: -114.81 |\n",
"| Experiment: 4 | Episode: 784 | Score: -133.46 | Avg Score: -115.75 |\n",
"| Experiment: 4 | Episode: 785 | Score: -127.15 | Avg Score: -115.79 |\n",
"| Experiment: 4 | Episode: 786 | Score: -302.73 | Avg Score: -116.42 |\n",
"| Experiment: 4 | Episode: 787 | Score: -172.83 | Avg Score: -117.37 |\n",
"| Experiment: 4 | Episode: 788 | Score: -122.97 | Avg Score: -117.04 |\n",
"| Experiment: 4 | Episode: 789 | Score: -92.92 | Avg Score: -117.49 |\n",
"| Experiment: 4 | Episode: 790 | Score: -128.49 | Avg Score: -117.47 |\n",
"| Experiment: 4 | Episode: 791 | Score: -132.05 | Avg Score: -115.61 |\n",
"| Experiment: 4 | Episode: 792 | Score: -86.23 | Avg Score: -114.73 |\n",
"| Experiment: 4 | Episode: 793 | Score: -111.17 | Avg Score: -115.39 |\n",
"| Experiment: 4 | Episode: 794 | Score: -80.42 | Avg Score: -115.64 |\n",
"| Experiment: 4 | Episode: 795 | Score: -15.5 | Avg Score: -115.29 |\n",
"| Experiment: 4 | Episode: 796 | Score: -61.6 | Avg Score: -115.09 |\n",
"| Experiment: 4 | Episode: 797 | Score: -168.17 | Avg Score: -115.87 |\n",
"| Experiment: 4 | Episode: 798 | Score: -121.79 | Avg Score: -116.03 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 4 | Episode: 799 | Score: -112.27 | Avg Score: -116.07 |\n",
"| Experiment: 4 | Episode: 800 | Score: -109.57 | Avg Score: -115.18 |\n",
"| Experiment: 4 | Episode: 801 | Score: -44.71 | Avg Score: -114.57 |\n",
"| Experiment: 4 | Episode: 802 | Score: -204.18 | Avg Score: -115.91 |\n",
"| Experiment: 4 | Episode: 803 | Score: -146.3 | Avg Score: -115.99 |\n",
"| Experiment: 4 | Episode: 804 | Score: -69.52 | Avg Score: -115.4 |\n",
"| Experiment: 4 | Episode: 805 | Score: -130.76 | Avg Score: -115.77 |\n",
"| Experiment: 4 | Episode: 806 | Score: -94.82 | Avg Score: -115.26 |\n",
"| Experiment: 4 | Episode: 807 | Score: -68.45 | Avg Score: -115.33 |\n",
"| Experiment: 4 | Episode: 808 | Score: -97.63 | Avg Score: -115.12 |\n",
"| Experiment: 4 | Episode: 809 | Score: -75.32 | Avg Score: -115.13 |\n",
"| Experiment: 4 | Episode: 810 | Score: -128.74 | Avg Score: -115.84 |\n",
"| Experiment: 4 | Episode: 811 | Score: -89.58 | Avg Score: -115.74 |\n",
"| Experiment: 4 | Episode: 812 | Score: -166.5 | Avg Score: -116.01 |\n",
"| Experiment: 4 | Episode: 813 | Score: -278.89 | Avg Score: -118.29 |\n",
"| Experiment: 4 | Episode: 814 | Score: -73.14 | Avg Score: -115.44 |\n",
"| Experiment: 4 | Episode: 815 | Score: -156.32 | Avg Score: -115.53 |\n",
"| Experiment: 4 | Episode: 816 | Score: -280.91 | Avg Score: -117.51 |\n",
"| Experiment: 4 | Episode: 817 | Score: -116.15 | Avg Score: -117.42 |\n",
"| Experiment: 4 | Episode: 818 | Score: -147.47 | Avg Score: -117.08 |\n",
"| Experiment: 4 | Episode: 819 | Score: -74.33 | Avg Score: -116.49 |\n",
"| Experiment: 4 | Episode: 820 | Score: -112.82 | Avg Score: -115.67 |\n",
"| Experiment: 4 | Episode: 821 | Score: -72.14 | Avg Score: -114.94 |\n",
"| Experiment: 4 | Episode: 822 | Score: -75.81 | Avg Score: -114.97 |\n",
"| Experiment: 4 | Episode: 823 | Score: -222.68 | Avg Score: -116.15 |\n",
"| Experiment: 4 | Episode: 824 | Score: -113.1 | Avg Score: -115.7 |\n",
"| Experiment: 4 | Episode: 825 | Score: -135.76 | Avg Score: -115.7 |\n",
"| Experiment: 4 | Episode: 826 | Score: -134.1 | Avg Score: -115.68 |\n",
"| Experiment: 4 | Episode: 827 | Score: -105.1 | Avg Score: -114.64 |\n",
"| Experiment: 4 | Episode: 828 | Score: -89.89 | Avg Score: -114.97 |\n",
"| Experiment: 4 | Episode: 829 | Score: -207.92 | Avg Score: -115.7 |\n",
"| Experiment: 4 | Episode: 830 | Score: -109.09 | Avg Score: -115.92 |\n",
"| Experiment: 4 | Episode: 831 | Score: -127.99 | Avg Score: -116.27 |\n",
"| Experiment: 4 | Episode: 832 | Score: -122.55 | Avg Score: -116.48 |\n",
"| Experiment: 4 | Episode: 833 | Score: -205.08 | Avg Score: -117.75 |\n",
"| Experiment: 4 | Episode: 834 | Score: -116.05 | Avg Score: -117.67 |\n",
"| Experiment: 4 | Episode: 835 | Score: 14.68 | Avg Score: -115.1 |\n",
"| Experiment: 4 | Episode: 836 | Score: -122.99 | Avg Score: -115.57 |\n",
"| Experiment: 4 | Episode: 837 | Score: -192.56 | Avg Score: -115.14 |\n",
"| Experiment: 4 | Episode: 838 | Score: -80.8 | Avg Score: -114.82 |\n",
"| Experiment: 4 | Episode: 839 | Score: -106.02 | Avg Score: -115.03 |\n",
"| Experiment: 4 | Episode: 840 | Score: -100.5 | Avg Score: -115.09 |\n",
"| Experiment: 4 | Episode: 841 | Score: -100.56 | Avg Score: -114.95 |\n",
"| Experiment: 4 | Episode: 842 | Score: -134.06 | Avg Score: -115.4 |\n",
"| Experiment: 4 | Episode: 843 | Score: -114.76 | Avg Score: -115.54 |\n",
"| Experiment: 4 | Episode: 844 | Score: -83.7 | Avg Score: -115.22 |\n",
"| Experiment: 4 | Episode: 845 | Score: -59.6 | Avg Score: -114.61 |\n",
"| Experiment: 4 | Episode: 846 | Score: -157.43 | Avg Score: -115.68 |\n",
"| Experiment: 4 | Episode: 847 | Score: -152.18 | Avg Score: -116.64 |\n",
"| Experiment: 4 | Episode: 848 | Score: -90.58 | Avg Score: -116.19 |\n",
"| Experiment: 4 | Episode: 849 | Score: -131.96 | Avg Score: -116.8 |\n",
"| Experiment: 4 | Episode: 850 | Score: -118.94 | Avg Score: -116.94 |\n",
"| Experiment: 4 | Episode: 851 | Score: -114.58 | Avg Score: -117.17 |\n",
"| Experiment: 4 | Episode: 852 | Score: -115.91 | Avg Score: -117.63 |\n",
"| Experiment: 4 | Episode: 853 | Score: -125.13 | Avg Score: -118.26 |\n",
"| Experiment: 4 | Episode: 854 | Score: -73.94 | Avg Score: -117.68 |\n",
"| Experiment: 4 | Episode: 855 | Score: -201.69 | Avg Score: -118.91 |\n",
"| Experiment: 4 | Episode: 856 | Score: -99.85 | Avg Score: -118.54 |\n",
"| Experiment: 4 | Episode: 857 | Score: -122.51 | Avg Score: -118.93 |\n",
"| Experiment: 4 | Episode: 858 | Score: -137.38 | Avg Score: -119.62 |\n",
"| Experiment: 4 | Episode: 859 | Score: -85.44 | Avg Score: -119.54 |\n",
"| Experiment: 4 | Episode: 860 | Score: -93.2 | Avg Score: -118.7 |\n",
"| Experiment: 4 | Episode: 861 | Score: -93.65 | Avg Score: -118.83 |\n",
"| Experiment: 4 | Episode: 862 | Score: -93.09 | Avg Score: -118.4 |\n",
"| Experiment: 4 | Episode: 863 | Score: -91.55 | Avg Score: -118.16 |\n",
"| Experiment: 4 | Episode: 864 | Score: -75.5 | Avg Score: -116.97 |\n",
"| Experiment: 4 | Episode: 865 | Score: -85.95 | Avg Score: -117.41 |\n",
"| Experiment: 4 | Episode: 866 | Score: -54.13 | Avg Score: -115.81 |\n",
"| Experiment: 4 | Episode: 867 | Score: -96.54 | Avg Score: -116.06 |\n",
"| Experiment: 4 | Episode: 868 | Score: -72.85 | Avg Score: -115.62 |\n",
"| Experiment: 4 | Episode: 869 | Score: -73.49 | Avg Score: -115.59 |\n",
"| Experiment: 4 | Episode: 870 | Score: -129.23 | Avg Score: -115.98 |\n",
"| Experiment: 4 | Episode: 871 | Score: -121.12 | Avg Score: -116.25 |\n",
"| Experiment: 4 | Episode: 872 | Score: -125.02 | Avg Score: -116.5 |\n",
"| Experiment: 4 | Episode: 873 | Score: -47.62 | Avg Score: -115.82 |\n",
"| Experiment: 4 | Episode: 874 | Score: -110.9 | Avg Score: -114.99 |\n",
"| Experiment: 4 | Episode: 875 | Score: -143.78 | Avg Score: -115.62 |\n",
"| Experiment: 4 | Episode: 876 | Score: -200.11 | Avg Score: -116.94 |\n",
"| Experiment: 4 | Episode: 877 | Score: -113.22 | Avg Score: -116.97 |\n",
"| Experiment: 4 | Episode: 878 | Score: -96.72 | Avg Score: -117.16 |\n",
"| Experiment: 4 | Episode: 879 | Score: -499.03 | Avg Score: -121.47 |\n",
"| Experiment: 4 | Episode: 880 | Score: -125.57 | Avg Score: -122.16 |\n",
"| Experiment: 4 | Episode: 881 | Score: -106.21 | Avg Score: -122.42 |\n",
"| Experiment: 4 | Episode: 882 | Score: -19.03 | Avg Score: -120.85 |\n",
"| Experiment: 4 | Episode: 883 | Score: -100.17 | Avg Score: -120.49 |\n",
"| Experiment: 4 | Episode: 884 | Score: -64.18 | Avg Score: -119.8 |\n",
"| Experiment: 4 | Episode: 885 | Score: -58.51 | Avg Score: -119.11 |\n",
"| Experiment: 4 | Episode: 886 | Score: -232.05 | Avg Score: -118.41 |\n",
"| Experiment: 4 | Episode: 887 | Score: -77.93 | Avg Score: -117.46 |\n",
"| Experiment: 4 | Episode: 888 | Score: -132.05 | Avg Score: -117.55 |\n",
"| Experiment: 4 | Episode: 889 | Score: -74.08 | Avg Score: -117.36 |\n",
"| Experiment: 4 | Episode: 890 | Score: -292.35 | Avg Score: -119.0 |\n",
"| Experiment: 4 | Episode: 891 | Score: -86.4 | Avg Score: -118.54 |\n",
"| Experiment: 4 | Episode: 892 | Score: -133.77 | Avg Score: -119.02 |\n",
"| Experiment: 4 | Episode: 893 | Score: -138.6 | Avg Score: -119.29 |\n",
"| Experiment: 4 | Episode: 894 | Score: -202.01 | Avg Score: -120.51 |\n",
"| Experiment: 4 | Episode: 895 | Score: -96.74 | Avg Score: -121.32 |\n",
"| Experiment: 4 | Episode: 896 | Score: -110.06 | Avg Score: -121.8 |\n",
"| Experiment: 4 | Episode: 897 | Score: -72.78 | Avg Score: -120.85 |\n",
"| Experiment: 4 | Episode: 898 | Score: -107.46 | Avg Score: -120.71 |\n",
"| Experiment: 4 | Episode: 899 | Score: -85.46 | Avg Score: -120.44 |\n",
"| Experiment: 4 | Episode: 900 | Score: -103.37 | Avg Score: -120.38 |\n",
"| Experiment: 4 | Episode: 901 | Score: -86.25 | Avg Score: -120.79 |\n",
"| Experiment: 4 | Episode: 902 | Score: -166.88 | Avg Score: -120.42 |\n",
"| Experiment: 4 | Episode: 903 | Score: -112.44 | Avg Score: -120.08 |\n",
"| Experiment: 4 | Episode: 904 | Score: -97.06 | Avg Score: -120.36 |\n",
"| Experiment: 4 | Episode: 905 | Score: -128.52 | Avg Score: -120.33 |\n",
"| Experiment: 4 | Episode: 906 | Score: -113.9 | Avg Score: -120.52 |\n",
"| Experiment: 4 | Episode: 907 | Score: -102.3 | Avg Score: -120.86 |\n",
"| Experiment: 4 | Episode: 908 | Score: -197.89 | Avg Score: -121.87 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 4 | Episode: 909 | Score: -210.17 | Avg Score: -123.21 |\n",
"| Experiment: 4 | Episode: 910 | Score: -188.98 | Avg Score: -123.82 |\n",
"| Experiment: 4 | Episode: 911 | Score: -118.35 | Avg Score: -124.1 |\n",
"| Experiment: 4 | Episode: 912 | Score: -125.66 | Avg Score: -123.7 |\n",
"| Experiment: 4 | Episode: 913 | Score: -170.35 | Avg Score: -122.61 |\n",
"| Experiment: 4 | Episode: 914 | Score: -98.14 | Avg Score: -122.86 |\n",
"| Experiment: 4 | Episode: 915 | Score: -190.96 | Avg Score: -123.21 |\n",
"| Experiment: 4 | Episode: 916 | Score: -151.98 | Avg Score: -121.92 |\n",
"| Experiment: 4 | Episode: 917 | Score: -105.19 | Avg Score: -121.81 |\n",
"| Experiment: 4 | Episode: 918 | Score: -223.18 | Avg Score: -122.56 |\n",
"| Experiment: 4 | Episode: 919 | Score: -343.86 | Avg Score: -125.26 |\n",
"| Experiment: 4 | Episode: 920 | Score: -97.54 | Avg Score: -125.11 |\n",
"| Experiment: 4 | Episode: 921 | Score: -143.06 | Avg Score: -125.82 |\n",
"| Experiment: 4 | Episode: 922 | Score: -90.0 | Avg Score: -125.96 |\n",
"| Experiment: 4 | Episode: 923 | Score: -141.14 | Avg Score: -125.14 |\n",
"| Experiment: 4 | Episode: 924 | Score: -173.86 | Avg Score: -125.75 |\n",
"| Experiment: 4 | Episode: 925 | Score: -58.62 | Avg Score: -124.98 |\n",
"| Experiment: 4 | Episode: 926 | Score: -108.53 | Avg Score: -124.72 |\n",
"| Experiment: 4 | Episode: 927 | Score: -100.39 | Avg Score: -124.68 |\n",
"| Experiment: 4 | Episode: 928 | Score: -96.93 | Avg Score: -124.75 |\n",
"| Experiment: 4 | Episode: 929 | Score: -105.98 | Avg Score: -123.73 |\n",
"| Experiment: 4 | Episode: 930 | Score: -68.38 | Avg Score: -123.32 |\n",
"| Experiment: 4 | Episode: 931 | Score: -194.9 | Avg Score: -123.99 |\n",
"| Experiment: 4 | Episode: 932 | Score: -142.54 | Avg Score: -124.19 |\n",
"| Experiment: 4 | Episode: 933 | Score: -93.69 | Avg Score: -123.08 |\n",
"| Experiment: 4 | Episode: 934 | Score: -96.26 | Avg Score: -122.88 |\n",
"| Experiment: 4 | Episode: 935 | Score: -82.74 | Avg Score: -123.85 |\n",
"| Experiment: 4 | Episode: 936 | Score: -89.04 | Avg Score: -123.51 |\n",
"| Experiment: 4 | Episode: 937 | Score: -90.38 | Avg Score: -122.49 |\n",
"| Experiment: 4 | Episode: 938 | Score: -70.72 | Avg Score: -122.39 |\n",
"| Experiment: 4 | Episode: 939 | Score: -80.05 | Avg Score: -122.13 |\n",
"| Experiment: 4 | Episode: 940 | Score: -86.59 | Avg Score: -121.99 |\n",
"| Experiment: 4 | Episode: 941 | Score: -164.44 | Avg Score: -122.63 |\n",
"| Experiment: 4 | Episode: 942 | Score: -76.54 | Avg Score: -122.05 |\n",
"| Experiment: 4 | Episode: 943 | Score: -152.54 | Avg Score: -122.43 |\n",
"| Experiment: 4 | Episode: 944 | Score: -124.66 | Avg Score: -122.84 |\n",
"| Experiment: 4 | Episode: 945 | Score: -129.05 | Avg Score: -123.54 |\n",
"| Experiment: 4 | Episode: 946 | Score: -130.12 | Avg Score: -123.26 |\n",
"| Experiment: 4 | Episode: 947 | Score: -215.75 | Avg Score: -123.9 |\n",
"| Experiment: 4 | Episode: 948 | Score: -91.69 | Avg Score: -123.91 |\n",
"| Experiment: 4 | Episode: 949 | Score: -111.89 | Avg Score: -123.71 |\n",
"| Experiment: 4 | Episode: 950 | Score: -78.47 | Avg Score: -123.31 |\n",
"| Experiment: 4 | Episode: 951 | Score: -87.02 | Avg Score: -123.03 |\n",
"| Experiment: 4 | Episode: 952 | Score: -87.82 | Avg Score: -122.75 |\n",
"| Experiment: 4 | Episode: 953 | Score: -141.41 | Avg Score: -122.91 |\n",
"| Experiment: 4 | Episode: 954 | Score: -107.67 | Avg Score: -123.25 |\n",
"| Experiment: 4 | Episode: 955 | Score: -65.83 | Avg Score: -121.89 |\n",
"| Experiment: 4 | Episode: 956 | Score: -107.09 | Avg Score: -121.96 |\n",
"| Experiment: 4 | Episode: 957 | Score: -140.44 | Avg Score: -122.14 |\n",
"| Experiment: 4 | Episode: 958 | Score: -68.31 | Avg Score: -121.45 |\n",
"| Experiment: 4 | Episode: 959 | Score: -143.39 | Avg Score: -122.03 |\n",
"| Experiment: 4 | Episode: 960 | Score: -147.36 | Avg Score: -122.57 |\n",
"| Experiment: 4 | Episode: 961 | Score: -106.65 | Avg Score: -122.7 |\n",
"| Experiment: 4 | Episode: 962 | Score: -272.36 | Avg Score: -124.49 |\n",
"| Experiment: 4 | Episode: 963 | Score: -61.04 | Avg Score: -124.19 |\n",
"| Experiment: 4 | Episode: 964 | Score: -142.21 | Avg Score: -124.86 |\n",
"| Experiment: 4 | Episode: 965 | Score: -13.32 | Avg Score: -124.13 |\n",
"| Experiment: 4 | Episode: 966 | Score: -113.88 | Avg Score: -124.73 |\n",
"| Experiment: 4 | Episode: 967 | Score: -100.67 | Avg Score: -124.77 |\n",
"| Experiment: 4 | Episode: 968 | Score: -92.31 | Avg Score: -124.96 |\n",
"| Experiment: 4 | Episode: 969 | Score: -87.29 | Avg Score: -125.1 |\n",
"| Experiment: 4 | Episode: 970 | Score: -145.72 | Avg Score: -125.27 |\n",
"| Experiment: 4 | Episode: 971 | Score: -150.1 | Avg Score: -125.56 |\n",
"| Experiment: 4 | Episode: 972 | Score: -141.44 | Avg Score: -125.72 |\n",
"| Experiment: 4 | Episode: 973 | Score: -119.37 | Avg Score: -126.44 |\n",
"| Experiment: 4 | Episode: 974 | Score: -166.75 | Avg Score: -127.0 |\n",
"| Experiment: 4 | Episode: 975 | Score: -175.2 | Avg Score: -127.31 |\n",
"| Experiment: 4 | Episode: 976 | Score: -72.85 | Avg Score: -126.04 |\n",
"| Experiment: 4 | Episode: 977 | Score: -102.71 | Avg Score: -125.93 |\n",
