{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Minecraft Agent - Deep Reinforcement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Work realised in collaboration with:\n",
    "\n",
    "- [Clément Romac](https://github.com/ClementRomac)\n",
    "- [Pierre Leroy](https://github.com/PierreLeroyBdx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspired by the work of [Arthur Juliani](https://github.com/awjuliani/DeepRL-Agents) and the [Malmo Project](https://github.com/Microsoft/malmo).\n",
    "Thanks to [Gym Minecraft](https://github.com/tambetm/gym-minecraft) & [Tensorflow](https://www.tensorflow.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "import gym_minecraft\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import math\n",
    "import random\n",
    "import time\n",
    "import datetime\n",
    "import pickle\n",
    "from enum import Enum\n",
    "import matplotlib.pyplot as plt\n",
    "from lxml import etree\n",
    "import logging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Envrionnemental Settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The frame size is a useful variable, it allows us to decide which size our inputs are going to be.\n",
    "The values are very important, wider your frame will be and longer the training session is going to be, however you'll give more information to your network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph1 = tf.Graph()\n",
    "frame_size = [60,60]\n",
    "num_input = frame_size[0] * frame_size[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.uint8'>. Please provide explicit dtype.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "env = gym.make('MinecraftBasic-v0')  \n",
    "env.init(start_minecraft = True, \n",
    "         videoResolution = [frame_size[0], frame_size[1]], \n",
    "         allowDiscreteMovement = [\"move\", \"turn\"],\n",
    "         step_sleep = 0.25,\n",
    "         skip_steps = 0) #Movements modified to a faster convergence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Net Definition 1 (Convolutional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Convolutional():\n",
    "    def __init__(self):\n",
    "        self.x = tf.placeholder(\"float\", [None, num_input])\n",
    "\n",
    "        #Reshape the flatten data\n",
    "        self.input_layer = tf.reshape(self.x, [-1, frame_size[1], frame_size[0], 1])\n",
    "    \n",
    "        #Convolutional Layer 1\n",
    "        self.conv1 = tf.layers.conv2d(\n",
    "            inputs = self.input_layer,\n",
    "            filters = 32,\n",
    "            kernel_size = [6, 6],\n",
    "            strides=[2, 2],\n",
    "            padding = \"valid\",\n",
    "            activation = tf.nn.relu)\n",
    "        #Output size = 28\n",
    "\n",
    "        #Convolutional Layer 2\n",
    "        self.conv2 = tf.layers.conv2d(\n",
    "            inputs = self.conv1,\n",
    "            filters = 64,           \n",
    "            kernel_size = [6, 6],\n",
    "            strides = [2, 2],\n",
    "            padding = \"valid\",\n",
    "            activation = tf.nn.relu)\n",
    "        #Output size = 12\n",
    "\n",
    "        #Convolutional Layer 3\n",
    "        self.conv3 = tf.layers.conv2d(\n",
    "            inputs = self.conv2,\n",
    "            filters = 64,\n",
    "            kernel_size = [4, 4],\n",
    "            strides = [2, 2],\n",
    "            padding = \"valid\",\n",
    "            activation = tf.nn.relu)\n",
    "        #Output size = 5\n",
    "\n",
    "        #Flatten the data to pass it through the feed forward\n",
    "        self.dims = self.conv3.get_shape().as_list()\n",
    "        self.final_dimension = self.dims[1] * self.dims[2] * self.dims[3]\n",
    "        self.conv3_flat = tf.reshape(self.conv3, [-1, self.final_dimension])\n",
    "        \n",
    "        #Feed Forward\n",
    "        self.dense = tf.layers.dense(inputs = self.conv3_flat, units = 512, activation = tf.nn.relu)\n",
    "        \n",
    "        self.Qout = tf.layers.dense(inputs = self.dense, units = num_classes)\n",
    "        \n",
    "        #Indexes of the actions the network shall take\n",
    "        self.prediction = tf.argmax(self.Qout, 1)\n",
    "        \n",
    "        self.actions = tf.placeholder(shape = [None], dtype = tf.int32)\n",
    "        #Multiply our Q-values by a OneHotEncoding to only take the chosen ones.\n",
    "        self.actions_onehot = tf.one_hot(self.actions, num_classes, dtype = tf.float32)\n",
    "        #So that Q's going to be the Q-values calculated by the Target network\n",
    "        self.Q = tf.reduce_sum(tf.multiply(self.Qout, self.actions_onehot), axis = 1)\n",
    "\n",
    "        #NextQ corresponds to the Q estimated by the Bellman Equation\n",
    "        self.nextQ = tf.placeholder(shape = [None], dtype = tf.float32)\n",
    "                \n",
    "        #The loss value coresponds to the difference between the two different Q-values estimated\n",
    "        self.loss = tf.reduce_mean(tf.square(self.nextQ - self.Q))\n",
    "    \n",
    "        #Let's print the important informations\n",
    "        self.merged = tf.summary.merge([tf.summary.histogram(\"nextQ\", self.nextQ),\n",
    "                                        tf.summary.histogram(\"Q\", self.Q),\n",
    "                                        tf.summary.scalar(\"Loss\", self.loss)])\n",
    "        \n",
    "        self.learningRate = learningRate\n",
    "        #We would prefer the Adam Optimizer\n",
    "        self.trainer = tf.train.AdamOptimizer(learning_rate = self.learningRate)\n",