"| Experiment: 4 | Episode: 978 | Score: -125.33 | Avg Score: -126.22 |\n",
"| Experiment: 4 | Episode: 979 | Score: -296.91 | Avg Score: -124.2 |\n",
"| Experiment: 4 | Episode: 980 | Score: -161.33 | Avg Score: -124.56 |\n",
"| Experiment: 4 | Episode: 981 | Score: -232.73 | Avg Score: -125.82 |\n",
"| Experiment: 4 | Episode: 982 | Score: -124.62 | Avg Score: -126.88 |\n",
"| Experiment: 4 | Episode: 983 | Score: -70.53 | Avg Score: -126.58 |\n",
"| Experiment: 4 | Episode: 984 | Score: -97.73 | Avg Score: -126.92 |\n",
"| Experiment: 4 | Episode: 985 | Score: -191.48 | Avg Score: -128.25 |\n",
"| Experiment: 4 | Episode: 986 | Score: -80.02 | Avg Score: -126.73 |\n",
"| Experiment: 4 | Episode: 987 | Score: -112.23 | Avg Score: -127.07 |\n",
"| Experiment: 4 | Episode: 988 | Score: -129.02 | Avg Score: -127.04 |\n",
"| Experiment: 4 | Episode: 989 | Score: -66.15 | Avg Score: -126.96 |\n",
"| Experiment: 4 | Episode: 990 | Score: -96.42 | Avg Score: -125.0 |\n",
"| Experiment: 4 | Episode: 991 | Score: -261.81 | Avg Score: -126.75 |\n",
"| Experiment: 4 | Episode: 992 | Score: -106.3 | Avg Score: -126.48 |\n",
"| Experiment: 4 | Episode: 993 | Score: -116.58 | Avg Score: -126.26 |\n",
"| Experiment: 4 | Episode: 994 | Score: -139.03 | Avg Score: -125.63 |\n",
"| Experiment: 4 | Episode: 995 | Score: -79.51 | Avg Score: -125.46 |\n",
"| Experiment: 4 | Episode: 996 | Score: -31.38 | Avg Score: -124.67 |\n",
"| Experiment: 4 | Episode: 997 | Score: -83.81 | Avg Score: -124.78 |\n",
"| Experiment: 4 | Episode: 998 | Score: -48.23 | Avg Score: -124.19 |\n",
"| Experiment: 4 | Episode: 999 | Score: -39.51 | Avg Score: -123.73 |\n",
"| Experiment: 4 | Episode: 1000 | Score: -158.06 | Avg Score: -124.28 |\n",
"| Experiment: 4 | Episode: 1001 | Score: -107.67 | Avg Score: -124.49 |\n",
"| Experiment: 4 | Episode: 1002 | Score: -106.97 | Avg Score: -123.89 |\n",
"| Experiment: 4 | Episode: 1003 | Score: -124.37 | Avg Score: -124.01 |\n",
"| Experiment: 4 | Episode: 1004 | Score: -137.02 | Avg Score: -124.41 |\n",
"| Experiment: 4 | Episode: 1005 | Score: -165.67 | Avg Score: -124.78 |\n",
"| Experiment: 4 | Episode: 1006 | Score: -139.96 | Avg Score: -125.04 |\n",
"| Experiment: 4 | Episode: 1007 | Score: -87.88 | Avg Score: -124.9 |\n",
"| Experiment: 4 | Episode: 1008 | Score: -154.93 | Avg Score: -124.47 |\n",
"| Experiment: 4 | Episode: 1009 | Score: -155.02 | Avg Score: -123.92 |\n",
"| Experiment: 4 | Episode: 1010 | Score: -161.59 | Avg Score: -123.64 |\n",
"| Experiment: 4 | Episode: 1011 | Score: -138.17 | Avg Score: -123.84 |\n",
"| Experiment: 4 | Episode: 1012 | Score: -119.64 | Avg Score: -123.78 |\n",
"| Experiment: 4 | Episode: 1013 | Score: -134.5 | Avg Score: -123.42 |\n",
"| Experiment: 4 | Episode: 1014 | Score: -47.56 | Avg Score: -122.92 |\n",
"| Experiment: 4 | Episode: 1015 | Score: -102.08 | Avg Score: -122.03 |\n",
"| Experiment: 4 | Episode: 1016 | Score: -184.55 | Avg Score: -122.35 |\n",
"| Experiment: 4 | Episode: 1017 | Score: -125.77 | Avg Score: -122.56 |\n",
"| Experiment: 4 | Episode: 1018 | Score: -93.02 | Avg Score: -121.26 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 4 | Episode: 1019 | Score: -340.65 | Avg Score: -121.22 |\n",
"| Experiment: 4 | Episode: 1020 | Score: -45.0 | Avg Score: -120.7 |\n",
"| Experiment: 4 | Episode: 1021 | Score: -81.61 | Avg Score: -120.08 |\n",
"| Experiment: 4 | Episode: 1022 | Score: -101.79 | Avg Score: -120.2 |\n",
"| Experiment: 4 | Episode: 1023 | Score: -134.32 | Avg Score: -120.13 |\n",
"| Experiment: 4 | Episode: 1024 | Score: -89.2 | Avg Score: -119.29 |\n",
"| Experiment: 4 | Episode: 1025 | Score: -121.07 | Avg Score: -119.91 |\n",
"| Experiment: 4 | Episode: 1026 | Score: -198.28 | Avg Score: -120.81 |\n",
"| Experiment: 4 | Episode: 1027 | Score: -80.27 | Avg Score: -120.61 |\n",
"| Experiment: 4 | Episode: 1028 | Score: -85.84 | Avg Score: -120.5 |\n",
"| Experiment: 4 | Episode: 1029 | Score: -93.93 | Avg Score: -120.38 |\n",
"| Experiment: 4 | Episode: 1030 | Score: -97.33 | Avg Score: -120.67 |\n",
"| Experiment: 4 | Episode: 1031 | Score: -61.87 | Avg Score: -119.34 |\n",
"| Experiment: 4 | Episode: 1032 | Score: -277.63 | Avg Score: -120.69 |\n",
"| Experiment: 4 | Episode: 1033 | Score: -56.09 | Avg Score: -120.31 |\n",
"| Experiment: 4 | Episode: 1034 | Score: -141.66 | Avg Score: -120.77 |\n",
"| Experiment: 4 | Episode: 1035 | Score: -124.02 | Avg Score: -121.18 |\n",
"| Experiment: 4 | Episode: 1036 | Score: -135.0 | Avg Score: -121.64 |\n",
"| Experiment: 4 | Episode: 1037 | Score: -224.77 | Avg Score: -122.98 |\n",
"| Experiment: 4 | Episode: 1038 | Score: -101.95 | Avg Score: -123.29 |\n",
"| Experiment: 4 | Episode: 1039 | Score: -354.34 | Avg Score: -126.04 |\n",
"| Experiment: 4 | Episode: 1040 | Score: -77.16 | Avg Score: -125.94 |\n",
"| Experiment: 4 | Episode: 1041 | Score: -111.38 | Avg Score: -125.41 |\n",
"| Experiment: 4 | Episode: 1042 | Score: -137.0 | Avg Score: -126.02 |\n",
"| Experiment: 4 | Episode: 1043 | Score: -147.62 | Avg Score: -125.97 |\n",
"| Experiment: 4 | Episode: 1044 | Score: -64.61 | Avg Score: -125.37 |\n",
"| Experiment: 4 | Episode: 1045 | Score: -213.72 | Avg Score: -126.21 |\n",
"| Experiment: 4 | Episode: 1046 | Score: -115.41 | Avg Score: -126.07 |\n",
"| Experiment: 4 | Episode: 1047 | Score: -105.38 | Avg Score: -124.96 |\n",
"| Experiment: 4 | Episode: 1048 | Score: -93.73 | Avg Score: -124.98 |\n",
"| Experiment: 4 | Episode: 1049 | Score: -85.41 | Avg Score: -124.72 |\n",
"| Experiment: 4 | Episode: 1050 | Score: -230.48 | Avg Score: -126.24 |\n",
"| Experiment: 4 | Episode: 1051 | Score: -117.26 | Avg Score: -126.54 |\n",
"| Experiment: 4 | Episode: 1052 | Score: -59.61 | Avg Score: -126.26 |\n",
"| Experiment: 4 | Episode: 1053 | Score: -41.71 | Avg Score: -125.26 |\n",
"| Experiment: 4 | Episode: 1054 | Score: -106.18 | Avg Score: -125.25 |\n",
"| Experiment: 4 | Episode: 1055 | Score: -104.81 | Avg Score: -125.64 |\n",
"| Experiment: 4 | Episode: 1056 | Score: -75.28 | Avg Score: -125.32 |\n",
"| Experiment: 4 | Episode: 1057 | Score: -58.74 | Avg Score: -124.5 |\n",
"| Experiment: 4 | Episode: 1058 | Score: -157.48 | Avg Score: -125.39 |\n",
"| Experiment: 4 | Episode: 1059 | Score: -75.28 | Avg Score: -124.71 |\n",
"| Experiment: 4 | Episode: 1060 | Score: -87.18 | Avg Score: -124.11 |\n",
"| Experiment: 4 | Episode: 1061 | Score: -26.99 | Avg Score: -123.31 |\n",
"| Experiment: 4 | Episode: 1062 | Score: -83.83 | Avg Score: -121.43 |\n",
"| Experiment: 4 | Episode: 1063 | Score: -155.0 | Avg Score: -122.37 |\n",
"| Experiment: 4 | Episode: 1064 | Score: -128.38 | Avg Score: -122.23 |\n",
"| Experiment: 4 | Episode: 1065 | Score: -106.23 | Avg Score: -123.16 |\n",
"| Experiment: 4 | Episode: 1066 | Score: -59.02 | Avg Score: -122.61 |\n",
"| Experiment: 4 | Episode: 1067 | Score: -111.97 | Avg Score: -122.72 |\n",
"| Experiment: 4 | Episode: 1068 | Score: -87.67 | Avg Score: -122.68 |\n",
"| Experiment: 4 | Episode: 1069 | Score: -87.79 | Avg Score: -122.68 |\n",
"| Experiment: 4 | Episode: 1070 | Score: -87.31 | Avg Score: -122.1 |\n",
"| Experiment: 4 | Episode: 1071 | Score: -176.7 | Avg Score: -122.36 |\n",
"| Experiment: 4 | Episode: 1072 | Score: -76.78 | Avg Score: -121.72 |\n",
"| Experiment: 4 | Episode: 1073 | Score: -199.5 | Avg Score: -122.52 |\n",
"| Experiment: 4 | Episode: 1074 | Score: -86.82 | Avg Score: -121.72 |\n",
"| Experiment: 4 | Episode: 1075 | Score: -161.63 | Avg Score: -121.58 |\n",
"| Experiment: 4 | Episode: 1076 | Score: -110.35 | Avg Score: -121.96 |\n",
"| Experiment: 4 | Episode: 1077 | Score: -18.9 | Avg Score: -121.12 |\n",
"| Experiment: 4 | Episode: 1078 | Score: -101.94 | Avg Score: -120.89 |\n",
"| Experiment: 4 | Episode: 1079 | Score: -93.03 | Avg Score: -118.85 |\n",
"| Experiment: 4 | Episode: 1080 | Score: -91.73 | Avg Score: -118.15 |\n",
"| Experiment: 4 | Episode: 1081 | Score: -104.13 | Avg Score: -116.87 |\n",
"| Experiment: 4 | Episode: 1082 | Score: -153.19 | Avg Score: -117.15 |\n",
"| Experiment: 4 | Episode: 1083 | Score: -36.4 | Avg Score: -116.81 |\n",
"| Experiment: 4 | Episode: 1084 | Score: -156.0 | Avg Score: -117.39 |\n",
"| Experiment: 4 | Episode: 1085 | Score: -109.3 | Avg Score: -116.57 |\n",
"| Experiment: 4 | Episode: 1086 | Score: -91.97 | Avg Score: -116.69 |\n",
"| Experiment: 4 | Episode: 1087 | Score: -42.21 | Avg Score: -115.99 |\n",
"| Experiment: 4 | Episode: 1088 | Score: -70.92 | Avg Score: -115.41 |\n",
"| Experiment: 4 | Episode: 1089 | Score: -98.26 | Avg Score: -115.73 |\n",
"| Experiment: 4 | Episode: 1090 | Score: -142.94 | Avg Score: -116.2 |\n",
"| Experiment: 4 | Episode: 1091 | Score: -133.53 | Avg Score: -114.91 |\n",
"| Experiment: 4 | Episode: 1092 | Score: -119.54 | Avg Score: -115.05 |\n",
"| Experiment: 4 | Episode: 1093 | Score: -93.62 | Avg Score: -114.82 |\n",
"| Experiment: 4 | Episode: 1094 | Score: -36.07 | Avg Score: -113.79 |\n",
"| Experiment: 4 | Episode: 1095 | Score: -133.73 | Avg Score: -114.33 |\n",
"| Experiment: 4 | Episode: 1096 | Score: -172.98 | Avg Score: -115.74 |\n",
"| Experiment: 4 | Episode: 1097 | Score: -195.4 | Avg Score: -116.86 |\n",
"| Experiment: 4 | Episode: 1098 | Score: -76.1 | Avg Score: -117.14 |\n",
"| Experiment: 4 | Episode: 1099 | Score: -40.44 | Avg Score: -117.15 |\n",
"| Experiment: 4 | Episode: 1100 | Score: -266.3 | Avg Score: -118.23 |\n",
"| Experiment: 4 | Episode: 1101 | Score: -92.97 | Avg Score: -118.08 |\n",
"| Experiment: 4 | Episode: 1102 | Score: -82.74 | Avg Score: -117.84 |\n",
"| Experiment: 4 | Episode: 1103 | Score: -193.43 | Avg Score: -118.53 |\n",
"| Experiment: 4 | Episode: 1104 | Score: -123.81 | Avg Score: -118.4 |\n",
"| Experiment: 4 | Episode: 1105 | Score: -90.6 | Avg Score: -117.65 |\n",
"| Experiment: 4 | Episode: 1106 | Score: -78.22 | Avg Score: -117.03 |\n",
"| Experiment: 4 | Episode: 1107 | Score: -188.36 | Avg Score: -118.04 |\n",
"| Experiment: 4 | Episode: 1108 | Score: -94.68 | Avg Score: -117.43 |\n",
"| Experiment: 4 | Episode: 1109 | Score: -43.84 | Avg Score: -116.32 |\n",
"| Experiment: 4 | Episode: 1110 | Score: -105.89 | Avg Score: -115.76 |\n",
"| Experiment: 4 | Episode: 1111 | Score: -301.12 | Avg Score: -117.39 |\n",
"| Experiment: 4 | Episode: 1112 | Score: -105.91 | Avg Score: -117.26 |\n",
"| Experiment: 4 | Episode: 1113 | Score: -75.21 | Avg Score: -116.66 |\n",
"| Experiment: 4 | Episode: 1114 | Score: -66.18 | Avg Score: -116.85 |\n",
"| Experiment: 4 | Episode: 1115 | Score: -103.36 | Avg Score: -116.86 |\n",
"| Experiment: 4 | Episode: 1116 | Score: -65.37 | Avg Score: -115.67 |\n",
"| Experiment: 4 | Episode: 1117 | Score: -90.5 | Avg Score: -115.32 |\n",
"| Experiment: 4 | Episode: 1118 | Score: -81.66 | Avg Score: -115.21 |\n",
"| Experiment: 4 | Episode: 1119 | Score: -155.7 | Avg Score: -113.36 |\n",
"| Experiment: 4 | Episode: 1120 | Score: -142.89 | Avg Score: -114.33 |\n",
"| Experiment: 4 | Episode: 1121 | Score: -120.62 | Avg Score: -114.72 |\n",
"| Experiment: 4 | Episode: 1122 | Score: -22.23 | Avg Score: -113.93 |\n",
"| Experiment: 4 | Episode: 1123 | Score: -161.75 | Avg Score: -114.2 |\n",
"| Experiment: 4 | Episode: 1124 | Score: -84.19 | Avg Score: -114.15 |\n",
"| Experiment: 4 | Episode: 1125 | Score: -66.05 | Avg Score: -113.6 |\n",
"| Experiment: 4 | Episode: 1126 | Score: -65.14 | Avg Score: -112.27 |\n",
"| Experiment: 4 | Episode: 1127 | Score: -83.98 | Avg Score: -112.31 |\n",
"| Experiment: 4 | Episode: 1128 | Score: -139.17 | Avg Score: -112.84 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 4 | Episode: 1129 | Score: -83.6 | Avg Score: -112.74 |\n",
"| Experiment: 4 | Episode: 1130 | Score: -78.0 | Avg Score: -112.55 |\n",
"| Experiment: 4 | Episode: 1131 | Score: -85.65 | Avg Score: -112.78 |\n",
"| Experiment: 4 | Episode: 1132 | Score: -272.64 | Avg Score: -112.73 |\n",
"| Experiment: 4 | Episode: 1133 | Score: -125.1 | Avg Score: -113.42 |\n",
"| Experiment: 4 | Episode: 1134 | Score: -12.04 | Avg Score: -112.13 |\n",
"| Experiment: 4 | Episode: 1135 | Score: -92.04 | Avg Score: -111.81 |\n",
"| Experiment: 4 | Episode: 1136 | Score: -97.27 | Avg Score: -111.43 |\n",
"| Experiment: 4 | Episode: 1137 | Score: -98.67 | Avg Score: -110.17 |\n",
"| Experiment: 4 | Episode: 1138 | Score: -58.59 | Avg Score: -109.74 |\n",
"| Experiment: 4 | Episode: 1139 | Score: -44.29 | Avg Score: -106.63 |\n",
"| Experiment: 4 | Episode: 1140 | Score: -60.98 | Avg Score: -106.47 |\n",
"| Experiment: 4 | Episode: 1141 | Score: -25.54 | Avg Score: -105.61 |\n",
"| Experiment: 4 | Episode: 1142 | Score: -188.83 | Avg Score: -106.13 |\n",
"| Experiment: 4 | Episode: 1143 | Score: -215.22 | Avg Score: -106.81 |\n",
"| Experiment: 4 | Episode: 1144 | Score: -100.43 | Avg Score: -107.17 |\n",
"| Experiment: 4 | Episode: 1145 | Score: -75.0 | Avg Score: -105.78 |\n",
"| Experiment: 4 | Episode: 1146 | Score: -82.22 | Avg Score: -105.45 |\n",
"| Experiment: 4 | Episode: 1147 | Score: -111.72 | Avg Score: -105.51 |\n",
"| Experiment: 4 | Episode: 1148 | Score: -62.3 | Avg Score: -105.2 |\n",
"| Experiment: 4 | Episode: 1149 | Score: -85.65 | Avg Score: -105.2 |\n",
"| Experiment: 4 | Episode: 1150 | Score: -50.61 | Avg Score: -103.4 |\n",
"| Experiment: 4 | Episode: 1151 | Score: -87.87 | Avg Score: -103.11 |\n",
"| Experiment: 4 | Episode: 1152 | Score: -71.35 | Avg Score: -103.22 |\n",
"| Experiment: 4 | Episode: 1153 | Score: -52.16 | Avg Score: -103.33 |\n",
"| Experiment: 4 | Episode: 1154 | Score: -75.24 | Avg Score: -103.02 |\n",
"| Experiment: 4 | Episode: 1155 | Score: -97.6 | Avg Score: -102.95 |\n",
"| Experiment: 4 | Episode: 1156 | Score: -63.31 | Avg Score: -102.83 |\n",
"| Experiment: 4 | Episode: 1157 | Score: -77.26 | Avg Score: -103.01 |\n",
"| Experiment: 4 | Episode: 1158 | Score: -54.92 | Avg Score: -101.99 |\n",
"| Experiment: 4 | Episode: 1159 | Score: -55.63 | Avg Score: -101.79 |\n",
"| Experiment: 4 | Episode: 1160 | Score: -50.03 | Avg Score: -101.42 |\n",
"| Experiment: 4 | Episode: 1161 | Score: -59.95 | Avg Score: -101.75 |\n",
"| Experiment: 4 | Episode: 1162 | Score: -73.04 | Avg Score: -101.64 |\n",
"| Experiment: 4 | Episode: 1163 | Score: -62.07 | Avg Score: -100.71 |\n",
"| Experiment: 4 | Episode: 1164 | Score: -137.98 | Avg Score: -100.81 |\n",
"| Experiment: 4 | Episode: 1165 | Score: -150.38 | Avg Score: -101.25 |\n",
"| Experiment: 4 | Episode: 1166 | Score: -72.91 | Avg Score: -101.39 |\n",
"| Experiment: 4 | Episode: 1167 | Score: -54.51 | Avg Score: -100.81 |\n",
"| Experiment: 4 | Episode: 1168 | Score: -141.15 | Avg Score: -101.35 |\n",