    "        self.updateModel = self.trainer.minimize(self.loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Net Definition 2 (Stacked Convolutional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "class StackedConvolutional():\n",
    "    def __init__(self, nb_stacked_images):\n",
    "        self.x = tf.placeholder(\"float\", [None, num_input*nb_stacked_images])\n",
    "        \n",
    "        self.original_input_layer = tf.reshape(self.x, [-1, nb_stacked_images, frame_size[1], frame_size[0]])\n",
    "        self.input_layer = tf.transpose(self.original_input_layer, [0, 2, 3, 1])\n",
    "\n",
    "        #Convolutional Layer 1\n",
    "        self.conv1 = tf.layers.conv2d(\n",
    "            inputs = self.input_layer,\n",
    "            filters = 32,\n",
    "            kernel_size = [6, 6],\n",
    "            strides=[2, 2],\n",
    "            padding = \"valid\",\n",
    "            activation = tf.nn.relu)\n",
    "        #Output size = 28\n",
    "\n",
    "        #Convolutional Layer 2\n",
    "        self.conv2 = tf.layers.conv2d(\n",
    "            inputs = self.conv1,\n",
    "            filters = 64,           \n",
    "            kernel_size = [6, 6],\n",
    "            strides = [2, 2],\n",
    "            padding = \"valid\",\n",
    "            activation = tf.nn.relu)\n",
    "        #Output size = 12\n",
    "\n",
    "        #Convolutional Layer 3\n",
    "        self.conv3 = tf.layers.conv2d(\n",
    "            inputs = self.conv2,\n",
    "            filters = 64,\n",
    "            kernel_size = [4, 4],\n",
    "            strides = [2, 2],\n",
    "            padding = \"valid\",\n",
    "            activation = tf.nn.relu)\n",
    "        #Output size = 5\n",
    "\n",
    "        #Flatten the data to pass it through the feed forward\n",
    "        self.dims = self.conv3.get_shape().as_list()\n",
    "        self.final_dimension = self.dims[1] * self.dims[2] * self.dims[3]\n",
    "        self.conv3_flat = tf.reshape(self.conv3, [-1, self.final_dimension])\n",
    "        \n",
    "        #Feed Forward\n",
    "        self.dense = tf.layers.dense(inputs = self.conv3_flat, units = 512, activation = tf.nn.relu)\n",
    "        \n",
    "        self.Qout = tf.layers.dense(inputs = self.dense, units = num_classes)\n",
    "        \n",
    "        #Indexes of the actions the network shall take\n",
    "        self.prediction = tf.argmax(self.Qout, 1)\n",
    "        \n",
    "        self.actions = tf.placeholder(shape = [None], dtype = tf.int32)\n",
    "        #Multiply our Q-values by a OneHotEncoding to only take the chosen ones.\n",
    "        self.actions_onehot = tf.one_hot(self.actions, num_classes, dtype = tf.float32)\n",
    "        #So that Q's going to be the Q-values chosen by the Target network\n",
    "        self.Q = tf.reduce_sum(tf.multiply(self.Qout, self.actions_onehot), axis = 1)\n",
    "\n",
    "        #NextQ corresponds to the Q estimated by the Bellman Equation\n",
    "        self.nextQ = tf.placeholder(shape = [None], dtype = tf.float32)\n",
    "                \n",
    "        #The loss value coresponds to the difference between the two different Q-values estimated\n",
    "        self.loss = tf.reduce_mean(tf.square(self.nextQ - self.Q))\n",
    "    \n",
    "        #Let's print the important informations\n",
    "        self.merged = tf.summary.merge([tf.summary.histogram(\"nextQ\", self.nextQ),\n",
    "                                        tf.summary.histogram(\"Q\", self.Q),\n",
    "                                        tf.summary.scalar(\"Loss\", self.loss)])\n",
    "        \n",
    "        self.learningRate = learningRate\n",
    "        #We would prefer the Adam Optimizer\n",
    "        self.trainer = tf.train.AdamOptimizer(learning_rate = self.learningRate)\n",
    "        self.updateModel = self.trainer.minimize(self.loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Net Definition 3 (Convolutional + LSTM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM():\n",
    "    def __init__(self, rnn_cell, scope):\n",
    "        self.x = tf.placeholder(\"float\", [None, num_input])\n",
    "        #Length of the frames' sequence \n",
    "        self.train_length = tf.placeholder(dtype=tf.int32)\n",
    "        self.batch_size = tf.placeholder(dtype=tf.int32,shape=[])\n",
    "\n",
    "        #Reshape the flatten data\n",
    "        self.input_layer = tf.reshape(self.x, [-1, frame_size[1], frame_size[0], 1])\n",
    "\n",
    "        #Convolutional Layer 1\n",
    "        self.conv1 = tf.layers.conv2d(\n",
    "            inputs = self.input_layer,\n",
    "            filters = 32,\n",
    "            kernel_size = [6, 6],\n",
    "            strides=[2, 2],\n",
    "            padding = \"valid\",\n",
    "            activation = tf.nn.relu)\n",
    "        #Output size = 28\n",
    "\n",
    "        #Convolutional Layer 2\n",
    "        self.conv2 = tf.layers.conv2d(\n",
    "            inputs = self.conv1,\n",
    "            filters = 64,           \n",
    "            kernel_size = [6, 6],\n",
    "            strides = [2, 2],\n",
    "            padding = \"valid\",\n",
    "            activation = tf.nn.relu)\n",
    "        #Output size = 12\n",
    "\n",
    "        #Convolutional Layer 3\n",
    "        self.conv3 = tf.layers.conv2d(\n",
    "            inputs = self.conv2,\n",
    "            filters = 64,\n",
    "            kernel_size = [4, 4],\n",
    "            strides = [2, 2],\n",
    "            padding = \"valid\",\n",
    "            activation = tf.nn.relu)\n",
    "        #Output size = 5\n",
    "\n",
    "        #Flatten the data before passing it through the recurrent layer\n",
    "        self.dims = self.conv3.get_shape().as_list()\n",