"| Experiment: 4 | Episode: 1169 | Score: -72.95 | Avg Score: -101.2 |\n",
"| Experiment: 4 | Episode: 1170 | Score: -76.42 | Avg Score: -101.09 |\n",
"| Experiment: 4 | Episode: 1171 | Score: -68.07 | Avg Score: -100.0 |\n",
"| Experiment: 4 | Episode: 1172 | Score: -97.34 | Avg Score: -100.21 |\n",
"| Experiment: 4 | Episode: 1173 | Score: -120.46 | Avg Score: -99.42 |\n",
"| Experiment: 4 | Episode: 1174 | Score: -206.72 | Avg Score: -100.62 |\n",
"| Experiment: 4 | Episode: 1175 | Score: -93.21 | Avg Score: -99.93 |\n",
"| Experiment: 4 | Episode: 1176 | Score: -96.07 | Avg Score: -99.79 |\n",
"| Experiment: 4 | Episode: 1177 | Score: -132.55 | Avg Score: -100.93 |\n",
"| Experiment: 4 | Episode: 1178 | Score: -26.58 | Avg Score: -100.18 |\n",
"| Experiment: 4 | Episode: 1179 | Score: -107.84 | Avg Score: -100.32 |\n",
"| Experiment: 4 | Episode: 1180 | Score: -67.31 | Avg Score: -100.08 |\n",
"| Experiment: 4 | Episode: 1181 | Score: -196.55 | Avg Score: -101.0 |\n",
"| Experiment: 4 | Episode: 1182 | Score: -76.44 | Avg Score: -100.24 |\n",
"| Experiment: 4 | Episode: 1183 | Score: -92.2 | Avg Score: -100.79 |\n",
"| Experiment: 4 | Episode: 1184 | Score: -61.26 | Avg Score: -99.85 |\n",
"| Experiment: 4 | Episode: 1185 | Score: -9.01 | Avg Score: -98.84 |\n",
"| Experiment: 4 | Episode: 1186 | Score: -150.26 | Avg Score: -99.43 |\n",
"| Experiment: 4 | Episode: 1187 | Score: -219.68 | Avg Score: -101.2 |\n",
"| Experiment: 4 | Episode: 1188 | Score: -53.01 | Avg Score: -101.02 |\n",
"| Experiment: 4 | Episode: 1189 | Score: -72.87 | Avg Score: -100.77 |\n",
"| Experiment: 4 | Episode: 1190 | Score: -92.85 | Avg Score: -100.27 |\n",
"| Experiment: 4 | Episode: 1191 | Score: -82.5 | Avg Score: -99.76 |\n",
"| Experiment: 4 | Episode: 1192 | Score: -70.75 | Avg Score: -99.27 |\n",
"| Experiment: 4 | Episode: 1193 | Score: -81.6 | Avg Score: -99.15 |\n",
"| Experiment: 4 | Episode: 1194 | Score: -200.48 | Avg Score: -100.79 |\n",
"| Experiment: 4 | Episode: 1195 | Score: -141.29 | Avg Score: -100.87 |\n",
"| Experiment: 4 | Episode: 1196 | Score: -62.91 | Avg Score: -99.77 |\n",
"| Experiment: 4 | Episode: 1197 | Score: -75.64 | Avg Score: -98.57 |\n",
"| Experiment: 4 | Episode: 1198 | Score: -54.32 | Avg Score: -98.35 |\n",
"| Experiment: 4 | Episode: 1199 | Score: -118.24 | Avg Score: -99.13 |\n",
"| Experiment: 4 | Episode: 1200 | Score: -214.01 | Avg Score: -98.61 |\n",
"| Experiment: 4 | Episode: 1201 | Score: -28.54 | Avg Score: -97.96 |\n",
"| Experiment: 4 | Episode: 1202 | Score: -94.05 | Avg Score: -98.08 |\n",
"| Experiment: 4 | Episode: 1203 | Score: -106.72 | Avg Score: -97.21 |\n",
"| Experiment: 4 | Episode: 1204 | Score: -101.68 | Avg Score: -96.99 |\n",
"| Experiment: 4 | Episode: 1205 | Score: -91.13 | Avg Score: -96.99 |\n",
"| Experiment: 4 | Episode: 1206 | Score: -111.87 | Avg Score: -97.33 |\n",
"| Experiment: 4 | Episode: 1207 | Score: -97.38 | Avg Score: -96.42 |\n",
"| Experiment: 4 | Episode: 1208 | Score: -102.38 | Avg Score: -96.5 |\n",
"| Experiment: 4 | Episode: 1209 | Score: -77.04 | Avg Score: -96.83 |\n",
"| Experiment: 4 | Episode: 1210 | Score: -52.62 | Avg Score: -96.3 |\n",
"| Experiment: 4 | Episode: 1211 | Score: -73.19 | Avg Score: -94.02 |\n",
"| Experiment: 4 | Episode: 1212 | Score: -107.56 | Avg Score: -94.03 |\n",
"| Experiment: 4 | Episode: 1213 | Score: -186.77 | Avg Score: -95.15 |\n",
"| Experiment: 4 | Episode: 1214 | Score: -90.58 | Avg Score: -95.39 |\n",
"| Experiment: 4 | Episode: 1215 | Score: -77.9 | Avg Score: -95.14 |\n",
"| Experiment: 4 | Episode: 1216 | Score: -93.59 | Avg Score: -95.42 |\n",
"| Experiment: 4 | Episode: 1217 | Score: -97.41 | Avg Score: -95.49 |\n",
"| Experiment: 4 | Episode: 1218 | Score: -54.59 | Avg Score: -95.22 |\n",
"| Experiment: 4 | Episode: 1219 | Score: -69.43 | Avg Score: -94.36 |\n",
"| Experiment: 4 | Episode: 1220 | Score: -182.43 | Avg Score: -94.75 |\n",
"| Experiment: 4 | Episode: 1221 | Score: -99.07 | Avg Score: -94.54 |\n",
"| Experiment: 4 | Episode: 1222 | Score: -95.43 | Avg Score: -95.27 |\n",
"| Experiment: 4 | Episode: 1223 | Score: -91.7 | Avg Score: -94.57 |\n",
"| Experiment: 4 | Episode: 1224 | Score: -50.31 | Avg Score: -94.23 |\n",
"| Experiment: 4 | Episode: 1225 | Score: -289.77 | Avg Score: -96.47 |\n",
"| Experiment: 4 | Episode: 1226 | Score: -71.47 | Avg Score: -96.53 |\n",
"| Experiment: 4 | Episode: 1227 | Score: -88.1 | Avg Score: -96.57 |\n",
"| Experiment: 4 | Episode: 1228 | Score: -180.15 | Avg Score: -96.98 |\n",
"| Experiment: 4 | Episode: 1229 | Score: -104.92 | Avg Score: -97.19 |\n",
"| Experiment: 4 | Episode: 1230 | Score: -2.08 | Avg Score: -96.43 |\n",
"| Experiment: 4 | Episode: 1231 | Score: -92.92 | Avg Score: -96.51 |\n",
"| Experiment: 4 | Episode: 1232 | Score: -82.61 | Avg Score: -94.61 |\n",
"| Experiment: 4 | Episode: 1233 | Score: -79.66 | Avg Score: -94.15 |\n",
"| Experiment: 4 | Episode: 1234 | Score: -175.06 | Avg Score: -95.78 |\n",
"| Experiment: 4 | Episode: 1235 | Score: -109.23 | Avg Score: -95.95 |\n",
"| Experiment: 4 | Episode: 1236 | Score: -51.05 | Avg Score: -95.49 |\n",
"| Experiment: 4 | Episode: 1237 | Score: -65.33 | Avg Score: -95.16 |\n",
"| Experiment: 4 | Episode: 1238 | Score: -101.37 | Avg Score: -95.59 |\n",
"| Experiment: 4 | Episode: 1239 | Score: -103.55 | Avg Score: -96.18 |\n",
"| Experiment: 4 | Episode: 1240 | Score: -80.17 | Avg Score: -96.37 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 4 | Episode: 1241 | Score: -122.33 | Avg Score: -97.34 |\n",
"| Experiment: 4 | Episode: 1242 | Score: -9.46 | Avg Score: -95.55 |\n",
"| Experiment: 4 | Episode: 1243 | Score: -20.45 | Avg Score: -93.6 |\n",
"| Experiment: 4 | Episode: 1244 | Score: -103.67 | Avg Score: -93.63 |\n",
"| Experiment: 4 | Episode: 1245 | Score: -57.2 | Avg Score: -93.45 |\n",
"| Experiment: 4 | Episode: 1246 | Score: -178.44 | Avg Score: -94.41 |\n",
"| Experiment: 4 | Episode: 1247 | Score: -60.13 | Avg Score: -93.9 |\n",
"| Experiment: 4 | Episode: 1248 | Score: -159.78 | Avg Score: -94.87 |\n",
"| Experiment: 4 | Episode: 1249 | Score: -99.17 | Avg Score: -95.01 |\n",
"| Experiment: 4 | Episode: 1250 | Score: -42.89 | Avg Score: -94.93 |\n",
"| Experiment: 4 | Episode: 1251 | Score: -56.53 | Avg Score: -94.62 |\n",
"| Experiment: 4 | Episode: 1252 | Score: -119.77 | Avg Score: -95.1 |\n",
"| Experiment: 4 | Episode: 1253 | Score: -51.3 | Avg Score: -95.09 |\n",
"| Experiment: 4 | Episode: 1254 | Score: -185.22 | Avg Score: -96.19 |\n",
"| Experiment: 4 | Episode: 1255 | Score: -115.73 | Avg Score: -96.37 |\n",
"| Experiment: 4 | Episode: 1256 | Score: -82.77 | Avg Score: -96.57 |\n",
"| Experiment: 4 | Episode: 1257 | Score: -172.3 | Avg Score: -97.52 |\n",
"| Experiment: 4 | Episode: 1258 | Score: -144.71 | Avg Score: -98.42 |\n",
"| Experiment: 4 | Episode: 1259 | Score: -222.98 | Avg Score: -100.09 |\n",
"| Experiment: 4 | Episode: 1260 | Score: -14.11 | Avg Score: -99.73 |\n",
"| Experiment: 4 | Episode: 1261 | Score: -132.83 | Avg Score: -100.46 |\n",
"| Experiment: 4 | Episode: 1262 | Score: -65.5 | Avg Score: -100.38 |\n",
"| Experiment: 4 | Episode: 1263 | Score: -177.14 | Avg Score: -101.54 |\n",
"| Experiment: 4 | Episode: 1264 | Score: -107.13 | Avg Score: -101.23 |\n",
"| Experiment: 4 | Episode: 1265 | Score: -119.12 | Avg Score: -100.91 |\n",
"| Experiment: 4 | Episode: 1266 | Score: -16.78 | Avg Score: -100.35 |\n",
"| Experiment: 4 | Episode: 1267 | Score: -73.76 | Avg Score: -100.55 |\n",
"| Experiment: 4 | Episode: 1268 | Score: -66.48 | Avg Score: -99.8 |\n",
"| Experiment: 4 | Episode: 1269 | Score: -171.47 | Avg Score: -100.78 |\n",
"| Experiment: 4 | Episode: 1270 | Score: -190.62 | Avg Score: -101.93 |\n",
"| Experiment: 4 | Episode: 1271 | Score: -11.39 | Avg Score: -101.36 |\n",
"| Experiment: 4 | Episode: 1272 | Score: -194.76 | Avg Score: -102.33 |\n",
"| Experiment: 4 | Episode: 1273 | Score: -98.65 | Avg Score: -102.12 |\n",
"| Experiment: 4 | Episode: 1274 | Score: -174.89 | Avg Score: -101.8 |\n",
"| Experiment: 4 | Episode: 1275 | Score: -53.01 | Avg Score: -101.4 |\n",
"| Experiment: 4 | Episode: 1276 | Score: -61.94 | Avg Score: -101.05 |\n",
"| Experiment: 4 | Episode: 1277 | Score: -15.33 | Avg Score: -99.88 |\n",
"| Experiment: 4 | Episode: 1278 | Score: -101.83 | Avg Score: -100.63 |\n",
"| Experiment: 4 | Episode: 1279 | Score: -110.01 | Avg Score: -100.66 |\n",
"| Experiment: 4 | Episode: 1280 | Score: -330.18 | Avg Score: -103.28 |\n",
"| Experiment: 4 | Episode: 1281 | Score: -157.8 | Avg Score: -102.9 |\n",
"| Experiment: 4 | Episode: 1282 | Score: -63.36 | Avg Score: -102.77 |\n",
"| Experiment: 4 | Episode: 1283 | Score: -61.38 | Avg Score: -102.46 |\n",
"| Experiment: 4 | Episode: 1284 | Score: -133.71 | Avg Score: -103.18 |\n",
"| Experiment: 4 | Episode: 1285 | Score: -143.64 | Avg Score: -104.53 |\n",
"| Experiment: 4 | Episode: 1286 | Score: -33.47 | Avg Score: -103.36 |\n",
"| Experiment: 4 | Episode: 1287 | Score: -64.09 | Avg Score: -101.8 |\n",
"| Experiment: 4 | Episode: 1288 | Score: -92.64 | Avg Score: -102.2 |\n",
"| Experiment: 4 | Episode: 1289 | Score: -298.3 | Avg Score: -104.46 |\n",
"| Experiment: 4 | Episode: 1290 | Score: -113.41 | Avg Score: -104.66 |\n",
"| Experiment: 4 | Episode: 1291 | Score: -84.32 | Avg Score: -104.68 |\n",
"| Experiment: 4 | Episode: 1292 | Score: 20.78 | Avg Score: -103.76 |\n",
"| Experiment: 4 | Episode: 1293 | Score: -75.41 | Avg Score: -103.7 |\n",
"| Experiment: 4 | Episode: 1294 | Score: -62.21 | Avg Score: -102.32 |\n",
"| Experiment: 4 | Episode: 1295 | Score: -176.29 | Avg Score: -102.67 |\n",
"| Experiment: 4 | Episode: 1296 | Score: -222.06 | Avg Score: -104.26 |\n",
"| Experiment: 4 | Episode: 1297 | Score: -250.81 | Avg Score: -106.01 |\n",
"| Experiment: 4 | Episode: 1298 | Score: 7.39 | Avg Score: -105.4 |\n",
"| Experiment: 4 | Episode: 1299 | Score: -95.42 | Avg Score: -105.17 |\n",
"| Experiment: 4 | Episode: 1300 | Score: -125.38 | Avg Score: -104.28 |\n",
"| Experiment: 4 | Episode: 1301 | Score: -131.27 | Avg Score: -105.31 |\n",
"| Experiment: 4 | Episode: 1302 | Score: -38.12 | Avg Score: -104.75 |\n",
"| Experiment: 4 | Episode: 1303 | Score: -152.23 | Avg Score: -105.2 |\n",
"| Experiment: 4 | Episode: 1304 | Score: -47.61 | Avg Score: -104.66 |\n",
"| Experiment: 4 | Episode: 1305 | Score: -89.59 | Avg Score: -104.65 |\n",
"| Experiment: 4 | Episode: 1306 | Score: -92.13 | Avg Score: -104.45 |\n",
"| Experiment: 4 | Episode: 1307 | Score: -320.27 | Avg Score: -106.68 |\n",
"| Experiment: 4 | Episode: 1308 | Score: -69.09 | Avg Score: -106.35 |\n",
"| Experiment: 4 | Episode: 1309 | Score: 38.78 | Avg Score: -105.19 |\n",
"| Experiment: 4 | Episode: 1310 | Score: -421.53 | Avg Score: -108.88 |\n",
"| Experiment: 4 | Episode: 1311 | Score: -37.15 | Avg Score: -108.52 |\n",
"| Experiment: 4 | Episode: 1312 | Score: -94.63 | Avg Score: -108.39 |\n",
"| Experiment: 4 | Episode: 1313 | Score: -175.97 | Avg Score: -108.28 |\n",
"| Experiment: 4 | Episode: 1314 | Score: -235.45 | Avg Score: -109.73 |\n",
"| Experiment: 4 | Episode: 1315 | Score: -91.96 | Avg Score: -109.87 |\n",
"| Experiment: 4 | Episode: 1316 | Score: -59.66 | Avg Score: -109.53 |\n",
"| Experiment: 4 | Episode: 1317 | Score: -37.68 | Avg Score: -108.93 |\n",
"| Experiment: 4 | Episode: 1318 | Score: -305.63 | Avg Score: -111.44 |\n",
"| Experiment: 4 | Episode: 1319 | Score: -102.12 | Avg Score: -111.77 |\n",
"| Experiment: 4 | Episode: 1320 | Score: -368.49 | Avg Score: -113.63 |\n",
"| Experiment: 4 | Episode: 1321 | Score: -265.68 | Avg Score: -115.3 |\n",
"| Experiment: 4 | Episode: 1322 | Score: -104.83 | Avg Score: -115.39 |\n",
"| Experiment: 4 | Episode: 1323 | Score: -93.01 | Avg Score: -115.4 |\n",
"| Experiment: 4 | Episode: 1324 | Score: -142.58 | Avg Score: -116.33 |\n",
"| Experiment: 4 | Episode: 1325 | Score: -369.07 | Avg Score: -117.12 |\n",
"| Experiment: 4 | Episode: 1326 | Score: -20.19 | Avg Score: -116.61 |\n",
"| Experiment: 4 | Episode: 1327 | Score: -119.4 | Avg Score: -116.92 |\n",
"| Experiment: 4 | Episode: 1328 | Score: -254.98 | Avg Score: -117.67 |\n",
"| Experiment: 4 | Episode: 1329 | Score: -141.47 | Avg Score: -118.03 |\n",
"| Experiment: 4 | Episode: 1330 | Score: -170.31 | Avg Score: -119.72 |\n",
"| Experiment: 4 | Episode: 1331 | Score: -241.75 | Avg Score: -121.2 |\n",
"| Experiment: 4 | Episode: 1332 | Score: -54.64 | Avg Score: -120.92 |\n",
"| Experiment: 4 | Episode: 1333 | Score: -66.67 | Avg Score: -120.79 |\n",
"| Experiment: 4 | Episode: 1334 | Score: -90.36 | Avg Score: -119.95 |\n",
"| Experiment: 4 | Episode: 1335 | Score: -103.76 | Avg Score: -119.89 |\n",
"| Experiment: 4 | Episode: 1336 | Score: -78.08 | Avg Score: -120.16 |\n",
"| Experiment: 4 | Episode: 1337 | Score: -26.68 | Avg Score: -119.78 |\n",
"| Experiment: 4 | Episode: 1338 | Score: -130.33 | Avg Score: -120.07 |\n",
"| Experiment: 4 | Episode: 1339 | Score: -45.59 | Avg Score: -119.49 |\n",
"| Experiment: 4 | Episode: 1340 | Score: -148.44 | Avg Score: -120.17 |\n",
"| Experiment: 4 | Episode: 1341 | Score: -35.35 | Avg Score: -119.3 |\n",
"| Experiment: 4 | Episode: 1342 | Score: -34.5 | Avg Score: -119.55 |\n",
"| Experiment: 4 | Episode: 1343 | Score: -72.95 | Avg Score: -120.07 |\n",
"| Experiment: 4 | Episode: 1344 | Score: -70.44 | Avg Score: -119.74 |\n",
"| Experiment: 4 | Episode: 1345 | Score: -85.32 | Avg Score: -120.02 |\n",
"| Experiment: 4 | Episode: 1346 | Score: -183.01 | Avg Score: -120.07 |\n",
"| Experiment: 4 | Episode: 1347 | Score: -88.19 | Avg Score: -120.35 |\n",
"| Experiment: 4 | Episode: 1348 | Score: -81.57 | Avg Score: -119.57 |\n",
"| Experiment: 4 | Episode: 1349 | Score: -111.82 | Avg Score: -119.69 |\n",
"| Experiment: 4 | Episode: 1350 | Score: -116.15 | Avg Score: -120.43 |\n",
"| Experiment: 4 | Episode: 1351 | Score: -172.85 | Avg Score: -121.59 |\n",
"| Experiment: 4 | Episode: 1352 | Score: -81.62 | Avg Score: -121.21 |\n",
"| Experiment: 4 | Episode: 1353 | Score: -87.23 | Avg Score: -121.57 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 4 | Episode: 1354 | Score: -78.35 | Avg Score: -120.5 |\n",
"| Experiment: 4 | Episode: 1355 | Score: -41.39 | Avg Score: -119.76 |\n",
"| Experiment: 4 | Episode: 1356 | Score: -105.28 | Avg Score: -119.98 |\n",
"| Experiment: 4 | Episode: 1357 | Score: -52.51 | Avg Score: -118.78 |\n",
"| Experiment: 4 | Episode: 1358 | Score: -87.42 | Avg Score: -118.21 |\n",
"| Experiment: 4 | Episode: 1359 | Score: -110.3 | Avg Score: -117.08 |\n",
"| Experiment: 4 | Episode: 1360 | Score: -73.36 | Avg Score: -117.68 |\n",
"| Experiment: 4 | Episode: 1361 | Score: -224.97 | Avg Score: -118.6 |\n",