    "        self.final_dimension = self.dims[1] * self.dims[2] * self.dims[3]\n",
    "        self.conv3_flat = tf.reshape(self.conv3, [-1, self.final_dimension])\n",
    "        self.rnn_input = tf.reshape(self.conv3_flat, [self.batch_size, self.train_length, self.final_dimension])\n",
    "        \n",
    "        #Initialize the LSTM state\n",
    "        self.lstm_state_in = rnn_cell.zero_state(self.batch_size, tf.float32)\n",
    "        self.rnn,self.rnn_state = tf.nn.dynamic_rnn(\\\n",
    "                inputs=self.rnn_input, cell=rnn_cell, dtype=tf.float32, initial_state=self.lstm_state_in, scope=scope + \"_rnn\")\n",
    "        self.rnn = tf.reshape(self.rnn,shape=[-1,num_nodes])\n",
    "        \n",
    "        #Feed Forward\n",
    "        self.dense = tf.layers.dense(inputs = self.rnn, units = 512, activation = tf.nn.relu)\n",
    "        \n",
    "        self.Qout = tf.layers.dense(inputs = self.dense, units = num_classes)\n",
    "        \n",
    "        #Indexes of the actions the network shall take\n",
    "        self.prediction = tf.argmax(self.Qout, 1)\n",
    "        \n",
    "        self.actions = tf.placeholder(shape = [None], dtype = tf.int32)\n",
    "        #Multiply our actions values by a OneHotEncoding to only take the chosen ones.\n",
    "        self.actions_onehot = tf.one_hot(self.actions, num_classes, dtype = tf.float32)\n",
    "        #So that Q's going to be the Q-values chosen by the Target network\n",
    "        self.Q = tf.reduce_sum(tf.multiply(self.Qout, self.actions_onehot), axis = 1)\n",
    "\n",
    "        #NextQ corresponds to the Q estimated by the Bellman Equation\n",
    "        self.nextQ = tf.placeholder(shape = [None], dtype = tf.float32)\n",
    "                \n",
    "        #The loss value coresponds to the difference between the two different Q-values estimated\n",
    "        self.loss = tf.reduce_mean(tf.square(self.nextQ - self.Q))\n",
    "    \n",
    "        #Let's print the important informations\n",
    "        self.merged = tf.summary.merge([tf.summary.histogram(\"nextQ\", self.nextQ),\n",
    "                                        tf.summary.histogram(\"Q\", self.Q),\n",
    "                                        tf.summary.scalar(\"Loss\", self.loss)])\n",
    "        \n",
    "        self.learningRate = learningRate\n",
    "        #We would prefer the Adam Optimizer\n",
    "        self.trainer = tf.train.AdamOptimizer(learning_rate = self.learningRate)\n",
    "        self.updateModel = self.trainer.minimize(self.loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ChooseNetwork(Enum):\n",
    "    Convolutional = Convolutional\n",
    "    StackedConvolutional = StackedConvolutional\n",
    "    LSTM = LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TensorBoardInfosLogger():\n",
    "    def __init__(self):\n",
    "        self.percent_win = tf.placeholder(dtype=tf.float32)\n",
    "        self.mean_j_by_win = tf.placeholder(dtype=tf.float32)\n",
    "        self.mean_rewards_sum = tf.placeholder(dtype=tf.float32)\n",
    "        self.merged = tf.summary.merge([tf.summary.scalar(\"Percent_of_win_on_last_50_episodes\", self.percent_win), \n",
    "                                        tf.summary.scalar(\"Number_of_steps_by_win_on_last_50_episodes\", self.mean_j_by_win),\n",
    "                                        tf.summary.scalar(\"Mean_of_sum_of_rewards_on_last_50_episodes\", self.mean_rewards_sum), ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def processState(state):\n",
    "    gray_state = np.dot(state[...,:3], [0.299, 0.587, 0.114]) #Downscale input to greyscale\n",
    "    return np.reshape(gray_state, num_input)/255.0 #Normalize pixels\n",
    "\n",
    "def reverse_processState(state):\n",
    "    return np.reshape(state, (frame_size[0], frame_size[1]))*255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stacked_states(episode_frames, trace_length): #Fills the stacked frames with images full of zero if the sequence is too short\n",
    "    if len(episode_frames) < trace_length:\n",
    "        nb_missing_states = trace_length - len(episode_frames)\n",
    "        zeros_padded_states = [np.zeros(num_input) for _ in range(nb_missing_states)]\n",
    "        zeros_padded_states.extend(episode_frames)\n",
    "        return np.reshape(np.array(zeros_padded_states), num_input*trace_length)\n",
    "    else:\n",
    "        return np.reshape(np.array(episode_frames[-trace_length:]), num_input*trace_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Double QNetwork"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def updateTargetGraph(tfVars,tau):\n",
    "    total_vars = len(tfVars)\n",
    "    op_holder = []\n",
    "    for idx,var in enumerate(tfVars[0:total_vars//2]): #Get the weights of the original network\n",
    "        op_holder.append(tfVars[idx+total_vars//2].assign((var.value()*tau) + ((1-tau)*tfVars[idx+total_vars//2].value()))) #Update the Target Network weights\n",
    "    return op_holder\n",
    "\n",
    "def updateTarget(op_holder,sess):\n",
    "    for op in op_holder:\n",
    "        sess.run(op)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experience Replay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "class experience_buffer():\n",
    "    def __init__(self, buffer_size = 200000): #Stores steps\n",
    "        self.buffer = []\n",
    "        self.buffer_size = buffer_size\n",
    "\n",
    "    def add(self, experience):\n",
    "        if len(self.buffer) + len(experience) >= self.buffer_size:\n",
    "            self.buffer[0:(len(experience) + len(self.buffer)) - self.buffer_size] = []\n",
    "        self.buffer.extend(experience)\n",
    "\n",
    "    def sample(self, size):\n",
    "        return np.reshape(np.array(random.sample(self.buffer, size)), [size, 5])\n",
    "    \n",
    "    def get(self):\n",
    "        return np.reshape(np.array(self.buffer), [len(self.buffer), 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "class recurrent_experience_buffer():\n",
    "    def __init__(self, buffer_size = 5000): #Stores episodes\n",
    "        self.buffer = []\n",
    "        self.buffer_size = buffer_size\n",
    "    \n",
    "    def add(self,experience):\n",
    "        if len(self.buffer) + 1 >= self.buffer_size:\n",
    "            self.buffer[0:(1+len(self.buffer))-self.buffer_size] = []\n",
    "        self.buffer.append(experience)\n",
    "            \n",
    "    def sample(self, batch_size, trace_length):\n",
    "        tmp_buffer = [episode for episode in self.buffer if len(episode)+1>trace_length]\n",
    "        sampled_episodes = random.sample(tmp_buffer, batch_size)\n",
    "        sampledTraces = []\n",
    "        for episode in sampled_episodes:\n",
    "            point = np.random.randint(0,len(episode)+1-trace_length)\n",
    "            sampledTraces.append(episode[point:point+trace_length])\n",
    "        sampledTraces = np.array(sampledTraces)\n",
    "        return np.reshape(sampledTraces,[batch_size*trace_length,5])\n",
    "    \n",
    "    def get(self):\n",
    "        return np.reshape(np.array(self.buffer), [len(self.buffer), 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "class stacked_experience_buffer():\n",
    "    def __init__(self, buffer_size = 5000): #Stores episodes\n",
    "        self.buffer = []\n",
    "        self.buffer_size = buffer_size\n",
    "    \n",
    "    def add(self,experience):\n",
    "        if len(self.buffer) + 1 >= self.buffer_size:\n",
    "            self.buffer[0:(1+len(self.buffer))-self.buffer_size] = []\n",
    "        self.buffer.append(experience)\n",
    "            \n",
    "    def sample(self, batch_size, trace_length):\n",
    "        tmp_buffer = [episode for episode in self.buffer if len(episode)+1>trace_length]\n",
    "        sampled_episodes = random.sample(tmp_buffer, batch_size)\n",
    "        sampledTraces = []\n",
    "        for episode in sampled_episodes:\n",
    "            point = np.random.randint(0,len(episode)+1-trace_length)\n",
    "            stacked_states = []\n",
    "            stacked_next_states = []\n",
    "            for trace in episode[point:point+trace_length]:\n",
    "                stacked_states.extend(trace[0])\n",
    "                stacked_next_states.extend(trace[3])\n",
    "            trace_to_return = episode[point+trace_length-1].copy()\n",
    "            trace_to_return[0] = stacked_states\n",
    "            trace_to_return[3] = stacked_next_states\n",
    "            sampledTraces.append(trace_to_return)\n",
    "        sampledTraces = np.array(sampledTraces)\n",
    "        return np.reshape(sampledTraces,[batch_size, 5])\n",
    "    \n",
    "    def get(self):\n",
    "        return np.reshape(np.array(self.buffer), [len(self.buffer), 5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "NetType = ChooseNetwork.LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "learningRate = 0.0001\n",
    "num_nodes = 256\n",
    "num_classes = len(env.action_names[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size =  32 #How many steps to use for each training step.\n",
    "trace_length = 4 #How long each experience trace will be when training\n",
    "if NetType == ChooseNetwork.LSTM:\n",
    "    myBuffer = recurrent_experience_buffer()\n",
    "    batch_size = int(batch_size / trace_length)\n",
    "elif NetType == ChooseNetwork.StackedConvolutional:\n",
    "    myBuffer = stacked_experience_buffer()\n",
    "else:\n",
    "    myBuffer = experience_buffer()\n",
    "    \n",
    "update_freq = 4 #How often to perform a training step.\n",
    "num_episodes = 100000 #How many episodes of game environment to train network with\n",
    "total_steps = 0\n",
    "rList = [] #List of our rewards gained by game\n",
    "jList = [] #Number of moves realised by game\n",
    "j_by_loss = [] #Number of moves before resulting with a death of the agent\n",
    "j_by_win = [] #Number of moves before resulting with a win of the agent\n",
    "j_by_nothing = [] #This list's going to be used to count how many times the agent moves until the limit of moves is reached\n",
    "y = .95 #Discount factor on the target Q-values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploration Settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An exploration step is realized before any exploitation step during the training which allows us to obtain a set of data to work with.\n",
    "\n",
    "This exploration step reduces as the number of training increases, making the exploitation majority step by step (e-greedy)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_train_steps = 10000 #How many episodes of random actions before training begins.\n",
    "startE = 1 #Starting chance of random action\n",
    "endE = 0.1 #Final chance of random action\n",
    "annealing_steps = 200000 #How many epsiodes of training to reduce startE towards endE.\n",
    "e = startE\n",
    "stepDrop = (startE - endE) / annealing_steps\n",
    "nb_win = 0\n",
    "nb_win_tb = 0\n",
    "nb_nothing = 0\n",
    "nb_loss = 0\n",
    "tau = 0.001\n",
    "load_model = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating an explicit and unique title for Tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = str(time.time()).replace(\".\",\"\")\n",
    "net = str(NetType).split(\".\")[1]\n",