"| Experiment: 4 | Episode: 1362 | Score: -124.57 | Avg Score: -119.19 |\n",
"| Experiment: 4 | Episode: 1363 | Score: -17.22 | Avg Score: -117.59 |\n",
"| Experiment: 4 | Episode: 1364 | Score: -84.66 | Avg Score: -117.36 |\n",
"| Experiment: 4 | Episode: 1365 | Score: -29.21 | Avg Score: -116.46 |\n",
"| Experiment: 4 | Episode: 1366 | Score: -46.19 | Avg Score: -116.76 |\n",
"| Experiment: 4 | Episode: 1367 | Score: -79.69 | Avg Score: -116.82 |\n",
"| Experiment: 4 | Episode: 1368 | Score: -164.84 | Avg Score: -117.8 |\n",
"| Experiment: 4 | Episode: 1369 | Score: -56.43 | Avg Score: -116.65 |\n",
"| Experiment: 4 | Episode: 1370 | Score: -83.41 | Avg Score: -115.58 |\n",
"| Experiment: 4 | Episode: 1371 | Score: -129.15 | Avg Score: -116.76 |\n",
"| Experiment: 4 | Episode: 1372 | Score: -212.73 | Avg Score: -116.94 |\n",
"| Experiment: 4 | Episode: 1373 | Score: -96.06 | Avg Score: -116.91 |\n",
"| Experiment: 4 | Episode: 1374 | Score: -274.28 | Avg Score: -117.9 |\n",
"| Experiment: 4 | Episode: 1375 | Score: -103.04 | Avg Score: -118.41 |\n",
"| Experiment: 4 | Episode: 1376 | Score: -137.0 | Avg Score: -119.16 |\n",
"| Experiment: 4 | Episode: 1377 | Score: -82.71 | Avg Score: -119.83 |\n",
"| Experiment: 4 | Episode: 1378 | Score: -84.42 | Avg Score: -119.66 |\n",
"| Experiment: 4 | Episode: 1379 | Score: -64.7 | Avg Score: -119.2 |\n",
"| Experiment: 4 | Episode: 1380 | Score: -84.25 | Avg Score: -116.74 |\n",
"| Experiment: 4 | Episode: 1381 | Score: -265.26 | Avg Score: -117.82 |\n",
"| Experiment: 4 | Episode: 1382 | Score: -175.38 | Avg Score: -118.94 |\n",
"| Experiment: 4 | Episode: 1383 | Score: -107.4 | Avg Score: -119.4 |\n",
"| Experiment: 4 | Episode: 1384 | Score: -32.66 | Avg Score: -118.39 |\n",
"| Experiment: 4 | Episode: 1385 | Score: -97.7 | Avg Score: -117.93 |\n",
"| Experiment: 4 | Episode: 1386 | Score: -220.9 | Avg Score: -119.8 |\n",
"| Experiment: 4 | Episode: 1387 | Score: -31.94 | Avg Score: -119.48 |\n",
"| Experiment: 4 | Episode: 1388 | Score: -53.77 | Avg Score: -119.09 |\n",
"| Experiment: 4 | Episode: 1389 | Score: -98.11 | Avg Score: -117.09 |\n",
"| Experiment: 4 | Episode: 1390 | Score: -136.56 | Avg Score: -117.32 |\n",
"| Experiment: 4 | Episode: 1391 | Score: -126.33 | Avg Score: -117.74 |\n",
"| Experiment: 4 | Episode: 1392 | Score: -51.03 | Avg Score: -118.46 |\n",
"| Experiment: 4 | Episode: 1393 | Score: -94.26 | Avg Score: -118.65 |\n",
"| Experiment: 4 | Episode: 1394 | Score: -100.38 | Avg Score: -119.03 |\n",
"| Experiment: 4 | Episode: 1395 | Score: -29.58 | Avg Score: -117.56 |\n",
"| Experiment: 4 | Episode: 1396 | Score: -105.86 | Avg Score: -116.4 |\n",
"| Experiment: 4 | Episode: 1397 | Score: -55.38 | Avg Score: -114.45 |\n",
"| Experiment: 4 | Episode: 1398 | Score: -180.15 | Avg Score: -116.32 |\n",
"| Experiment: 4 | Episode: 1399 | Score: -57.38 | Avg Score: -115.94 |\n",
"| Experiment: 4 | Episode: 1400 | Score: -87.76 | Avg Score: -115.57 |\n",
"| Experiment: 4 | Episode: 1401 | Score: -146.07 | Avg Score: -115.71 |\n",
"| Experiment: 4 | Episode: 1402 | Score: -77.53 | Avg Score: -116.11 |\n",
"| Experiment: 4 | Episode: 1403 | Score: -86.93 | Avg Score: -115.45 |\n",
"| Experiment: 4 | Episode: 1404 | Score: -30.51 | Avg Score: -115.28 |\n",
"| Experiment: 4 | Episode: 1405 | Score: -76.55 | Avg Score: -115.15 |\n",
"| Experiment: 4 | Episode: 1406 | Score: -101.2 | Avg Score: -115.24 |\n",
"| Experiment: 4 | Episode: 1407 | Score: -86.37 | Avg Score: -112.9 |\n",
"| Experiment: 4 | Episode: 1408 | Score: -10.77 | Avg Score: -112.32 |\n",
"| Experiment: 4 | Episode: 1409 | Score: -22.96 | Avg Score: -112.94 |\n",
"| Experiment: 4 | Episode: 1410 | Score: -102.39 | Avg Score: -109.75 |\n",
"| Experiment: 4 | Episode: 1411 | Score: -94.21 | Avg Score: -110.32 |\n",
"| Experiment: 4 | Episode: 1412 | Score: -22.83 | Avg Score: -109.6 |\n",
"| Experiment: 4 | Episode: 1413 | Score: -119.82 | Avg Score: -109.04 |\n",
"| Experiment: 4 | Episode: 1414 | Score: -44.33 | Avg Score: -107.13 |\n",
"| Experiment: 4 | Episode: 1415 | Score: -71.62 | Avg Score: -106.92 |\n",
"| Experiment: 4 | Episode: 1416 | Score: -55.66 | Avg Score: -106.88 |\n",
"| Experiment: 4 | Episode: 1417 | Score: -92.23 | Avg Score: -107.43 |\n",
"| Experiment: 4 | Episode: 1418 | Score: -279.09 | Avg Score: -107.16 |\n",
"| Experiment: 4 | Episode: 1419 | Score: -80.49 | Avg Score: -106.95 |\n",
"| Experiment: 4 | Episode: 1420 | Score: -33.16 | Avg Score: -103.59 |\n",
"| Experiment: 4 | Episode: 1421 | Score: -35.35 | Avg Score: -101.29 |\n",
"| Experiment: 4 | Episode: 1422 | Score: -75.84 | Avg Score: -101.0 |\n",
"| Experiment: 4 | Episode: 1423 | Score: -11.18 | Avg Score: -100.18 |\n",
"| Experiment: 4 | Episode: 1424 | Score: -215.18 | Avg Score: -100.91 |\n",
"| Experiment: 4 | Episode: 1425 | Score: -91.29 | Avg Score: -98.13 |\n",
"| Experiment: 4 | Episode: 1426 | Score: -34.08 | Avg Score: -98.27 |\n",
"| Experiment: 4 | Episode: 1427 | Score: -368.66 | Avg Score: -100.76 |\n",
"| Experiment: 4 | Episode: 1428 | Score: -124.0 | Avg Score: -99.45 |\n",
"| Experiment: 4 | Episode: 1429 | Score: -106.71 | Avg Score: -99.11 |\n",
"| Experiment: 4 | Episode: 1430 | Score: -87.88 | Avg Score: -98.28 |\n",
"| Experiment: 4 | Episode: 1431 | Score: -68.04 | Avg Score: -96.54 |\n",
"| Experiment: 4 | Episode: 1432 | Score: -77.04 | Avg Score: -96.77 |\n",
"| Experiment: 4 | Episode: 1433 | Score: -105.98 | Avg Score: -97.16 |\n",
"| Experiment: 4 | Episode: 1434 | Score: -68.64 | Avg Score: -96.94 |\n",
"| Experiment: 4 | Episode: 1435 | Score: -4.36 | Avg Score: -95.95 |\n",
"| Experiment: 4 | Episode: 1436 | Score: -114.64 | Avg Score: -96.32 |\n",
"| Experiment: 4 | Episode: 1437 | Score: -81.17 | Avg Score: -96.86 |\n",
"| Experiment: 4 | Episode: 1438 | Score: -8.11 | Avg Score: -95.64 |\n",
"| Experiment: 4 | Episode: 1439 | Score: -73.27 | Avg Score: -95.91 |\n",
"| Experiment: 4 | Episode: 1440 | Score: -119.18 | Avg Score: -95.62 |\n",
"| Experiment: 4 | Episode: 1441 | Score: -5.46 | Avg Score: -95.32 |\n",
"| Experiment: 4 | Episode: 1442 | Score: -17.18 | Avg Score: -95.15 |\n",
"| Experiment: 4 | Episode: 1443 | Score: -16.32 | Avg Score: -94.58 |\n",
"| Experiment: 4 | Episode: 1444 | Score: -56.95 | Avg Score: -94.45 |\n",
"| Experiment: 4 | Episode: 1445 | Score: -44.9 | Avg Score: -94.04 |\n",
"| Experiment: 4 | Episode: 1446 | Score: -153.23 | Avg Score: -93.75 |\n",
"| Experiment: 4 | Episode: 1447 | Score: -98.93 | Avg Score: -93.85 |\n",
"| Experiment: 4 | Episode: 1448 | Score: -106.99 | Avg Score: -94.11 |\n",
"| Experiment: 4 | Episode: 1449 | Score: -95.58 | Avg Score: -93.95 |\n",
"| Experiment: 4 | Episode: 1450 | Score: -33.01 | Avg Score: -93.11 |\n",
"| Experiment: 4 | Episode: 1451 | Score: -48.7 | Avg Score: -91.87 |\n",
"| Experiment: 4 | Episode: 1452 | Score: -81.71 | Avg Score: -91.87 |\n",
"| Experiment: 4 | Episode: 1453 | Score: -58.77 | Avg Score: -91.59 |\n",
"| Experiment: 4 | Episode: 1454 | Score: -73.18 | Avg Score: -91.54 |\n",
"| Experiment: 4 | Episode: 1455 | Score: -70.44 | Avg Score: -91.83 |\n",
"| Experiment: 4 | Episode: 1456 | Score: -136.36 | Avg Score: -92.14 |\n",
"| Experiment: 4 | Episode: 1457 | Score: -220.54 | Avg Score: -93.82 |\n",
"| Experiment: 4 | Episode: 1458 | Score: -6.83 | Avg Score: -93.01 |\n",
"| Experiment: 4 | Episode: 1459 | Score: -20.81 | Avg Score: -92.12 |\n",
"| Experiment: 4 | Episode: 1460 | Score: -147.9 | Avg Score: -92.86 |\n",
"| Experiment: 4 | Episode: 1461 | Score: -16.79 | Avg Score: -90.78 |\n",
"| Experiment: 4 | Episode: 1462 | Score: -224.3 | Avg Score: -91.78 |\n",
"| Experiment: 4 | Episode: 1463 | Score: -126.98 | Avg Score: -92.88 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 4 | Episode: 1464 | Score: -130.46 | Avg Score: -93.34 |\n",
"| Experiment: 4 | Episode: 1465 | Score: -41.06 | Avg Score: -93.45 |\n",
"| Experiment: 4 | Episode: 1466 | Score: -44.5 | Avg Score: -93.44 |\n",
"| Experiment: 4 | Episode: 1467 | Score: -38.27 | Avg Score: -93.02 |\n",
"| Experiment: 4 | Episode: 1468 | Score: -0.86 | Avg Score: -91.38 |\n",
"| Experiment: 4 | Episode: 1469 | Score: -3.95 | Avg Score: -90.86 |\n",
"| Experiment: 4 | Episode: 1470 | Score: -113.13 | Avg Score: -91.16 |\n",
"| Experiment: 4 | Episode: 1471 | Score: -112.12 | Avg Score: -90.98 |\n",
"| Experiment: 4 | Episode: 1472 | Score: -130.11 | Avg Score: -90.16 |\n",
"| Experiment: 4 | Episode: 1473 | Score: -132.24 | Avg Score: -90.52 |\n",
"| Experiment: 4 | Episode: 1474 | Score: -70.51 | Avg Score: -88.48 |\n",
"| Experiment: 4 | Episode: 1475 | Score: -211.81 | Avg Score: -89.57 |\n",
"| Experiment: 4 | Episode: 1476 | Score: -124.18 | Avg Score: -89.44 |\n",
"| Experiment: 4 | Episode: 1477 | Score: -404.13 | Avg Score: -92.66 |\n",
"| Experiment: 4 | Episode: 1478 | Score: -122.43 | Avg Score: -93.04 |\n",
"| Experiment: 4 | Episode: 1479 | Score: -246.19 | Avg Score: -94.85 |\n",
"| Experiment: 4 | Episode: 1480 | Score: 77.81 | Avg Score: -93.23 |\n",
"| Experiment: 4 | Episode: 1481 | Score: -17.49 | Avg Score: -90.75 |\n",
"| Experiment: 4 | Episode: 1482 | Score: -42.16 | Avg Score: -89.42 |\n",
"| Experiment: 4 | Episode: 1483 | Score: -71.6 | Avg Score: -89.06 |\n",
"| Experiment: 4 | Episode: 1484 | Score: -134.44 | Avg Score: -90.08 |\n",
"| Experiment: 4 | Episode: 1485 | Score: -33.83 | Avg Score: -89.44 |\n",
"| Experiment: 4 | Episode: 1486 | Score: -60.3 | Avg Score: -87.84 |\n",
"| Experiment: 4 | Episode: 1487 | Score: -37.85 | Avg Score: -87.9 |\n",
"| Experiment: 4 | Episode: 1488 | Score: -12.7 | Avg Score: -87.48 |\n",
"| Experiment: 4 | Episode: 1489 | Score: -47.11 | Avg Score: -86.97 |\n",
"| Experiment: 4 | Episode: 1490 | Score: -55.9 | Avg Score: -86.17 |\n",
"| Experiment: 4 | Episode: 1491 | Score: -35.35 | Avg Score: -85.26 |\n",
"| Experiment: 4 | Episode: 1492 | Score: -52.35 | Avg Score: -85.27 |\n",
"| Experiment: 4 | Episode: 1493 | Score: -145.67 | Avg Score: -85.79 |\n",
"| Experiment: 4 | Episode: 1494 | Score: -128.08 | Avg Score: -86.06 |\n",
"| Experiment: 4 | Episode: 1495 | Score: -82.02 | Avg Score: -86.59 |\n",
"| Experiment: 4 | Episode: 1496 | Score: -37.54 | Avg Score: -85.9 |\n",
"| Experiment: 4 | Episode: 1497 | Score: -34.03 | Avg Score: -85.69 |\n",
"| Experiment: 4 | Episode: 1498 | Score: -34.32 | Avg Score: -84.23 |\n",
"| Experiment: 4 | Episode: 1499 | Score: -71.98 | Avg Score: -84.38 |\n",
"| Experiment: 4 | Episode: 1500 | Score: -125.21 | Avg Score: -84.75 |\n",
"| Experiment: 4 | Episode: 1501 | Score: -67.41 | Avg Score: -83.97 |\n",
"| Experiment: 4 | Episode: 1502 | Score: -139.18 | Avg Score: -84.58 |\n",
"| Experiment: 4 | Episode: 1503 | Score: -59.73 | Avg Score: -84.31 |\n",
"| Experiment: 4 | Episode: 1504 | Score: -70.98 | Avg Score: -84.72 |\n",
"| Experiment: 4 | Episode: 1505 | Score: -55.81 | Avg Score: -84.51 |\n",
"| Experiment: 4 | Episode: 1506 | Score: -74.44 | Avg Score: -84.24 |\n",
"| Experiment: 4 | Episode: 1507 | Score: -104.16 | Avg Score: -84.42 |\n",
"| Experiment: 4 | Episode: 1508 | Score: -59.99 | Avg Score: -84.91 |\n",
"| Experiment: 4 | Episode: 1509 | Score: -173.53 | Avg Score: -86.42 |\n",
"| Experiment: 4 | Episode: 1510 | Score: -68.86 | Avg Score: -86.08 |\n",
"| Experiment: 4 | Episode: 1511 | Score: 84.2 | Avg Score: -84.3 |\n",
"| Experiment: 4 | Episode: 1512 | Score: -90.28 | Avg Score: -84.97 |\n",
"| Experiment: 4 | Episode: 1513 | Score: -153.38 | Avg Score: -85.31 |\n",
"| Experiment: 4 | Episode: 1514 | Score: -91.85 | Avg Score: -85.78 |\n",
"| Experiment: 4 | Episode: 1515 | Score: 27.44 | Avg Score: -84.79 |\n",
"| Experiment: 4 | Episode: 1516 | Score: -139.23 | Avg Score: -85.63 |\n",
"| Experiment: 4 | Episode: 1517 | Score: -72.14 | Avg Score: -85.43 |\n",
"| Experiment: 4 | Episode: 1518 | Score: -34.5 | Avg Score: -82.98 |\n",
"| Experiment: 4 | Episode: 1519 | Score: -194.83 | Avg Score: -84.12 |\n",
"| Experiment: 4 | Episode: 1520 | Score: -47.61 | Avg Score: -84.27 |\n",
"| Experiment: 4 | Episode: 1521 | Score: -135.32 | Avg Score: -85.27 |\n",
"| Experiment: 4 | Episode: 1522 | Score: -59.77 | Avg Score: -85.11 |\n",
"| Experiment: 4 | Episode: 1523 | Score: -56.59 | Avg Score: -85.56 |\n",
"| Experiment: 4 | Episode: 1524 | Score: -61.1 | Avg Score: -84.02 |\n",
"| Experiment: 4 | Episode: 1525 | Score: -53.49 | Avg Score: -83.64 |\n",
"| Experiment: 4 | Episode: 1526 | Score: -53.04 | Avg Score: -83.83 |\n",
"| Experiment: 4 | Episode: 1527 | Score: -44.25 | Avg Score: -80.59 |\n",
"| Experiment: 4 | Episode: 1528 | Score: -5.12 | Avg Score: -79.4 |\n",
"| Experiment: 4 | Episode: 1529 | Score: -155.61 | Avg Score: -79.89 |\n",
"| Experiment: 4 | Episode: 1530 | Score: -84.36 | Avg Score: -79.85 |\n",
"| Experiment: 4 | Episode: 1531 | Score: -10.09 | Avg Score: -79.27 |\n",
"| Experiment: 4 | Episode: 1532 | Score: -257.04 | Avg Score: -81.07 |\n",
"| Experiment: 4 | Episode: 1533 | Score: -48.09 | Avg Score: -80.49 |\n",
"| Experiment: 4 | Episode: 1534 | Score: -28.38 | Avg Score: -80.09 |\n",
"| Experiment: 4 | Episode: 1535 | Score: -111.44 | Avg Score: -81.16 |\n",
"| Experiment: 4 | Episode: 1536 | Score: -122.97 | Avg Score: -81.25 |\n",
"| Experiment: 4 | Episode: 1537 | Score: -62.8 | Avg Score: -81.06 |\n",
"| Experiment: 4 | Episode: 1538 | Score: -225.65 | Avg Score: -83.24 |\n",
"| Experiment: 4 | Episode: 1539 | Score: 19.78 | Avg Score: -82.31 |\n",
"| Experiment: 4 | Episode: 1540 | Score: -60.99 | Avg Score: -81.73 |\n",
"| Experiment: 4 | Episode: 1541 | Score: -82.59 | Avg Score: -82.5 |\n",
"| Experiment: 4 | Episode: 1542 | Score: -99.91 | Avg Score: -83.32 |\n",
"| Experiment: 4 | Episode: 1543 | Score: -52.33 | Avg Score: -83.68 |\n",
"| Experiment: 4 | Episode: 1544 | Score: -75.96 | Avg Score: -83.87 |\n",
"| Experiment: 4 | Episode: 1545 | Score: -59.28 | Avg Score: -84.02 |\n",
"| Experiment: 4 | Episode: 1546 | Score: -155.39 | Avg Score: -84.04 |\n",
"| Experiment: 4 | Episode: 1547 | Score: -101.02 | Avg Score: -84.06 |\n",
"| Experiment: 4 | Episode: 1548 | Score: -46.79 | Avg Score: -83.46 |\n",
"| Experiment: 4 | Episode: 1549 | Score: -206.81 | Avg Score: -84.57 |\n",
"| Experiment: 4 | Episode: 1550 | Score: -43.21 | Avg Score: -84.67 |\n",
"| Experiment: 4 | Episode: 1551 | Score: -150.46 | Avg Score: -85.69 |\n",
"| Experiment: 4 | Episode: 1552 | Score: -74.46 | Avg Score: -85.62 |\n",
"| Experiment: 4 | Episode: 1553 | Score: -27.66 | Avg Score: -85.31 |\n",
"| Experiment: 4 | Episode: 1554 | Score: -81.18 | Avg Score: -85.39 |\n",
"| Experiment: 4 | Episode: 1555 | Score: -22.77 | Avg Score: -84.91 |\n",
"| Experiment: 4 | Episode: 1556 | Score: -84.92 | Avg Score: -84.4 |\n",
"| Experiment: 4 | Episode: 1557 | Score: -84.83 | Avg Score: -83.04 |\n",
"| Experiment: 4 | Episode: 1558 | Score: -86.0 | Avg Score: -83.83 |\n",
"| Experiment: 4 | Episode: 1559 | Score: -101.69 | Avg Score: -84.64 |\n",
"| Experiment: 4 | Episode: 1560 | Score: -93.33 | Avg Score: -84.09 |\n",
"| Experiment: 4 | Episode: 1561 | Score: -108.23 | Avg Score: -85.01 |\n",
"| Experiment: 4 | Episode: 1562 | Score: -182.71 | Avg Score: -84.59 |\n",
"| Experiment: 4 | Episode: 1563 | Score: -139.1 | Avg Score: -84.71 |\n",
"| Experiment: 4 | Episode: 1564 | Score: -168.59 | Avg Score: -85.09 |\n",
"| Experiment: 4 | Episode: 1565 | Score: -124.79 | Avg Score: -85.93 |\n",
"| Experiment: 4 | Episode: 1566 | Score: -52.36 | Avg Score: -86.01 |\n",
"| Experiment: 4 | Episode: 1567 | Score: -135.65 | Avg Score: -86.98 |\n",
"| Experiment: 4 | Episode: 1568 | Score: -91.16 | Avg Score: -87.89 |\n",
"| Experiment: 4 | Episode: 1569 | Score: -100.64 | Avg Score: -88.85 |\n",
"| Experiment: 4 | Episode: 1570 | Score: -223.25 | Avg Score: -89.96 |\n",
"| Experiment: 4 | Episode: 1571 | Score: -153.91 | Avg Score: -90.37 |\n",
"| Experiment: 4 | Episode: 1572 | Score: -6.45 | Avg Score: -89.14 |\n",
"| Experiment: 4 | Episode: 1573 | Score: -75.7 | Avg Score: -88.57 |\n",
"| Experiment: 4 | Episode: 1574 | Score: -158.51 | Avg Score: -89.45 |\n",
"| Experiment: 4 | Episode: 1575 | Score: -192.12 | Avg Score: -89.25 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 4 | Episode: 1576 | Score: -32.79 | Avg Score: -88.34 |\n",
"| Experiment: 4 | Episode: 1577 | Score: -18.32 | Avg Score: -84.48 |\n",
"| Experiment: 4 | Episode: 1578 | Score: -74.16 | Avg Score: -84.0 |\n",
"| Experiment: 4 | Episode: 1579 | Score: -203.59 | Avg Score: -83.57 |\n",
"| Experiment: 4 | Episode: 1580 | Score: -220.35 | Avg Score: -86.56 |\n",
"| Experiment: 4 | Episode: 1581 | Score: -48.85 | Avg Score: -86.87 |\n",
"| Experiment: 4 | Episode: 1582 | Score: -9.14 | Avg Score: -86.54 |\n",
"| Experiment: 4 | Episode: 1583 | Score: -84.38 | Avg Score: -86.67 |\n",
"| Experiment: 4 | Episode: 1584 | Score: -1.16 | Avg Score: -85.33 |\n",
"| Experiment: 4 | Episode: 1585 | Score: -187.82 | Avg Score: -86.87 |\n",
"| Experiment: 4 | Episode: 1586 | Score: -88.49 | Avg Score: -87.16 |\n",
"| Experiment: 4 | Episode: 1587 | Score: -42.66 | Avg Score: -87.2 |\n",
"| Experiment: 4 | Episode: 1588 | Score: -77.53 | Avg Score: -87.85 |\n",
"| Experiment: 4 | Episode: 1589 | Score: -13.56 | Avg Score: -87.52 |\n",
"| Experiment: 4 | Episode: 1590 | Score: -268.35 | Avg Score: -89.64 |\n",
"| Experiment: 4 | Episode: 1591 | Score: -90.98 | Avg Score: -90.2 |\n",
"| Experiment: 4 | Episode: 1592 | Score: -105.93 | Avg Score: -90.73 |\n",
"| Experiment: 4 | Episode: 1593 | Score: -15.88 | Avg Score: -89.44 |\n",
"| Experiment: 4 | Episode: 1594 | Score: -81.35 | Avg Score: -88.97 |\n",
"| Experiment: 4 | Episode: 1595 | Score: -23.33 | Avg Score: -88.38 |\n",
"| Experiment: 4 | Episode: 1596 | Score: -105.32 | Avg Score: -89.06 |\n",
"| Experiment: 4 | Episode: 1597 | Score: -54.02 | Avg Score: -89.26 |\n",
"| Experiment: 4 | Episode: 1598 | Score: -23.71 | Avg Score: -89.15 |\n",
"| Experiment: 4 | Episode: 1599 | Score: -100.24 | Avg Score: -89.44 |\n",
"| Experiment: 4 | Episode: 1600 | Score: -63.66 | Avg Score: -88.82 |\n",
"| Experiment: 4 | Episode: 1601 | Score: -35.63 | Avg Score: -88.5 |\n",
"| Experiment: 4 | Episode: 1602 | Score: -39.38 | Avg Score: -87.5 |\n",
"| Experiment: 4 | Episode: 1603 | Score: -97.94 | Avg Score: -87.89 |\n",
"| Experiment: 4 | Episode: 1604 | Score: 25.28 | Avg Score: -86.92 |\n",
"| Experiment: 4 | Episode: 1605 | Score: -77.62 | Avg Score: -87.14 |\n",
"| Experiment: 4 | Episode: 1606 | Score: -69.05 | Avg Score: -87.09 |\n",
"| Experiment: 4 | Episode: 1607 | Score: -36.38 | Avg Score: -86.41 |\n",
"| Experiment: 4 | Episode: 1608 | Score: -7.68 | Avg Score: -85.89 |\n",
"| Experiment: 4 | Episode: 1609 | Score: -53.35 | Avg Score: -84.68 |\n",
"| Experiment: 4 | Episode: 1610 | Score: -55.22 | Avg Score: -84.55 |\n",
"| Experiment: 4 | Episode: 1611 | Score: -100.88 | Avg Score: -86.4 |\n",
"| Experiment: 4 | Episode: 1612 | Score: -70.64 | Avg Score: -86.2 |\n",
"| Experiment: 4 | Episode: 1613 | Score: -59.86 | Avg Score: -85.27 |\n",
"| Experiment: 4 | Episode: 1614 | Score: -79.73 | Avg Score: -85.15 |\n",
"| Experiment: 4 | Episode: 1615 | Score: -38.22 | Avg Score: -85.8 |\n",
"| Experiment: 4 | Episode: 1616 | Score: -291.03 | Avg Score: -87.32 |\n",
"| Experiment: 4 | Episode: 1617 | Score: 7.9 | Avg Score: -86.52 |\n",
"| Experiment: 4 | Episode: 1618 | Score: -107.58 | Avg Score: -87.25 |\n",
"| Experiment: 4 | Episode: 1619 | Score: -133.95 | Avg Score: -86.64 |\n",
"| Experiment: 4 | Episode: 1620 | Score: -183.99 | Avg Score: -88.01 |\n",
"| Experiment: 4 | Episode: 1621 | Score: -17.81 | Avg Score: -86.83 |\n",
"| Experiment: 4 | Episode: 1622 | Score: -28.33 | Avg Score: -86.52 |\n",
"| Experiment: 4 | Episode: 1623 | Score: -105.76 | Avg Score: -87.01 |\n",
"| Experiment: 4 | Episode: 1624 | Score: 21.67 | Avg Score: -86.18 |\n",
"| Experiment: 4 | Episode: 1625 | Score: -34.01 | Avg Score: -85.99 |\n",
"| Experiment: 4 | Episode: 1626 | Score: -66.9 | Avg Score: -86.12 |\n",
"| Experiment: 4 | Episode: 1627 | Score: -92.39 | Avg Score: -86.61 |\n",
"| Experiment: 4 | Episode: 1628 | Score: -53.9 | Avg Score: -87.09 |\n",
"| Experiment: 4 | Episode: 1629 | Score: 34.46 | Avg Score: -85.19 |\n",
"| Experiment: 4 | Episode: 1630 | Score: -119.06 | Avg Score: -85.54 |\n",
"| Experiment: 4 | Episode: 1631 | Score: -54.77 | Avg Score: -85.99 |\n",
"| Experiment: 4 | Episode: 1632 | Score: -12.93 | Avg Score: -83.55 |\n",
"| Experiment: 4 | Episode: 1633 | Score: -36.5 | Avg Score: -83.43 |\n",
"| Experiment: 4 | Episode: 1634 | Score: -177.55 | Avg Score: -84.92 |\n",
"| Experiment: 4 | Episode: 1635 | Score: -107.23 | Avg Score: -84.88 |\n",
"| Experiment: 4 | Episode: 1636 | Score: -88.89 | Avg Score: -84.54 |\n",
"| Experiment: 4 | Episode: 1637 | Score: -145.68 | Avg Score: -85.37 |\n",
"| Experiment: 4 | Episode: 1638 | Score: -186.64 | Avg Score: -84.98 |\n",
"| Experiment: 4 | Episode: 1639 | Score: -325.36 | Avg Score: -88.43 |\n",
"| Experiment: 4 | Episode: 1640 | Score: -110.36 | Avg Score: -88.92 |\n",
"| Experiment: 4 | Episode: 1641 | Score: -73.09 | Avg Score: -88.83 |\n",
"| Experiment: 4 | Episode: 1642 | Score: -43.37 | Avg Score: -88.26 |\n",
"| Experiment: 4 | Episode: 1643 | Score: -15.9 | Avg Score: -87.9 |\n",
"| Experiment: 4 | Episode: 1644 | Score: -60.13 | Avg Score: -87.74 |\n",
"| Experiment: 4 | Episode: 1645 | Score: -19.4 | Avg Score: -87.34 |\n",
"| Experiment: 4 | Episode: 1646 | Score: -214.3 | Avg Score: -87.93 |\n",
"| Experiment: 4 | Episode: 1647 | Score: -74.95 | Avg Score: -87.67 |\n",
"| Experiment: 4 | Episode: 1648 | Score: -256.55 | Avg Score: -89.77 |\n",
"| Experiment: 4 | Episode: 1649 | Score: -146.68 | Avg Score: -89.17 |\n",
"| Experiment: 4 | Episode: 1650 | Score: -129.72 | Avg Score: -90.03 |\n",
"| Experiment: 4 | Episode: 1651 | Score: -81.96 | Avg Score: -89.35 |\n",
"| Experiment: 4 | Episode: 1652 | Score: -164.28 | Avg Score: -90.24 |\n",
"| Experiment: 4 | Episode: 1653 | Score: -27.99 | Avg Score: -90.25 |\n",
"| Experiment: 4 | Episode: 1654 | Score: -163.77 | Avg Score: -91.07 |\n",
"| Experiment: 4 | Episode: 1655 | Score: -110.06 | Avg Score: -91.95 |\n",
"| Experiment: 4 | Episode: 1656 | Score: -55.86 | Avg Score: -91.65 |\n",
"| Experiment: 4 | Episode: 1657 | Score: -151.13 | Avg Score: -92.32 |\n",
"| Experiment: 4 | Episode: 1658 | Score: -5.23 | Avg Score: -91.51 |\n",
"| Experiment: 4 | Episode: 1659 | Score: -21.94 | Avg Score: -90.71 |\n",
"| Experiment: 4 | Episode: 1660 | Score: -92.8 | Avg Score: -90.71 |\n",
"| Experiment: 4 | Episode: 1661 | Score: -160.95 | Avg Score: -91.23 |\n",
"| Experiment: 4 | Episode: 1662 | Score: -106.23 | Avg Score: -90.47 |\n",
"| Experiment: 4 | Episode: 1663 | Score: -36.06 | Avg Score: -89.44 |\n",
"| Experiment: 4 | Episode: 1664 | Score: -31.38 | Avg Score: -88.07 |\n",
"| Experiment: 4 | Episode: 1665 | Score: -30.51 | Avg Score: -87.12 |\n",
"| Experiment: 4 | Episode: 1666 | Score: -87.39 | Avg Score: -87.47 |\n",
"| Experiment: 4 | Episode: 1667 | Score: -55.18 | Avg Score: -86.67 |\n",
"| Experiment: 4 | Episode: 1668 | Score: -61.18 | Avg Score: -86.37 |\n",
"| Experiment: 4 | Episode: 1669 | Score: -15.39 | Avg Score: -85.52 |\n",
"| Experiment: 4 | Episode: 1670 | Score: -58.74 | Avg Score: -83.87 |\n",
"| Experiment: 4 | Episode: 1671 | Score: -1.76 | Avg Score: -82.35 |\n",
"| Experiment: 4 | Episode: 1672 | Score: -275.72 | Avg Score: -85.04 |\n",
"| Experiment: 4 | Episode: 1673 | Score: -21.86 | Avg Score: -84.51 |\n",
"| Experiment: 4 | Episode: 1674 | Score: -71.87 | Avg Score: -83.64 |\n",
"| Experiment: 4 | Episode: 1675 | Score: -22.88 | Avg Score: -81.95 |\n",
"| Experiment: 4 | Episode: 1676 | Score: -60.3 | Avg Score: -82.22 |\n",
"| Experiment: 4 | Episode: 1677 | Score: -13.34 | Avg Score: -82.17 |\n",
"| Experiment: 4 | Episode: 1678 | Score: -71.41 | Avg Score: -82.14 |\n",
"| Experiment: 4 | Episode: 1679 | Score: -118.03 | Avg Score: -81.29 |\n",
"| Experiment: 4 | Episode: 1680 | Score: -41.47 | Avg Score: -79.5 |\n",
"| Experiment: 4 | Episode: 1681 | Score: -94.61 | Avg Score: -79.96 |\n",
"| Experiment: 4 | Episode: 1682 | Score: -11.63 | Avg Score: -79.98 |\n",
"| Experiment: 4 | Episode: 1683 | Score: -53.71 | Avg Score: -79.68 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 4 | Episode: 1684 | Score: -28.19 | Avg Score: -79.95 |\n",
"| Experiment: 4 | Episode: 1685 | Score: -55.4 | Avg Score: -78.62 |\n",
"| Experiment: 4 | Episode: 1686 | Score: -53.63 | Avg Score: -78.27 |\n",
"| Experiment: 4 | Episode: 1687 | Score: -62.98 | Avg Score: -78.48 |\n",
"| Experiment: 4 | Episode: 1688 | Score: -123.0 | Avg Score: -78.93 |\n",
"| Experiment: 4 | Episode: 1689 | Score: -58.93 | Avg Score: -79.38 |\n",
"| Experiment: 4 | Episode: 1690 | Score: -92.11 | Avg Score: -77.62 |\n",
"| Experiment: 4 | Episode: 1691 | Score: -81.18 | Avg Score: -77.52 |\n",
"| Experiment: 4 | Episode: 1692 | Score: -142.78 | Avg Score: -77.89 |\n",
"| Experiment: 4 | Episode: 1693 | Score: -162.71 | Avg Score: -79.36 |\n",
"| Experiment: 4 | Episode: 1694 | Score: -89.59 | Avg Score: -79.44 |\n",
"| Experiment: 4 | Episode: 1695 | Score: -90.93 | Avg Score: -80.12 |\n",
"| Experiment: 4 | Episode: 1696 | Score: -45.64 | Avg Score: -79.52 |\n",
"| Experiment: 4 | Episode: 1697 | Score: -110.36 | Avg Score: -80.09 |\n",
"| Experiment: 4 | Episode: 1698 | Score: -239.18 | Avg Score: -82.24 |\n",
"| Experiment: 4 | Episode: 1699 | Score: -30.62 | Avg Score: -81.54 |\n",
"| Experiment: 4 | Episode: 1700 | Score: -49.26 | Avg Score: -81.4 |\n",
"| Experiment: 4 | Episode: 1701 | Score: -169.27 | Avg Score: -82.74 |\n",
"| Experiment: 4 | Episode: 1702 | Score: -95.18 | Avg Score: -83.3 |\n",
"| Experiment: 4 | Episode: 1703 | Score: -33.86 | Avg Score: -82.65 |\n",
"| Experiment: 4 | Episode: 1704 | Score: -19.35 | Avg Score: -83.1 |\n",
"| Experiment: 4 | Episode: 1705 | Score: -80.59 | Avg Score: -83.13 |\n",
"| Experiment: 4 | Episode: 1706 | Score: -55.73 | Avg Score: -83.0 |\n",
"| Experiment: 4 | Episode: 1707 | Score: -308.13 | Avg Score: -85.71 |\n",
"| Experiment: 4 | Episode: 1708 | Score: -41.96 | Avg Score: -86.06 |\n",
"| Experiment: 4 | Episode: 1709 | Score: -89.59 | Avg Score: -86.42 |\n",
"| Experiment: 4 | Episode: 1710 | Score: -50.81 | Avg Score: -86.38 |\n",
"| Experiment: 4 | Episode: 1711 | Score: -61.32 | Avg Score: -85.98 |\n",
"| Experiment: 4 | Episode: 1712 | Score: -74.11 | Avg Score: -86.02 |\n",
"| Experiment: 4 | Episode: 1713 | Score: -52.71 | Avg Score: -85.94 |\n",
"| Experiment: 4 | Episode: 1714 | Score: -56.72 | Avg Score: -85.71 |\n",
"| Experiment: 4 | Episode: 1715 | Score: -73.69 | Avg Score: -86.07 |\n",
"| Experiment: 4 | Episode: 1716 | Score: -54.51 | Avg Score: -83.7 |\n",
"| Experiment: 4 | Episode: 1717 | Score: -103.84 | Avg Score: -84.82 |\n",
"| Experiment: 4 | Episode: 1718 | Score: -98.76 | Avg Score: -84.73 |\n",
"| Experiment: 4 | Episode: 1719 | Score: -91.62 | Avg Score: -84.31 |\n",
"| Experiment: 4 | Episode: 1720 | Score: -98.46 | Avg Score: -83.45 |\n",
"| Experiment: 4 | Episode: 1721 | Score: -169.86 | Avg Score: -84.97 |\n",
"| Experiment: 4 | Episode: 1722 | Score: -48.93 | Avg Score: -85.18 |\n",
"| Experiment: 4 | Episode: 1723 | Score: -46.15 | Avg Score: -84.58 |\n",
"| Experiment: 4 | Episode: 1724 | Score: -94.57 | Avg Score: -85.75 |\n",
"| Experiment: 4 | Episode: 1725 | Score: -77.5 | Avg Score: -86.18 |\n",
"| Experiment: 4 | Episode: 1726 | Score: -65.9 | Avg Score: -86.17 |\n",
"| Experiment: 4 | Episode: 1727 | Score: -34.46 | Avg Score: -85.59 |\n",
"| Experiment: 4 | Episode: 1728 | Score: -114.77 | Avg Score: -86.2 |\n",
"| Experiment: 4 | Episode: 1729 | Score: -219.58 | Avg Score: -88.74 |\n",
"| Experiment: 4 | Episode: 1730 | Score: -195.72 | Avg Score: -89.51 |\n",
"| Experiment: 4 | Episode: 1731 | Score: -100.65 | Avg Score: -89.97 |\n",
"| Experiment: 4 | Episode: 1732 | Score: 2.35 | Avg Score: -89.81 |\n",
"| Experiment: 4 | Episode: 1733 | Score: 45.15 | Avg Score: -89.0 |\n",
"| Experiment: 4 | Episode: 1734 | Score: -271.56 | Avg Score: -89.94 |\n",
"| Experiment: 4 | Episode: 1735 | Score: 9.6 | Avg Score: -88.77 |\n",
"| Experiment: 4 | Episode: 1736 | Score: -39.71 | Avg Score: -88.28 |\n",
"| Experiment: 4 | Episode: 1737 | Score: -48.62 | Avg Score: -87.31 |\n",
"| Experiment: 4 | Episode: 1738 | Score: -247.67 | Avg Score: -87.92 |\n",
"| Experiment: 4 | Episode: 1739 | Score: -29.85 | Avg Score: -84.96 |\n",
"| Experiment: 4 | Episode: 1740 | Score: -46.04 | Avg Score: -84.32 |\n",
"| Experiment: 4 | Episode: 1741 | Score: -96.39 | Avg Score: -84.55 |\n",
"| Experiment: 4 | Episode: 1742 | Score: -92.2 | Avg Score: -85.04 |\n",
"| Experiment: 4 | Episode: 1743 | Score: -41.41 | Avg Score: -85.3 |\n",
"| Experiment: 4 | Episode: 1744 | Score: -59.19 | Avg Score: -85.29 |\n",
"| Experiment: 4 | Episode: 1745 | Score: -98.0 | Avg Score: -86.07 |\n",
"| Experiment: 4 | Episode: 1746 | Score: -83.43 | Avg Score: -84.76 |\n",
"| Experiment: 4 | Episode: 1747 | Score: -89.89 | Avg Score: -84.91 |\n",
"| Experiment: 4 | Episode: 1748 | Score: -104.86 | Avg Score: -83.4 |\n",
"| Experiment: 4 | Episode: 1749 | Score: -133.94 | Avg Score: -83.27 |\n",
"| Experiment: 4 | Episode: 1750 | Score: -75.1 | Avg Score: -82.72 |\n",