    "bs = \"BatchSize-\" + str(batch_size)\n",
    "strlr = \"lr-\" + str(learningRate)\n",
    "rand_step = \"RandStep-\" + str(pre_train_steps)\n",
    "nb_to_reduce_e = \"ReducE-\" + str(annealing_steps)\n",
    "write_path = \"train/\" + net + \"_\" + bs + \"_\" + strlr + \"_\" + rand_step + \"_\" + nb_to_reduce_e + \"_\" + date[-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Debugging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_debug = False\n",
    "if is_debug:\n",
    "    write_path = 'train/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_debug_states(tf_session, QNet, raw_input, trace_length):\n",
    "    tmp = tf_session.run(QNet.input_layer, feed_dict={QNet.x:[raw_input]})\n",
    "    for depth in range(tmp.shape[3]):\n",
    "        print(\"## Input image n°\" + str(depth) + \" ##\")\n",
    "        plt.imshow(tmp[0, :, :, depth], cmap=plt.get_cmap('gray'))\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-23-302bc8821a37>:8: BasicLSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is deprecated, please use tf.nn.rnn_cell.LSTMCell, which supports all the feature this cell currently has. Please replace the existing code with tf.nn.rnn_cell.LSTMCell(name='basic_lstm_cell').\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\user\\anaconda3\\envs\\minecraftrl\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2957: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "c:\\users\\user\\anaconda3\\envs\\minecraftrl\\lib\\site-packages\\numpy\\core\\_methods.py:80: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#######################################\n",
      "% Win : 0.0%\n",
      "% Nothing : 0.2%\n",
      "% Loss : 0.0%\n",
      "Nb J before win: nan\n",
      "Nb J before die: nan\n",
      "Total Steps: 40\n",
      "I: 0\n",
      "Epsilon:  1\n",
      "#### LAST 5 MOVES of LAST EPISODE  ####\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Buffer Move 35 : move -1\n",
      "- Move Array 35 : move -1\n",
      "ZPos : 2.5\n",
      "XPos : 5.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0          1          0          0\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Buffer Move 36 : turn 1\n",
      "- Move Array 36 : turn 1\n",
      "ZPos : 2.5\n",
      "XPos : 5.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0          0          1          0\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Buffer Move 37 : turn -1\n",
      "- Move Array 37 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 5.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0          0          0          1\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Buffer Move 38 : turn -1\n",
      "- Move Array 38 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 5.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0          0          0          1\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Buffer Move 39 : move 1\n",
      "- Move Array 39 : move 1\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         1          0          0          0\n",
      "Obtained reward : -1.0099999904632568\n",
      "State error : State did not changed though the action was turn 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move -1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move -1\n",
      "State error : State did not changed though the action was move -1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move -1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move -1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move -1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move -1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move -1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move -1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move -1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move -1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move -1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-302bc8821a37>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     97\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m             \u001b[1;31m#Get new state and reward from environment\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 99\u001b[1;33m             \u001b[0ms1_raw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex_action_predicted\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0minfo\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"observation\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\user\\anaconda3\\envs\\minecraftrl\\lib\\site-packages\\gym_minecraft-0.0.2-py3.6.egg\\gym_minecraft\\envs\\minecraft_env.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, action)\u001b[0m\n\u001b[0;32m    322\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_take_action\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    323\u001b[0m         \u001b[1;31m# wait for the new state\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 324\u001b[1;33m         \u001b[0mworld_state\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_world_state\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    325\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    326\u001b[0m         \u001b[1;31m# log errors and control messages\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\user\\anaconda3\\envs\\minecraftrl\\lib\\site-packages\\gym_minecraft-0.0.2-py3.6.egg\\gym_minecraft\\envs\\minecraft_env.py\u001b[0m in \u001b[0;36m_get_world_state\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    282\u001b[0m         \u001b[1;31m# wait till we have got at least one observation or mission has ended\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    283\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 