"| Experiment: 4 | Episode: 1751 | Score: -268.01 | Avg Score: -84.58 |\n",
"| Experiment: 4 | Episode: 1752 | Score: -89.45 | Avg Score: -83.83 |\n",
"| Experiment: 4 | Episode: 1753 | Score: -37.56 | Avg Score: -83.93 |\n",
"| Experiment: 4 | Episode: 1754 | Score: -48.84 | Avg Score: -82.78 |\n",
"| Experiment: 4 | Episode: 1755 | Score: -133.35 | Avg Score: -83.01 |\n",
"| Experiment: 4 | Episode: 1756 | Score: -40.84 | Avg Score: -82.86 |\n",
"| Experiment: 4 | Episode: 1757 | Score: -9.05 | Avg Score: -81.44 |\n",
"| Experiment: 4 | Episode: 1758 | Score: -90.51 | Avg Score: -82.3 |\n",
"| Experiment: 4 | Episode: 1759 | Score: -31.7 | Avg Score: -82.39 |\n",
"| Experiment: 4 | Episode: 1760 | Score: -58.36 | Avg Score: -82.05 |\n",
"| Experiment: 4 | Episode: 1761 | Score: -78.2 | Avg Score: -81.22 |\n",
"| Experiment: 4 | Episode: 1762 | Score: -35.77 | Avg Score: -80.52 |\n",
"| Experiment: 4 | Episode: 1763 | Score: -286.36 | Avg Score: -83.02 |\n",
"| Experiment: 4 | Episode: 1764 | Score: -61.6 | Avg Score: -83.32 |\n",
"| Experiment: 4 | Episode: 1765 | Score: -37.12 | Avg Score: -83.39 |\n",
"| Experiment: 4 | Episode: 1766 | Score: -189.7 | Avg Score: -84.41 |\n",
"| Experiment: 4 | Episode: 1767 | Score: -44.43 | Avg Score: -84.3 |\n",
"| Experiment: 4 | Episode: 1768 | Score: -180.37 | Avg Score: -85.49 |\n",
"| Experiment: 4 | Episode: 1769 | Score: -121.73 | Avg Score: -86.56 |\n",
"| Experiment: 4 | Episode: 1770 | Score: 16.31 | Avg Score: -85.81 |\n",
"| Experiment: 4 | Episode: 1771 | Score: 66.15 | Avg Score: -85.13 |\n",
"| Experiment: 4 | Episode: 1772 | Score: -91.54 | Avg Score: -83.29 |\n",
"| Experiment: 4 | Episode: 1773 | Score: -116.41 | Avg Score: -84.23 |\n",
"| Experiment: 4 | Episode: 1774 | Score: -328.22 | Avg Score: -86.8 |\n",
"| Experiment: 4 | Episode: 1775 | Score: -148.83 | Avg Score: -88.06 |\n",
"| Experiment: 4 | Episode: 1776 | Score: -179.84 | Avg Score: -89.25 |\n",
"| Experiment: 4 | Episode: 1777 | Score: 93.09 | Avg Score: -88.19 |\n",
"| Experiment: 4 | Episode: 1778 | Score: -273.34 | Avg Score: -90.21 |\n",
"| Experiment: 4 | Episode: 1779 | Score: -57.6 | Avg Score: -89.6 |\n",
"| Experiment: 4 | Episode: 1780 | Score: -44.61 | Avg Score: -89.63 |\n",
"| Experiment: 4 | Episode: 1781 | Score: -208.33 | Avg Score: -90.77 |\n",
"| Experiment: 4 | Episode: 1782 | Score: -4.1 | Avg Score: -90.69 |\n",
"| Experiment: 4 | Episode: 1783 | Score: -42.54 | Avg Score: -90.58 |\n",
"| Experiment: 4 | Episode: 1784 | Score: -23.47 | Avg Score: -90.54 |\n",
"| Experiment: 4 | Episode: 1785 | Score: 33.21 | Avg Score: -89.65 |\n",
"| Experiment: 4 | Episode: 1786 | Score: -52.73 | Avg Score: -89.64 |\n",
"| Experiment: 4 | Episode: 1787 | Score: -82.63 | Avg Score: -89.84 |\n",
"| Experiment: 4 | Episode: 1788 | Score: -52.4 | Avg Score: -89.13 |\n",
"| Experiment: 4 | Episode: 1789 | Score: -166.71 | Avg Score: -90.21 |\n",
"| Experiment: 4 | Episode: 1790 | Score: -35.55 | Avg Score: -89.64 |\n",
"| Experiment: 4 | Episode: 1791 | Score: -132.95 | Avg Score: -90.16 |\n",
"| Experiment: 4 | Episode: 1792 | Score: -55.66 | Avg Score: -89.29 |\n",
"| Experiment: 4 | Episode: 1793 | Score: -23.56 | Avg Score: -87.9 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 4 | Episode: 1794 | Score: -253.16 | Avg Score: -89.53 |\n",
"| Experiment: 4 | Episode: 1795 | Score: 13.1 | Avg Score: -88.49 |\n",
"| Experiment: 4 | Episode: 1796 | Score: -70.43 | Avg Score: -88.74 |\n",
"| Experiment: 4 | Episode: 1797 | Score: -103.22 | Avg Score: -88.67 |\n",
"| Experiment: 4 | Episode: 1798 | Score: -109.58 | Avg Score: -87.37 |\n",
"| Experiment: 4 | Episode: 1799 | Score: -64.06 | Avg Score: -87.71 |\n",
"| Experiment: 4 | Episode: 1800 | Score: -52.24 | Avg Score: -87.74 |\n",
"| Experiment: 4 | Episode: 1801 | Score: -240.12 | Avg Score: -88.45 |\n",
"| Experiment: 4 | Episode: 1802 | Score: -289.78 | Avg Score: -90.39 |\n",
"| Experiment: 4 | Episode: 1803 | Score: -15.61 | Avg Score: -90.21 |\n",
"| Experiment: 4 | Episode: 1804 | Score: -95.06 | Avg Score: -90.97 |\n",
"| Experiment: 4 | Episode: 1805 | Score: -64.72 | Avg Score: -90.81 |\n",
"| Experiment: 4 | Episode: 1806 | Score: -84.64 | Avg Score: -91.1 |\n",
"| Experiment: 4 | Episode: 1807 | Score: -72.36 | Avg Score: -88.74 |\n",
"| Experiment: 4 | Episode: 1808 | Score: -23.16 | Avg Score: -88.55 |\n",
"| Experiment: 4 | Episode: 1809 | Score: -42.17 | Avg Score: -88.08 |\n",
"| Experiment: 4 | Episode: 1810 | Score: -67.64 | Avg Score: -88.25 |\n",
"| Experiment: 4 | Episode: 1811 | Score: -54.04 | Avg Score: -88.17 |\n",
"| Experiment: 4 | Episode: 1812 | Score: -50.55 | Avg Score: -87.94 |\n",
"| Experiment: 4 | Episode: 1813 | Score: -38.54 | Avg Score: -87.8 |\n",
"| Experiment: 4 | Episode: 1814 | Score: -46.2 | Avg Score: -87.69 |\n",
"| Experiment: 4 | Episode: 1815 | Score: -70.84 | Avg Score: -87.66 |\n",
"| Experiment: 4 | Episode: 1816 | Score: -10.68 | Avg Score: -87.22 |\n",
"| Experiment: 4 | Episode: 1817 | Score: -4.51 | Avg Score: -86.23 |\n",
"| Experiment: 4 | Episode: 1818 | Score: -57.11 | Avg Score: -85.81 |\n",
"| Experiment: 4 | Episode: 1819 | Score: -73.63 | Avg Score: -85.63 |\n",
"| Experiment: 4 | Episode: 1820 | Score: -159.92 | Avg Score: -86.25 |\n",
"| Experiment: 4 | Episode: 1821 | Score: -5.6 | Avg Score: -84.61 |\n",
"| Experiment: 4 | Episode: 1822 | Score: 0.88 | Avg Score: -84.11 |\n",
"| Experiment: 4 | Episode: 1823 | Score: -117.96 | Avg Score: -84.83 |\n",
"| Experiment: 4 | Episode: 1824 | Score: -83.6 | Avg Score: -84.72 |\n",
"| Experiment: 4 | Episode: 1825 | Score: -100.99 | Avg Score: -84.95 |\n",
"| Experiment: 4 | Episode: 1826 | Score: -25.29 | Avg Score: -84.55 |\n",
"| Experiment: 4 | Episode: 1827 | Score: -11.28 | Avg Score: -84.31 |\n",
"| Experiment: 4 | Episode: 1828 | Score: -137.37 | Avg Score: -84.54 |\n",
"| Experiment: 4 | Episode: 1829 | Score: -34.44 | Avg Score: -82.69 |\n",
"| Experiment: 4 | Episode: 1830 | Score: -98.83 | Avg Score: -81.72 |\n",
"| Experiment: 4 | Episode: 1831 | Score: -19.72 | Avg Score: -80.91 |\n",
"| Experiment: 4 | Episode: 1832 | Score: -54.14 | Avg Score: -81.47 |\n",
"| Experiment: 4 | Episode: 1833 | Score: -11.92 | Avg Score: -82.05 |\n",
"| Experiment: 4 | Episode: 1834 | Score: -21.69 | Avg Score: -79.55 |\n",
"| Experiment: 4 | Episode: 1835 | Score: -13.2 | Avg Score: -79.78 |\n",
"| Experiment: 4 | Episode: 1836 | Score: -128.71 | Avg Score: -80.67 |\n",
"| Experiment: 4 | Episode: 1837 | Score: -53.9 | Avg Score: -80.72 |\n",
"| Experiment: 4 | Episode: 1838 | Score: -40.65 | Avg Score: -78.65 |\n",
"| Experiment: 4 | Episode: 1839 | Score: -67.49 | Avg Score: -79.02 |\n",
"| Experiment: 4 | Episode: 1840 | Score: -76.84 | Avg Score: -79.33 |\n",
"| Experiment: 4 | Episode: 1841 | Score: -383.75 | Avg Score: -82.21 |\n",
"| Experiment: 4 | Episode: 1842 | Score: -66.31 | Avg Score: -81.95 |\n",
"| Experiment: 4 | Episode: 1843 | Score: -250.49 | Avg Score: -84.04 |\n",
"| Experiment: 4 | Episode: 1844 | Score: -3.13 | Avg Score: -83.48 |\n",
"| Experiment: 4 | Episode: 1845 | Score: 0.53 | Avg Score: -82.49 |\n",
"| Experiment: 4 | Episode: 1846 | Score: 36.38 | Avg Score: -81.29 |\n",
"| Experiment: 4 | Episode: 1847 | Score: -53.04 | Avg Score: -80.93 |\n",
"| Experiment: 4 | Episode: 1848 | Score: -468.13 | Avg Score: -84.56 |\n",
"| Experiment: 4 | Episode: 1849 | Score: 13.78 | Avg Score: -83.08 |\n",
"| Experiment: 4 | Episode: 1850 | Score: -75.18 | Avg Score: -83.08 |\n",
"| Experiment: 4 | Episode: 1851 | Score: -179.59 | Avg Score: -82.2 |\n",
"| Experiment: 4 | Episode: 1852 | Score: -66.22 | Avg Score: -81.96 |\n",
"| Experiment: 4 | Episode: 1853 | Score: -77.97 | Avg Score: -82.37 |\n",
"| Experiment: 4 | Episode: 1854 | Score: -44.25 | Avg Score: -82.32 |\n",
"| Experiment: 4 | Episode: 1855 | Score: 15.98 | Avg Score: -80.83 |\n",
"| Experiment: 4 | Episode: 1856 | Score: -54.23 | Avg Score: -80.96 |\n",
"| Experiment: 4 | Episode: 1857 | Score: 11.81 | Avg Score: -80.75 |\n",
"| Experiment: 4 | Episode: 1858 | Score: -73.72 | Avg Score: -80.59 |\n",
"| Experiment: 4 | Episode: 1859 | Score: -32.45 | Avg Score: -80.59 |\n",
"| Experiment: 4 | Episode: 1860 | Score: -75.25 | Avg Score: -80.76 |\n",
"| Experiment: 4 | Episode: 1861 | Score: -35.62 | Avg Score: -80.34 |\n",
"| Experiment: 4 | Episode: 1862 | Score: -25.01 | Avg Score: -80.23 |\n",
"| Experiment: 4 | Episode: 1863 | Score: -67.09 | Avg Score: -78.04 |\n",
"| Experiment: 4 | Episode: 1864 | Score: -425.88 | Avg Score: -81.68 |\n",
"| Experiment: 4 | Episode: 1865 | Score: 46.7 | Avg Score: -80.84 |\n",
"| Experiment: 4 | Episode: 1866 | Score: -7.71 | Avg Score: -79.02 |\n",
"| Experiment: 4 | Episode: 1867 | Score: -64.93 | Avg Score: -79.23 |\n",
"| Experiment: 4 | Episode: 1868 | Score: 13.73 | Avg Score: -77.29 |\n",
"| Experiment: 4 | Episode: 1869 | Score: -80.25 | Avg Score: -76.87 |\n",
"| Experiment: 4 | Episode: 1870 | Score: -71.46 | Avg Score: -77.75 |\n",
"| Experiment: 4 | Episode: 1871 | Score: 7.05 | Avg Score: -78.34 |\n",
"| Experiment: 4 | Episode: 1872 | Score: -178.87 | Avg Score: -79.21 |\n",
"| Experiment: 4 | Episode: 1873 | Score: -22.88 | Avg Score: -78.28 |\n",
"| Experiment: 4 | Episode: 1874 | Score: -11.09 | Avg Score: -75.11 |\n",
"| Experiment: 4 | Episode: 1875 | Score: -59.81 | Avg Score: -74.22 |\n",
"| Experiment: 4 | Episode: 1876 | Score: -29.51 | Avg Score: -72.71 |\n",
"| Experiment: 4 | Episode: 1877 | Score: -164.77 | Avg Score: -75.29 |\n",
"| Experiment: 4 | Episode: 1878 | Score: -218.47 | Avg Score: -74.74 |\n",
"| Experiment: 4 | Episode: 1879 | Score: -257.59 | Avg Score: -76.74 |\n",
"| Experiment: 4 | Episode: 1880 | Score: -86.29 | Avg Score: -77.16 |\n",
"| Experiment: 4 | Episode: 1881 | Score: -41.94 | Avg Score: -75.5 |\n",
"| Experiment: 4 | Episode: 1882 | Score: -77.25 | Avg Score: -76.23 |\n",
"| Experiment: 4 | Episode: 1883 | Score: -48.4 | Avg Score: -76.29 |\n",
"| Experiment: 4 | Episode: 1884 | Score: -37.17 | Avg Score: -76.42 |\n",
"| Experiment: 4 | Episode: 1885 | Score: -118.81 | Avg Score: -77.94 |\n",
"| Experiment: 4 | Episode: 1886 | Score: -143.25 | Avg Score: -78.85 |\n",
"| Experiment: 4 | Episode: 1887 | Score: -41.68 | Avg Score: -78.44 |\n",
"| Experiment: 4 | Episode: 1888 | Score: -152.25 | Avg Score: -79.44 |\n",
"| Experiment: 4 | Episode: 1889 | Score: -61.9 | Avg Score: -78.39 |\n",
"| Experiment: 4 | Episode: 1890 | Score: 6.3 | Avg Score: -77.97 |\n",
"| Experiment: 4 | Episode: 1891 | Score: -81.21 | Avg Score: -77.45 |\n",
"| Experiment: 4 | Episode: 1892 | Score: -80.84 | Avg Score: -77.71 |\n",
"| Experiment: 4 | Episode: 1893 | Score: 76.23 | Avg Score: -76.71 |\n",
"| Experiment: 4 | Episode: 1894 | Score: -50.46 | Avg Score: -74.68 |\n",
"| Experiment: 4 | Episode: 1895 | Score: -2.15 | Avg Score: -74.83 |\n",
"| Experiment: 4 | Episode: 1896 | Score: -73.88 | Avg Score: -74.87 |\n",
"| Experiment: 4 | Episode: 1897 | Score: -67.89 | Avg Score: -74.51 |\n",
"| Experiment: 4 | Episode: 1898 | Score: -27.63 | Avg Score: -73.69 |\n",
"| Experiment: 4 | Episode: 1899 | Score: -0.15 | Avg Score: -73.06 |\n",
"| Experiment: 4 | Episode: 1900 | Score: -84.72 | Avg Score: -73.38 |\n",
"| Experiment: 4 | Episode: 1901 | Score: -23.05 | Avg Score: -71.21 |\n",
"| Experiment: 4 | Episode: 1902 | Score: -32.97 | Avg Score: -68.64 |\n",
"| Experiment: 4 | Episode: 1903 | Score: -109.99 | Avg Score: -69.59 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 4 | Episode: 1904 | Score: -29.91 | Avg Score: -68.93 |\n",
"| Experiment: 4 | Episode: 1905 | Score: -37.62 | Avg Score: -68.66 |\n",
"| Experiment: 4 | Episode: 1906 | Score: -149.76 | Avg Score: -69.31 |\n",
"| Experiment: 4 | Episode: 1907 | Score: -31.74 | Avg Score: -68.91 |\n",
"| Experiment: 4 | Episode: 1908 | Score: -9.09 | Avg Score: -68.77 |\n",
"| Experiment: 4 | Episode: 1909 | Score: -59.57 | Avg Score: -68.94 |\n",
"| Experiment: 4 | Episode: 1910 | Score: -53.97 | Avg Score: -68.8 |\n",
"| Experiment: 4 | Episode: 1911 | Score: -133.27 | Avg Score: -69.6 |\n",
"| Experiment: 4 | Episode: 1912 | Score: -38.5 | Avg Score: -69.48 |\n",
"| Experiment: 4 | Episode: 1913 | Score: -15.3 | Avg Score: -69.24 |\n",
"| Experiment: 4 | Episode: 1914 | Score: -49.31 | Avg Score: -69.28 |\n",
"| Experiment: 4 | Episode: 1915 | Score: -79.84 | Avg Score: -69.36 |\n",
"| Experiment: 4 | Episode: 1916 | Score: -24.51 | Avg Score: -69.5 |\n",
"| Experiment: 4 | Episode: 1917 | Score: -6.96 | Avg Score: -69.53 |\n",
"| Experiment: 4 | Episode: 1918 | Score: -30.25 | Avg Score: -69.26 |\n",
"| Experiment: 4 | Episode: 1919 | Score: -18.69 | Avg Score: -68.71 |\n",
"| Experiment: 4 | Episode: 1920 | Score: 12.72 | Avg Score: -66.98 |\n",
"| Experiment: 4 | Episode: 1921 | Score: -60.57 | Avg Score: -67.53 |\n",
"| Experiment: 4 | Episode: 1922 | Score: -7.8 | Avg Score: -67.62 |\n",
"| Experiment: 4 | Episode: 1923 | Score: -47.38 | Avg Score: -66.91 |\n",
"| Experiment: 4 | Episode: 1924 | Score: -38.88 | Avg Score: -66.47 |\n",
"| Experiment: 4 | Episode: 1925 | Score: -94.93 | Avg Score: -66.41 |\n",
"| Experiment: 4 | Episode: 1926 | Score: -75.25 | Avg Score: -66.91 |\n",
"| Experiment: 4 | Episode: 1927 | Score: -33.34 | Avg Score: -67.13 |\n",
"| Experiment: 4 | Episode: 1928 | Score: -9.65 | Avg Score: -65.85 |\n",
"| Experiment: 4 | Episode: 1929 | Score: -59.52 | Avg Score: -66.1 |\n",
"| Experiment: 4 | Episode: 1930 | Score: -9.8 | Avg Score: -65.21 |\n",
"| Experiment: 4 | Episode: 1931 | Score: -43.54 | Avg Score: -65.45 |\n",
"| Experiment: 4 | Episode: 1932 | Score: -23.62 | Avg Score: -65.14 |\n",
"| Experiment: 4 | Episode: 1933 | Score: 7.05 | Avg Score: -64.95 |\n",
"| Experiment: 4 | Episode: 1934 | Score: -167.29 | Avg Score: -66.41 |\n",
"| Experiment: 4 | Episode: 1935 | Score: -46.04 | Avg Score: -66.74 |\n",
"| Experiment: 4 | Episode: 1936 | Score: -3.4 | Avg Score: -65.48 |\n",
"| Experiment: 4 | Episode: 1937 | Score: -41.27 | Avg Score: -65.36 |\n",
"| Experiment: 4 | Episode: 1938 | Score: -53.98 | Avg Score: -65.49 |\n",
"| Experiment: 4 | Episode: 1939 | Score: -105.46 | Avg Score: -65.87 |\n",
"| Experiment: 4 | Episode: 1940 | Score: -112.48 | Avg Score: -66.23 |\n",
"| Experiment: 4 | Episode: 1941 | Score: -27.16 | Avg Score: -62.66 |\n",
"| Experiment: 4 | Episode: 1942 | Score: 76.28 | Avg Score: -61.24 |\n",
"| Experiment: 4 | Episode: 1943 | Score: -13.65 | Avg Score: -58.87 |\n",
"| Experiment: 4 | Episode: 1944 | Score: -109.0 | Avg Score: -59.93 |\n",
"| Experiment: 4 | Episode: 1945 | Score: -2.68 | Avg Score: -59.96 |\n",
"| Experiment: 4 | Episode: 1946 | Score: -54.71 | Avg Score: -60.87 |\n",
"| Experiment: 4 | Episode: 1947 | Score: -11.64 | Avg Score: -60.45 |\n",
"| Experiment: 4 | Episode: 1948 | Score: 47.46 | Avg Score: -55.3 |\n",
"| Experiment: 4 | Episode: 1949 | Score: -193.75 | Avg Score: -57.37 |\n",
"| Experiment: 4 | Episode: 1950 | Score: -77.61 | Avg Score: -57.4 |\n",
"| Experiment: 4 | Episode: 1951 | Score: -31.78 | Avg Score: -55.92 |\n",
"| Experiment: 4 | Episode: 1952 | Score: -66.77 | Avg Score: -55.93 |\n",
"| Experiment: 4 | Episode: 1953 | Score: -47.04 | Avg Score: -55.62 |\n",
"| Experiment: 4 | Episode: 1954 | Score: -24.97 | Avg Score: -55.42 |\n",
"| Experiment: 4 | Episode: 1955 | Score: -161.43 | Avg Score: -57.2 |\n",
"| Experiment: 4 | Episode: 1956 | Score: -37.62 | Avg Score: -57.03 |\n",
"| Experiment: 4 | Episode: 1957 | Score: -49.81 | Avg Score: -57.65 |\n",
"| Experiment: 4 | Episode: 1958 | Score: -40.5 | Avg Score: -57.32 |\n",
"| Experiment: 4 | Episode: 1959 | Score: -31.75 | Avg Score: -57.31 |\n",
"| Experiment: 4 | Episode: 1960 | Score: -30.59 | Avg Score: -56.86 |\n",
"| Experiment: 4 | Episode: 1961 | Score: -31.07 | Avg Score: -56.82 |\n",
"| Experiment: 4 | Episode: 1962 | Score: 18.33 | Avg Score: -56.38 |\n",
"| Experiment: 4 | Episode: 1963 | Score: -59.7 | Avg Score: -56.31 |\n",
"| Experiment: 4 | Episode: 1964 | Score: 82.76 | Avg Score: -51.22 |\n",
"| Experiment: 4 | Episode: 1965 | Score: -34.59 | Avg Score: -52.04 |\n",
"| Experiment: 4 | Episode: 1966 | Score: -171.73 | Avg Score: -53.68 |\n",
"| Experiment: 4 | Episode: 1967 | Score: 5.56 | Avg Score: -52.97 |\n",
"| Experiment: 4 | Episode: 1968 | Score: -90.7 | Avg Score: -54.02 |\n",
"| Experiment: 4 | Episode: 1969 | Score: -48.61 | Avg Score: -53.7 |\n",
"| Experiment: 4 | Episode: 1970 | Score: -93.92 | Avg Score: -53.92 |\n",
"| Experiment: 4 | Episode: 1971 | Score: 44.85 | Avg Score: -53.55 |\n",
"| Experiment: 4 | Episode: 1972 | Score: 2.22 | Avg Score: -51.73 |\n",
"| Experiment: 4 | Episode: 1973 | Score: -56.73 | Avg Score: -52.07 |\n",
"| Experiment: 4 | Episode: 1974 | Score: -7.62 | Avg Score: -52.04 |\n",
"| Experiment: 4 | Episode: 1975 | Score: -39.89 | Avg Score: -51.84 |\n",
"| Experiment: 4 | Episode: 1976 | Score: -85.52 | Avg Score: -52.4 |\n",
"| Experiment: 4 | Episode: 1977 | Score: -42.38 | Avg Score: -51.18 |\n",
"| Experiment: 4 | Episode: 1978 | Score: -13.27 | Avg Score: -49.12 |\n",
"| Experiment: 4 | Episode: 1979 | Score: 21.51 | Avg Score: -46.33 |\n",
"| Experiment: 4 | Episode: 1980 | Score: -101.44 | Avg Score: -46.48 |\n",
"| Experiment: 4 | Episode: 1981 | Score: -116.52 | Avg Score: -47.23 |\n",
"| Experiment: 4 | Episode: 1982 | Score: 83.54 | Avg Score: -45.62 |\n",
"| Experiment: 4 | Episode: 1983 | Score: -101.12 | Avg Score: -46.15 |\n",
"| Experiment: 4 | Episode: 1984 | Score: -6.92 | Avg Score: -45.85 |\n",
"| Experiment: 4 | Episode: 1985 | Score: 8.53 | Avg Score: -44.57 |\n",
"| Experiment: 4 | Episode: 1986 | Score: -142.86 | Avg Score: -44.57 |\n",
"| Experiment: 4 | Episode: 1987 | Score: 80.37 | Avg Score: -43.35 |\n",
"| Experiment: 4 | Episode: 1988 | Score: 14.56 | Avg Score: -41.68 |\n",
"| Experiment: 4 | Episode: 1989 | Score: -94.71 | Avg Score: -42.01 |\n",
"| Experiment: 4 | Episode: 1990 | Score: -16.01 | Avg Score: -42.23 |\n",
"| Experiment: 4 | Episode: 1991 | Score: -41.77 | Avg Score: -41.84 |\n",
"| Experiment: 4 | Episode: 1992 | Score: 83.71 | Avg Score: -40.19 |\n",
"| Experiment: 4 | Episode: 1993 | Score: 101.07 | Avg Score: -39.94 |\n",
"| Experiment: 4 | Episode: 1994 | Score: -62.18 | Avg Score: -40.06 |\n",
"| Experiment: 4 | Episode: 1995 | Score: -5.99 | Avg Score: -40.1 |\n",
"| Experiment: 4 | Episode: 1996 | Score: -142.98 | Avg Score: -40.79 |\n",
"| Experiment: 4 | Episode: 1997 | Score: -201.7 | Avg Score: -42.13 |\n",
"| Experiment: 4 | Episode: 1998 | Score: -163.9 | Avg Score: -43.49 |\n",
"| Experiment: 4 | Episode: 1999 | Score: -40.9 | Avg Score: -43.9 |\n"
]
}
],
"source": [
"# make the environment\n",
"env = gym.make(\n",
" \"LunarLander-v2\",\n",
" continuous=True,\n",
" gravity=-10.0,\n",
" enable_wind=False,\n",
" wind_power=0.0,\n",
" turbulence_power=0.)\n",
"\n",
"# set hyperparameters\n",
"N_EXPERIMENTS = 5\n",
"N_EPISODES = 2000\n",
"MAX_STEP = 300\n",
"GAMMA = 0.99\n",
"ACTION_DIM = env.action_space.shape[0]\n",
"OBSERVATION_DIM = env.observation_space.shape[0]\n",
"LEARNING_RATE = 1e-4\n",
"\n",
"# set empty lists for collecting scores\n",
"scores = []\n",
"avg_scores = []\n",
"std_scores = []\n",
"\n",
"# run the experiments\n",
"for ex_i in range(N_EXPERIMENTS):\n",
" scores.append([])\n",
" avg_scores.append([])\n",
" std_scores.append([])\n",
" agent = ReinforceAgent(observation_dim=OBSERVATION_DIM, \n",
" action_dim=ACTION_DIM, \n",
" gamma=GAMMA, \n",
" learning_rate=LEARNING_RATE)\n",
" \n",
" for ep_i in range(N_EPISODES):\n",
" observation, info = env.reset()\n",
" agent.reset()\n",
" done = False\n",
" score = 0\n",
" step = 0\n",
" \n",
" while not done:\n",
" step += 1\n",
" action, log_prob = agent.choose_action(observation)\n",
" observation_, reward, terminated, truncated, info = env.step(action)\n",
" score += reward\n",
" agent.store_transition(reward, log_prob)\n",
" observation = observation_\n",
" if terminated or truncated or step==MAX_STEP:\n",
" done = True\n",
" scores[ex_i].append(score)\n",
" avg_scores[ex_i].append(np.mean(scores[ex_i][-100:]))\n",
" std_scores[ex_i].append(np.std(scores[ex_i][-100:]))\n",
" agent.end_episode_computation()\n",
" agent.learn()\n",
" print(f\"| Experiment: {ex_i:3} | Episode: {ep_i:3} | Score: {np.round(score, 2):8.5} | Avg Score: {np.round(avg_scores[ex_i][-1], 2):8.5} |\")\n",
"env.close()\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 92,
"id": "11f0f0c5",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# save the random agent results for future comparison\n",
"results_data_dict[\"REINFORCE\"] = [avg_scores, std_scores]\n",
"plot_results(avg_scores, std_scores)"
]
},
{
"attachments": {
"REINFORCE_with_baseline_algorithm.png": {
"image/png": 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"
}
},
"cell_type": "markdown",
"id": "e74ba0aa",
"metadata": {},
"source": [
"## REINFORCE with baseline with continuous action space\n",
"Now we implement the REINFORCE with baseline algorithm as it was introduced in the Reinforcement Learning - An Introduction book (Chapter 13.3) in the case of a continuous action space (Chapter 13.7). The algorithm is specified in the figure below\n",
"\n",
"\n",
"So let's get started"
]
},
{
"cell_type": "markdown",
"id": "efd45acd",
"metadata": {},
"source": [
"### The Policy and Value Networks\n",
"Actually, the policy network is the same as for the REINFORCE algorithm case, and the value network is the same as for the REINFORCE algorithm for the discrete action space case, so we do not need to write anything new."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0a76e6bf",
"metadata": {},
"outputs": [],
"source": [
"class PolicyNetwork(nn.Module):\n",
" def __init__(self, observation_dim, action_dim, learning_rate):\n",
" super(PolicyNetwork, self).__init__()\n",
" self.fc1 = nn.Linear(in_features=observation_dim, out_features=256)\n",
" self.fc2 = nn.Linear(in_features=256, out_features=256)\n",
" self.mu = nn.Linear(in_features=256, out_features=action_dim)\n",
" self.sigma = nn.Linear(in_features=256, out_features=action_dim)\n",
" \n",
" self.optimizer = T.optim.Adam(params=self.parameters(), lr=learning_rate)\n",
" self.device = ('cuda:0' if T.cuda.is_available() else 'cpu')\n",
" self.to(self.device)\n",
" \n",
" def forward(self, x):\n",
" x = self.fc1(x)\n",
" x = F.relu(x)\n",
" x = self.fc2(x)\n",
" x = F.relu(x)\n",
" mu = self.mu(x)\n",
" sigma = self.sigma(x)\n",
" sigma = T.exp(sigma)\n",
" return mu, sigma\n",
" \n",
"class ValueNetwork(nn.Module):\n",
" def __init__(self, observation_dim, learning_rate):\n",
" super(ValueNetwork, self).__init__()\n",
" self.fc1 = nn.Linear(in_features=observation_dim, out_features=256)\n",
" self.fc2 = nn.Linear(in_features=256, out_features=256)\n",
" self.fc3 = nn.Linear(in_features=256, out_features=1)\n",
" \n",
" self.optimizer = T.optim.Adam(params=self.parameters(), lr=learning_rate)\n",
" self.device = (\"cuda:0\" if T.cuda.is_available() else \"cpu\")\n",
" self.to(self.device)\n",
" \n",
" def forward(self, x):\n",
" x = self.fc1(x)\n",
" x = F.relu(x)\n",
" x = self.fc2(x)\n",
" x = F.relu(x)\n",
" x = self.fc3(x)\n",
" return x"
]
},
{
"cell_type": "raw",
"id": "2d0d1fa3",
"metadata": {},
"source": [
"Now we write the agent class."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9f28d8bc",
"metadata": {},
"outputs": [],
"source": [
"class ReinforceWithBaselineAgent:\n",
" def __init__(self, observation_dim, action_dim, learning_rate, gamma):\n",
" self.lr = learning_rate\n",
" self.gamma = gamma\n",
" \n",
" # initialize policy network\n",
" self.policy = PolicyNetwork(observation_dim=observation_dim, \n",
" action_dim=action_dim, learning_rate=self.lr)\n",
" \n",
" # initialize the value network\n",
" self.value = ValueNetwork(observation_dim=observation_dim, learning_rate=self.lr)\n",
" \n",
" # lists for saving trajectories\n",
" self.rewards = []\n",
" self.log_probs = []\n",
" self.reverse_log_probs = []\n",
" self.returns = []\n",
" self.values = []\n",
" self.states = []\n",
" \n",
" def reset(self):\n",
" self.rewards = []\n",
" self.log_probs = []\n",
" self.reverse_log_probs = []\n",
" self.returns = []\n",
" self.values = []\n",
" self.states = []\n",
" \n",
" def choose_action(self, observation): \n",
" # transform to torch.Tensor and send to device\n",
" observation = T.Tensor(observation).float().to(self.policy.device)\n",
" \n",
" # get the distribution parameters \n",
" mu, sigma = self.policy(observation)\n",
" \n",
" # create the distribution\n",
" m = T.distributions.Normal(loc=mu, scale=sigma)\n",
" \n",
" # sample an action\n",
" action = m.sample()\n",
" return T.tanh(action).detach().numpy(), m.log_prob(action)\n",
" \n",
" \n",
" def store_transition(self, observation, reward, log_probs):\n",
" self.rewards.append(reward)\n",
" self.log_probs.append(log_probs)\n",
" self.states.append(observation)\n",
" \n",
" def end_episode_computation(self):\n",
" t = len(self.rewards)\n",
" G = 0\n",
" while t > 0:\n",
" t -= 1\n",
" \n",
" # compute the returns in reversed order\n",
" G = self.rewards[t] + self.gamma * G\n",
" self.returns.append(G)\n",
" state = T.tensor(self.states[t]).float().to(self.value.device)\n",
" self.values.append(self.value(state))\n",
" self.reverse_log_probs.append(self.log_probs[t])\n",
" \n",
" def learn(self):\n",
" # transform to torch.Tensor and send to device\n",
" log_probs = T.stack(self.reverse_log_probs).float().to(self.policy.device)\n",
" returns = T.Tensor(self.returns).float().unsqueeze(dim=1).to(self.policy.device)\n",
" values = T.stack(self.values).float().to(self.value.device)\n",
" \n",
" # compute delta\n",
" delta = returns - values\n",
" \n",
" # compute the value loss and backpropogate \n",
" self.value.optimizer.zero_grad()\n",
" value_loss = F.mse_loss(values, returns)\n",
" value_loss.backward()\n",
" self.value.optimizer.step()\n",
" \n",
" \n",
" # compute the policy loss and backpropogate\n",
" self.policy.optimizer.zero_grad()\n",
" loss = T.sum(-delta.detach() * log_probs).to(self.policy.device)\n",
" loss.backward()\n",
" self.policy.optimizer.step()"
]
},
{
"cell_type": "markdown",
"id": "0f58b8e5",
"metadata": {},
"source": [
"Now let us run a few experiments with REINFORCE with baseline"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "028e29fc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 0 | Score: -249.54 | Avg Score: -249.54 |\n",
"| Experiment: 0 | Episode: 1 | Score: -352.98 | Avg Score: -301.26 |\n",
"| Experiment: 0 | Episode: 2 | Score: -243.57 | Avg Score: -282.03 |\n",
"| Experiment: 0 | Episode: 3 | Score: -272.03 | Avg Score: -279.53 |\n",
"| Experiment: 0 | Episode: 4 | Score: -209.6 | Avg Score: -265.54 |\n",
"| Experiment: 0 | Episode: 5 | Score: -218.08 | Avg Score: -257.63 |\n",
"| Experiment: 0 | Episode: 6 | Score: -313.72 | Avg Score: -265.65 |\n",
"| Experiment: 0 | Episode: 7 | Score: -381.68 | Avg Score: -280.15 |\n",
"| Experiment: 0 | Episode: 8 | Score: -207.16 | Avg Score: -272.04 |\n",
"| Experiment: 0 | Episode: 9 | Score: 15.77 | Avg Score: -243.26 |\n",
"| Experiment: 0 | Episode: 10 | Score: -118.57 | Avg Score: -231.92 |\n",
"| Experiment: 0 | Episode: 11 | Score: -112.15 | Avg Score: -221.94 |\n",
"| Experiment: 0 | Episode: 12 | Score: -303.03 | Avg Score: -228.18 |\n",
"| Experiment: 0 | Episode: 13 | Score: -276.25 | Avg Score: -231.61 |\n",
"| Experiment: 0 | Episode: 14 | Score: -60.4 | Avg Score: -220.2 |\n",
"| Experiment: 0 | Episode: 15 | Score: -100.02 | Avg Score: -212.69 |\n",
"| Experiment: 0 | Episode: 16 | Score: -287.53 | Avg Score: -217.09 |\n",
"| Experiment: 0 | Episode: 17 | Score: -386.13 | Avg Score: -226.48 |\n",
"| Experiment: 0 | Episode: 18 | Score: -306.27 | Avg Score: -230.68 |\n",
"| Experiment: 0 | Episode: 19 | Score: -156.76 | Avg Score: -226.98 |\n",
"| Experiment: 0 | Episode: 20 | Score: -330.52 | Avg Score: -231.91 |\n",
"| Experiment: 0 | Episode: 21 | Score: -145.69 | Avg Score: -228.0 |\n",
"| Experiment: 0 | Episode: 22 | Score: -317.33 | Avg Score: -231.88 |\n",
"| Experiment: 0 | Episode: 23 | Score: -92.46 | Avg Score: -226.07 |\n",
"| Experiment: 0 | Episode: 24 | Score: -135.52 | Avg Score: -222.45 |\n",
"| Experiment: 0 | Episode: 25 | Score: -100.45 | Avg Score: -217.76 |\n",
"| Experiment: 0 | Episode: 26 | Score: -323.64 | Avg Score: -221.68 |\n",
"| Experiment: 0 | Episode: 27 | Score: -269.71 | Avg Score: -223.39 |\n",
"| Experiment: 0 | Episode: 28 | Score: -243.39 | Avg Score: -224.08 |\n",
"| Experiment: 0 | Episode: 29 | Score: -330.24 | Avg Score: -227.62 |\n",
"| Experiment: 0 | Episode: 30 | Score: -161.61 | Avg Score: -225.49 |\n",
"| Experiment: 0 | Episode: 31 | Score: -94.83 | Avg Score: -221.41 |\n",
"| Experiment: 0 | Episode: 32 | Score: -96.59 | Avg Score: -217.63 |\n",
"| Experiment: 0 | Episode: 33 | Score: -336.11 | Avg Score: -221.11 |\n",
"| Experiment: 0 | Episode: 34 | Score: -93.17 | Avg Score: -217.46 |\n",
"| Experiment: 0 | Episode: 35 | Score: -223.25 | Avg Score: -217.62 |\n",
"| Experiment: 0 | Episode: 36 | Score: -220.12 | Avg Score: -217.68 |\n",
"| Experiment: 0 | Episode: 37 | Score: -35.26 | Avg Score: -212.88 |\n",
"| Experiment: 0 | Episode: 38 | Score: -199.66 | Avg Score: -212.54 |\n",
"| Experiment: 0 | Episode: 39 | Score: -182.53 | Avg Score: -211.79 |\n",