284\u001b[1;33m             \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep_sleep\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# wait for 1ms to not consume entire CPU\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    285\u001b[0m             \u001b[0mworld_state\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magent_host\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpeekWorldState\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    286\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mworld_state\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumber_of_observations_since_last_state\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mskip_steps\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mworld_state\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_mission_running\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "with tf.Session() as sess:\n",
    "    \n",
    "    #Use a Double Q-Network\n",
    "    if NetType == ChooseNetwork.LSTM:\n",
    "        cell = tf.contrib.rnn.BasicLSTMCell(num_units = num_nodes, state_is_tuple = True)\n",
    "        cellT = tf.contrib.rnn.BasicLSTMCell(num_units = num_nodes, state_is_tuple = True)\n",
    "        \n",
    "        mainQN = NetType.value(cell, 'main')\n",
    "        targetQN = NetType.value(cellT, 'target')\n",
    "    elif NetType == ChooseNetwork.StackedConvolutional:\n",
    "        mainQN = NetType.value(trace_length)\n",
    "        targetQN = NetType.value(trace_length)\n",
    "    else:\n",
    "        mainQN = NetType.value()\n",
    "        targetQN = NetType.value()\n",
    "    \n",
    "    trainables = tf.trainable_variables()\n",
    "    targetOps = updateTargetGraph(trainables,tau)\n",
    "    \n",
    "    #Save the network\n",
    "    saver = tf.train.Saver()\n",
    "    path_to_save = \"saves/\" + str(datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")) + \"/\"\n",
    "    \n",
    "    init = tf.global_variables_initializer()\n",
    "    sess.run(init)\n",
    "    \n",
    "    tb_infos_logger = TensorBoardInfosLogger()\n",
    "    writer = tf.summary.FileWriter(write_path)\n",
    "    \n",
    "    if load_model == True:\n",
    "        print('Loading Model...')\n",
    "        ckpt = tf.train.get_checkpoint_state(path_to_save)\n",
    "        saver.restore(sess,ckpt.model_checkpoint_path)\n",
    "    \n",
    "    for i in range(num_episodes):\n",
    "        if NetType == ChooseNetwork.LSTM:\n",
    "            lstm_state = (np.zeros([1, num_nodes]), np.zeros([1, num_nodes])) #Reset the recurrent layer's hidden state\n",
    "            \n",
    "        episodeBuffer = experience_buffer()\n",
    "\n",
    "        s = env.reset()\n",
    "        s = processState(s)\n",
    "        d = False\n",
    "        j = 0\n",
    "        episode_frames = []\n",
    "        episode_frames.append(s)\n",
    "        episode_qvalues = []\n",
    "        episode_rewards = []\n",
    "        \n",
    "        ZPos = []\n",
    "        XPos = []\n",
    "        Yaw = []\n",
    "        moves = []\n",
    "        \n",
    "        while not d:\n",
    "            j += 1\n",
    "            \n",
    "            ### Epsilon Greedy ###\n",
    "            if total_steps > pre_train_steps:\n",
    "                if e > endE:\n",
    "                    e -= stepDrop\n",
    "                \n",
    "            # Make full exploration before the number of pre-train episodes then play with an e chance of random action during the training (e-greedy)\n",
    "            if(np.random.rand(1) < e or total_steps < pre_train_steps):\n",
    "                if NetType == ChooseNetwork.LSTM:\n",
    "                    lstm_state1 = sess.run(mainQN.rnn_state,\\\n",
    "                        feed_dict = {mainQN.x:[s], mainQN.train_length:1, \\\n",
    "                                     mainQN.lstm_state_in:lstm_state, mainQN.batch_size:1})\n",
    "                index_action_predicted = env.action_space.sample()\n",
    "                episode_qvalues.append([1 if i == index_action_predicted else 0 for i in range(len(env.action_names[0]))])\n",
    "            else:\n",
    "                if NetType == ChooseNetwork.LSTM:\n",
    "                    if is_debug:\n",
    "                        print_debug_states(sess, mainQN, s, trace_length)\n",
    "                        \n",
    "                    prediction, qvalues, lstm_state1 = sess.run([mainQN.prediction, mainQN.Qout, mainQN.rnn_state],\\\n",
    "                        feed_dict={mainQN.x:[s], mainQN.train_length:1, \\\n",
    "                                   mainQN.lstm_state_in:lstm_state, mainQN.batch_size:1})\n",
    "                elif NetType == ChooseNetwork.StackedConvolutional:\n",
    "                    stacked_s = get_stacked_states(episode_frames, trace_length)\n",
    "                    \n",
    "                    if is_debug:\n",
    "                        print_debug_states(sess, mainQN, stacked_s, trace_length)\n",
    "                            \n",
    "                    prediction, qvalues = sess.run([mainQN.prediction, mainQN.Qout],\\\n",
    "                        feed_dict={mainQN.x:[stacked_s]})\n",
    "                else:\n",
    "                    if is_debug:\n",
    "                        print_debug_states(sess, mainQN, s, trace_length)\n",
    "                        \n",
    "                    prediction, qvalues = sess.run([mainQN.prediction, mainQN.Qout], \\\n",
    "                                                      feed_dict = {mainQN.x:[s]})\n",
    "                index_action_predicted = prediction[0]\n",