"| Experiment: 0 | Episode: 40 | Score: -263.85 | Avg Score: -213.06 |\n",
"| Experiment: 0 | Episode: 41 | Score: -257.2 | Avg Score: -214.11 |\n",
"| Experiment: 0 | Episode: 42 | Score: -336.4 | Avg Score: -216.96 |\n",
"| Experiment: 0 | Episode: 43 | Score: -63.0 | Avg Score: -213.46 |\n",
"| Experiment: 0 | Episode: 44 | Score: -187.62 | Avg Score: -212.89 |\n",
"| Experiment: 0 | Episode: 45 | Score: -143.99 | Avg Score: -211.39 |\n",
"| Experiment: 0 | Episode: 46 | Score: -176.86 | Avg Score: -210.65 |\n",
"| Experiment: 0 | Episode: 47 | Score: -37.58 | Avg Score: -207.05 |\n",
"| Experiment: 0 | Episode: 48 | Score: -447.98 | Avg Score: -211.96 |\n",
"| Experiment: 0 | Episode: 49 | Score: -168.49 | Avg Score: -211.09 |\n",
"| Experiment: 0 | Episode: 50 | Score: -353.12 | Avg Score: -213.88 |\n",
"| Experiment: 0 | Episode: 51 | Score: -130.59 | Avg Score: -212.28 |\n",
"| Experiment: 0 | Episode: 52 | Score: -209.07 | Avg Score: -212.22 |\n",
"| Experiment: 0 | Episode: 53 | Score: -264.08 | Avg Score: -213.18 |\n",
"| Experiment: 0 | Episode: 54 | Score: -81.03 | Avg Score: -210.78 |\n",
"| Experiment: 0 | Episode: 55 | Score: -355.51 | Avg Score: -213.36 |\n",
"| Experiment: 0 | Episode: 56 | Score: -229.24 | Avg Score: -213.64 |\n",
"| Experiment: 0 | Episode: 57 | Score: -33.48 | Avg Score: -210.53 |\n",
"| Experiment: 0 | Episode: 58 | Score: -111.22 | Avg Score: -208.85 |\n",
"| Experiment: 0 | Episode: 59 | Score: -261.51 | Avg Score: -209.73 |\n",
"| Experiment: 0 | Episode: 60 | Score: -86.13 | Avg Score: -207.7 |\n",
"| Experiment: 0 | Episode: 61 | Score: -304.53 | Avg Score: -209.26 |\n",
"| Experiment: 0 | Episode: 62 | Score: -231.88 | Avg Score: -209.62 |\n",
"| Experiment: 0 | Episode: 63 | Score: 96.63 | Avg Score: -204.84 |\n",
"| Experiment: 0 | Episode: 64 | Score: -9.68 | Avg Score: -201.83 |\n",
"| Experiment: 0 | Episode: 65 | Score: -139.65 | Avg Score: -200.89 |\n",
"| Experiment: 0 | Episode: 66 | Score: -127.57 | Avg Score: -199.8 |\n",
"| Experiment: 0 | Episode: 67 | Score: -102.53 | Avg Score: -198.37 |\n",
"| Experiment: 0 | Episode: 68 | Score: -240.82 | Avg Score: -198.98 |\n",
"| Experiment: 0 | Episode: 69 | Score: -131.47 | Avg Score: -198.02 |\n",
"| Experiment: 0 | Episode: 70 | Score: -9.79 | Avg Score: -195.37 |\n",
"| Experiment: 0 | Episode: 71 | Score: -141.2 | Avg Score: -194.61 |\n",
"| Experiment: 0 | Episode: 72 | Score: -99.85 | Avg Score: -193.32 |\n",
"| Experiment: 0 | Episode: 73 | Score: -103.02 | Avg Score: -192.1 |\n",
"| Experiment: 0 | Episode: 74 | Score: -171.49 | Avg Score: -191.82 |\n",
"| Experiment: 0 | Episode: 75 | Score: -42.23 | Avg Score: -189.85 |\n",
"| Experiment: 0 | Episode: 76 | Score: -82.23 | Avg Score: -188.46 |\n",
"| Experiment: 0 | Episode: 77 | Score: -207.23 | Avg Score: -188.7 |\n",
"| Experiment: 0 | Episode: 78 | Score: -148.59 | Avg Score: -188.19 |\n",
"| Experiment: 0 | Episode: 79 | Score: -338.8 | Avg Score: -190.07 |\n",
"| Experiment: 0 | Episode: 80 | Score: -88.88 | Avg Score: -188.82 |\n",
"| Experiment: 0 | Episode: 81 | Score: -345.39 | Avg Score: -190.73 |\n",
"| Experiment: 0 | Episode: 82 | Score: -126.01 | Avg Score: -189.95 |\n",
"| Experiment: 0 | Episode: 83 | Score: -86.1 | Avg Score: -188.71 |\n",
"| Experiment: 0 | Episode: 84 | Score: -100.1 | Avg Score: -187.67 |\n",
"| Experiment: 0 | Episode: 85 | Score: -79.93 | Avg Score: -186.42 |\n",
"| Experiment: 0 | Episode: 86 | Score: -105.89 | Avg Score: -185.49 |\n",
"| Experiment: 0 | Episode: 87 | Score: -107.8 | Avg Score: -184.61 |\n",
"| Experiment: 0 | Episode: 88 | Score: -286.7 | Avg Score: -185.76 |\n",
"| Experiment: 0 | Episode: 89 | Score: -240.15 | Avg Score: -186.36 |\n",
"| Experiment: 0 | Episode: 90 | Score: -78.73 | Avg Score: -185.18 |\n",
"| Experiment: 0 | Episode: 91 | Score: -111.38 | Avg Score: -184.38 |\n",
"| Experiment: 0 | Episode: 92 | Score: -224.74 | Avg Score: -184.81 |\n",
"| Experiment: 0 | Episode: 93 | Score: -61.15 | Avg Score: -183.5 |\n",
"| Experiment: 0 | Episode: 94 | Score: -205.13 | Avg Score: -183.72 |\n",
"| Experiment: 0 | Episode: 95 | Score: -102.93 | Avg Score: -182.88 |\n",
"| Experiment: 0 | Episode: 96 | Score: -45.25 | Avg Score: -181.46 |\n",
"| Experiment: 0 | Episode: 97 | Score: -108.51 | Avg Score: -180.72 |\n",
"| Experiment: 0 | Episode: 98 | Score: -100.96 | Avg Score: -179.91 |\n",
"| Experiment: 0 | Episode: 99 | Score: -113.92 | Avg Score: -179.25 |\n",
"| Experiment: 0 | Episode: 100 | Score: -302.95 | Avg Score: -179.79 |\n",
"| Experiment: 0 | Episode: 101 | Score: -81.0 | Avg Score: -177.07 |\n",
"| Experiment: 0 | Episode: 102 | Score: -359.07 | Avg Score: -178.22 |\n",
"| Experiment: 0 | Episode: 103 | Score: -100.12 | Avg Score: -176.5 |\n",
"| Experiment: 0 | Episode: 104 | Score: -302.34 | Avg Score: -177.43 |\n",
"| Experiment: 0 | Episode: 105 | Score: -111.82 | Avg Score: -176.37 |\n",
"| Experiment: 0 | Episode: 106 | Score: -74.82 | Avg Score: -173.98 |\n",
"| Experiment: 0 | Episode: 107 | Score: -218.68 | Avg Score: -172.35 |\n",
"| Experiment: 0 | Episode: 108 | Score: -243.07 | Avg Score: -172.71 |\n",
"| Experiment: 0 | Episode: 109 | Score: -73.28 | Avg Score: -173.6 |\n",
"| Experiment: 0 | Episode: 110 | Score: -68.58 | Avg Score: -173.1 |\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"| Experiment: 0 | Episode: 111 | Score: -132.28 | Avg Score: -173.3 |\n",
"| Experiment: 0 | Episode: 112 | Score: -123.56 | Avg Score: -171.51 |\n",
"| Experiment: 0 | Episode: 113 | Score: -85.02 | Avg Score: -169.59 |\n",
"| Experiment: 0 | Episode: 114 | Score: -304.46 | Avg Score: -172.03 |\n",
"| Experiment: 0 | Episode: 115 | Score: -50.11 | Avg Score: -171.53 |\n",
"| Experiment: 0 | Episode: 116 | Score: -125.36 | Avg Score: -169.91 |\n",
"| Experiment: 0 | Episode: 117 | Score: -35.54 | Avg Score: -166.41 |\n",
"| Experiment: 0 | Episode: 118 | Score: -96.31 | Avg Score: -164.31 |\n",
"| Experiment: 0 | Episode: 119 | Score: -112.73 | Avg Score: -163.87 |\n",
"| Experiment: 0 | Episode: 120 | Score: -71.54 | Avg Score: -161.28 |\n",
"| Experiment: 0 | Episode: 121 | Score: -114.89 | Avg Score: -160.97 |\n",
"| Experiment: 0 | Episode: 122 | Score: -141.9 | Avg Score: -159.22 |\n",
"| Experiment: 0 | Episode: 123 | Score: -62.36 | Avg Score: -158.91 |\n",
"| Experiment: 0 | Episode: 124 | Score: -266.0 | Avg Score: -160.22 |\n",
"| Experiment: 0 | Episode: 125 | Score: -139.06 | Avg Score: -160.6 |\n",
"| Experiment: 0 | Episode: 126 | Score: -93.73 | Avg Score: -158.31 |\n",
"| Experiment: 0 | Episode: 127 | Score: -115.93 | Avg Score: -156.77 |\n",
"| Experiment: 0 | Episode: 128 | Score: -134.32 | Avg Score: -155.68 |\n",
"| Experiment: 0 | Episode: 129 | Score: -146.29 | Avg Score: -153.84 |\n",
"| Experiment: 0 | Episode: 130 | Score: -106.43 | Avg Score: -153.29 |\n",
"| Experiment: 0 | Episode: 131 | Score: -128.16 | Avg Score: -153.62 |\n",
"| Experiment: 0 | Episode: 132 | Score: -190.33 | Avg Score: -154.56 |\n",
"| Experiment: 0 | Episode: 133 | Score: -66.34 | Avg Score: -151.86 |\n",
"| Experiment: 0 | Episode: 134 | Score: -23.94 | Avg Score: -151.17 |\n",
"| Experiment: 0 | Episode: 135 | Score: -35.03 | Avg Score: -149.28 |\n",
"| Experiment: 0 | Episode: 136 | Score: -169.52 | Avg Score: -148.78 |\n",
"| Experiment: 0 | Episode: 137 | Score: -67.29 | Avg Score: -149.1 |\n",
"| Experiment: 0 | Episode: 138 | Score: -70.48 | Avg Score: -147.81 |\n",
"| Experiment: 0 | Episode: 139 | Score: -70.29 | Avg Score: -146.68 |\n",
"| Experiment: 0 | Episode: 140 | Score: -95.33 | Avg Score: -145.0 |\n",
"| Experiment: 0 | Episode: 141 | Score: -20.97 | Avg Score: -142.64 |\n",
"| Experiment: 0 | Episode: 142 | Score: -82.43 | Avg Score: -140.1 |\n",
"| Experiment: 0 | Episode: 143 | Score: -117.71 | Avg Score: -140.64 |\n",
"| Experiment: 0 | Episode: 144 | Score: -62.78 | Avg Score: -139.4 |\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[5], line 40\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m done:\n\u001b[1;32m 39\u001b[0m step \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m---> 40\u001b[0m action, log_prob \u001b[38;5;241m=\u001b[39m \u001b[43magent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchoose_action\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobservation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 41\u001b[0m observation_, reward, terminated, truncated, info \u001b[38;5;241m=\u001b[39m env\u001b[38;5;241m.\u001b[39mstep(action)\n\u001b[1;32m 42\u001b[0m score \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m reward\n",
"Cell \u001b[0;32mIn[3], line 41\u001b[0m, in \u001b[0;36mReinforceWithBaselineAgent.choose_action\u001b[0;34m(self, observation)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;66;03m# sample an action\u001b[39;00m\n\u001b[1;32m 40\u001b[0m action \u001b[38;5;241m=\u001b[39m m\u001b[38;5;241m.\u001b[39msample()\n\u001b[0;32m---> 41\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m T\u001b[38;5;241m.\u001b[39mtanh(action)\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mnumpy(), \u001b[43mm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/opt/miniconda3/envs/rl_algorithms/lib/python3.9/site-packages/torch/distributions/normal.py:79\u001b[0m, in \u001b[0;36mNormal.log_prob\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlog_prob\u001b[39m(\u001b[38;5;28mself\u001b[39m, value):\n\u001b[1;32m 78\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_args:\n\u001b[0;32m---> 79\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_sample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 80\u001b[0m \u001b[38;5;66;03m# compute the variance\u001b[39;00m\n\u001b[1;32m 81\u001b[0m var \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscale \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m)\n",
"File \u001b[0;32m~/opt/miniconda3/envs/rl_algorithms/lib/python3.9/site-packages/torch/distributions/distribution.py:299\u001b[0m, in \u001b[0;36mDistribution._validate_sample\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m support \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 298\u001b[0m valid \u001b[38;5;241m=\u001b[39m support\u001b[38;5;241m.\u001b[39mcheck(value)\n\u001b[0;32m--> 299\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m valid\u001b[38;5;241m.\u001b[39mall():\n\u001b[1;32m 300\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 301\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected value argument \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 302\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(value)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m of shape \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtuple\u001b[39m(value\u001b[38;5;241m.\u001b[39mshape)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut found invalid values:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 306\u001b[0m )\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"env = gym.make(\n",
" \"LunarLander-v2\",\n",
" continuous=True,\n",
" gravity=-10.0,\n",
" enable_wind=False,\n",
" wind_power=0.0,\n",
" turbulence_power=0.)\n",
"\n",
"N_EXPERIMENTS = 5\n",
"N_EPISODES = 2000\n",
"MAX_STEP = 300\n",
"GAMMA = 0.99\n",
"ACTION_DIM = env.action_space.shape[0]\n",
"OBSERVATION_DIM = env.observation_space.shape[0]\n",
"LEARNING_RATE = 5e-4\n",
"\n",
"scores = []\n",
"avg_scores = []\n",
"std_scores = []\n",
"\n",
"\n",
"for ex_i in range(N_EXPERIMENTS):\n",
" scores.append([])\n",
" avg_scores.append([])\n",
" std_scores.append([])\n",
" agent = ReinforceWithBaselineAgent(observation_dim=OBSERVATION_DIM, \n",
" action_dim=ACTION_DIM, \n",
" gamma=GAMMA, \n",
" learning_rate=LEARNING_RATE)\n",
" \n",
" for ep_i in range(N_EPISODES):\n",
" observation, info = env.reset()\n",
" agent.reset()\n",
" done = False\n",
" score = 0\n",
" step = 0\n",
" \n",
" while not done:\n",
" step += 1\n",
" action, log_prob = agent.choose_action(observation)\n",
" observation_, reward, terminated, truncated, info = env.step(action)\n",
" score += reward\n",
" agent.store_transition(observation, reward, log_prob)\n",
" observation = observation_\n",
" if terminated or truncated or step==MAX_STEP:\n",
" done = True\n",
" scores[ex_i].append(score)\n",
" avg_scores[ex_i].append(np.mean(scores[ex_i][-100:]))\n",
" std_scores[ex_i].append(np.std(scores[ex_i][-100:]))\n",
" agent.end_episode_computation()\n",
" agent.learn()\n",
" print(f\"| Experiment: {ex_i:3} | Episode: {ep_i:3} | Score: {np.round(score, 2):8.5} | Avg Score: {np.round(avg_scores[ex_i][-1], 2):8.5} |\")\n",
"env.close()\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 97,
"id": "bd8c620b",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# save the random agent results for future comparison\n",
"results_data_dict[\"REINFORCE with baseline\"] = [avg_scores, std_scores]\n",
"plot_results(avg_scores, std_scores)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"id": "8360b9fa",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"compare_results(results_data_dict)"
]
},
{
"cell_type": "markdown",
"id": "b3faaa7f",
"metadata": {},
"source": [
"## Conclusion\n",
"In the aforementioned notebook, we have seen implementations for the *REINFORCE* and *REINFORCE with baseline* algorithms for environments with continuous action spaces, as they were introduced in [1] (Chapters 13.3 and 13.4).\n",
"It is easy to see from the comparison of the two results plots, that *REINFORCE with baseline* reaches for batter results than the original *REINFORCE* algorithm, however, both behave very unstable in the training process and have high variance in terms of performance. This is a known problem of all the Monte-Carlo based RL algorithms. The next improvement would be to actually consider the value function in the estimation of the return, and switch to a Temporal Difference (TD) update method. In that case we will end up with the so-called *Actor-Critic* algorithm, where the Actor is given by the policy network, and the Critic is given by the value network."
]
},
{
"cell_type": "markdown",
"id": "7f8fe0d3",
"metadata": {},
"source": [
"## References\n",
"[1] Sutton & Barto, Reinforcement Learning An Introduction -Second edition (2018)"
]
},
{
"cell_type": "markdown",
"id": "2ee35859",
"metadata": {},
"source": [
"## Contact\n",
"If you find any errors or have any comments, corrections or questions about the material presented in the notebook, please do not hesitate to contact me at elkabetz.roy@gmail.com"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f5228e6f",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}