    "                episode_qvalues.append(qvalues[0])\n",
    "            \n",
    "            #Get new state and reward from environment\n",
    "            s1_raw, r, d, info = env.step(index_action_predicted)\n",
    "                \n",
    "            if info[\"observation\"]:\n",
    "                ZPos.append(info['observation']['ZPos'])\n",
    "                XPos.append(info['observation']['XPos'])\n",
    "                Yaw.append(info['observation']['Yaw'])\n",
    "            s1 = processState(s1_raw)\n",
    "            moves.append(index_action_predicted)\n",
    "            episodeBuffer.add(np.reshape(np.array([s, index_action_predicted, r, s1, d]), [1, 5]))\n",
    "            episode_frames.append(s1)\n",
    "            \n",
    "            if total_steps > pre_train_steps:\n",
    "                if total_steps % (update_freq) == 0:\n",
    "                    \n",
    "                    updateTarget(targetOps,sess) #Update Target Network\n",
    "                    \n",
    "                    if NetType == ChooseNetwork.LSTM:\n",
    "                        lstm_state_train = (np.zeros([batch_size, num_nodes]), \\\n",
    "                                            np.zeros([batch_size, num_nodes]))\n",
    "                        \n",
    "                        trainBatch = myBuffer.sample(batch_size, trace_length)\n",
    "                        \n",
    "                        if is_debug:\n",
    "                            print_debug_states(sess, mainQN, trainBatch[0, 0], trace_length)\n",
    "                        \n",
    "                        #Estimate the action to choose by our first network\n",
    "                        actionChosen = sess.run(mainQN.prediction, \\\n",
    "                                                feed_dict = {mainQN.x:np.vstack(trainBatch[:, 3]), \\\n",
    "                                                             mainQN.train_length:trace_length, \\\n",
    "                                                             mainQN.lstm_state_in:lstm_state_train, \\\n",
    "                                                             mainQN.batch_size:batch_size})\n",
    "                        #Estimate all the Q-values by our second network --> Double\n",
    "                        allQValues = sess.run(targetQN.Qout, \\\n",
    "                                              feed_dict = {targetQN.x:np.vstack(trainBatch[:, 3]), \\\n",
    "                                                           targetQN.train_length:trace_length, \\\n",
    "                                                           targetQN.lstm_state_in:lstm_state_train, \\\n",
    "                                                           targetQN.batch_size:batch_size})\n",
    "\n",
    "                        #Train our network using target and predicted Q-values\n",
    "                        end_multiplier = -(trainBatch[:, 4] -1)\n",
    "                        maxQ = allQValues[range(batch_size*trace_length), actionChosen]\n",
    "                        #Bellman Equation\n",
    "                        targetQ = trainBatch[:, 2] + (y * maxQ * end_multiplier)\n",
    "\n",
    "                        _, summaryPlot = sess.run([mainQN.updateModel, mainQN.merged], \\\n",
    "                                                  feed_dict = {mainQN.x:np.vstack(trainBatch[:, 0]), \\\n",
    "                                                               mainQN.nextQ:targetQ, \\\n",
    "                                                               mainQN.actions:trainBatch[:, 1], \\\n",
    "                                                               mainQN.train_length:trace_length, \\\n",
    "                                                               mainQN.lstm_state_in:lstm_state_train, \\\n",
    "                                                               mainQN.batch_size:batch_size})\n",
    "                        \n",
    "                    elif NetType == ChooseNetwork.StackedConvolutional:\n",
    "                        trainBatch = myBuffer.sample(batch_size, trace_length)\n",
    "                        \n",
    "                        if is_debug:\n",
    "                            print_debug_states(sess, mainQN, trainBatch[0, 0], trace_length)\n",
    "                        \n",
    "                        #Estimate the action to choose by our first network\n",
    "                        actionChosen = sess.run(mainQN.prediction, \\\n",
    "                                                feed_dict = {mainQN.x:np.vstack(trainBatch[:, 3])})\n",
    "                        #Estimate all the Q-values by our second network --> Double\n",
    "                        allQValues = sess.run(targetQN.Qout, \\\n",
    "                                              feed_dict = {targetQN.x:np.vstack(trainBatch[:, 3])})\n",
    "\n",
    "                        #Train our network using target and predicted Q-values\n",
    "                        end_multiplier = -(trainBatch[:, 4] -1)\n",
    "                        maxQ = allQValues[range(batch_size), actionChosen]\n",
    "                        #Bellman Equation\n",
    "                        targetQ = trainBatch[:, 2] + (y * maxQ * end_multiplier)\n",
    "\n",
    "                        _, summaryPlot = sess.run([mainQN.updateModel, mainQN.merged], \\\n",
    "                                                  feed_dict = {mainQN.x:np.vstack(trainBatch[:, 0]), \\\n",
    "                                                               mainQN.nextQ:targetQ, \\\n",
    "                                                               mainQN.actions:trainBatch[:, 1]})\n",
    "                    \n",
    "                    else:\n",
    "                        trainBatch = myBuffer.sample(batch_size) #Get a random batch of experiences.\n",
    "                        \n",
    "                        if is_debug:\n",
    "                            print_debug_states(sess, mainQN, trainBatch[0, 0], trace_length)\n",
    "                    \n",
    "                        #Estimate the action to choose by our first network\n",
    "                        actionChosen = sess.run(mainQN.prediction, \\\n",
    "                                                feed_dict = {mainQN.x:np.vstack(trainBatch[:, 3])})\n",
    "                        #Estimate all the Q-values by our second network --> Double\n",
    "                        allQValues = sess.run(targetQN.Qout, \\\n",
    "                                              feed_dict = {targetQN.x:np.vstack(trainBatch[:, 3])})\n",
    "\n",
    "                        #Train our network using target and predicted Q-values\n",
    "                        end_multiplier = -(trainBatch[:, 4] -1)\n",
    "                        maxQ = allQValues[range(batch_size), actionChosen]\n",
    "                        #Bellman Equation\n",
    "                        targetQ = trainBatch[:, 2] + (y * maxQ * end_multiplier)\n",
    "\n",
    "                        _, summaryPlot = sess.run([mainQN.updateModel, mainQN.merged], \\\n",
    "                                                  feed_dict = {mainQN.x:np.vstack(trainBatch[:, 0]), \\\n",
    "                                                               mainQN.nextQ:targetQ, \\\n",
    "                                                               mainQN.actions:trainBatch[:, 1]})\n",
    "                        \n",
    "                    writer.add_summary(summaryPlot, total_steps)  \n",
    "                    \n",
    "            episode_rewards.append(r)\n",
    "            if (s == s1).all():\n",
    "                print(\"State error : State did not changed though the action was \" + env.action_names[0][index_action_predicted])\n",
    "            \n",
    "            s = s1\n",
    "            total_steps += 1\n",
    "            if NetType == ChooseNetwork.LSTM:\n",
    "                lstm_state = lstm_state1\n",
    "                \n",
    "            if d == True:\n",
    "                if r == 0 or r == 10 or r == -10:\n",
    "                    print(\"Unrecognized reward Error : \" + str(r))\n",
    "                    j_by_loss.append(j)\n",
    "                elif r > 0:\n",
    "                    j_by_win.append(j)\n",
    "                elif r < 0:\n",
    "                    j_by_nothing.append(j)                    \n",
    "                break\n",
    "                  \n",
    "        myBuffer.add(episodeBuffer.buffer)\n",
    "        jList.append(j)\n",
    "        rList.append(sum(episode_rewards))\n",
    "        rewards = np.array(rList)\n",
    "        \n",
    "        if i % (50) == 0:\n",
    "            nb_of_win_on_last_50 = (len(j_by_win) - nb_win_tb)\n",
    "            win_perc = nb_of_win_on_last_50/50*100\n",
    "            mean_j_by_win = np.mean(j_by_win[-nb_of_win_on_last_50:])\n",
    "            mean_rewards_sum = np.mean(rList[-50:])\n",
    "            summaryPlot = sess.run(tb_infos_logger.merged, \n",
    "                                   feed_dict = {tb_infos_logger.percent_win: win_perc, \\\n",
    "                                                tb_infos_logger.mean_j_by_win: mean_j_by_win, \\\n",
    "                                                tb_infos_logger.mean_rewards_sum: mean_rewards_sum})                       \n",
    "            writer.add_summary(summaryPlot, i)  \n",
    "            nb_win_tb = len(j_by_win)\n",
    "        \n",
    "        if i % (500) == 0:\n",
    "            print(\"#######################################\")\n",
    "            print(\"% Win : \" + str((len(j_by_win) - nb_win)/5) + \"%\")\n",
    "            print(\"% Nothing : \" + str((len(j_by_nothing) - nb_nothing)/5) + \"%\")\n",
    "            print(\"% Loss : \" + str((len(j_by_loss) - nb_loss)/5) + \"%\")\n",
    "            \n",
    "            print(\"Nb J before win: \" + str(np.mean(j_by_win[-(len(j_by_win) - nb_win):])))\n",
    "            print(\"Nb J before die: \" + str(np.mean(j_by_loss[-(len(j_by_loss) - nb_loss):])))\n",
    "                  \n",
    "            print(\"Total Steps: \" + str(total_steps))\n",
    "            print(\"I: \" + str(i))\n",
    "            print(\"Epsilon: \", str(e))\n",
    "                  \n",
    "            nb_win = len(j_by_win)\n",
    "            nb_nothing = len(j_by_nothing)\n",
    "            nb_loss = len(j_by_loss)\n",
    "            \n",
    "            print(\"#### LAST 5 MOVES of LAST EPISODE  ####\")\n",
    "            last_episode_moves = episodeBuffer.get()\n",
    "            starting_point = j-5 if j >= 5 else 0\n",
    "            for z in range(starting_point, j):  \n",
    "                print(\"-----------------------\")\n",
    "                plt.imshow(reverse_processState(episode_frames[z]), cmap=plt.get_cmap(\"gray\"))\n",
    "                plt.show()\n",
    "\n",
    "                print(\"- Buffer Move \" + str(z) + \" : \" + env.action_names[0][last_episode_moves[z, 1]])\n",
    "                print(\"- Move Array \" + str(z) + \" : \" + env.action_names[0][moves[z]])\n",
    "                if z != j-1:\n",
    "                    print(\"ZPos : \"+ str(ZPos[z]))\n",
    "                    print(\"XPos : \"+ str(XPos[z]))\n",
    "                    print(\"Yaw : \"+ str(Yaw[z]))\n",
    "                figure = plt.figure()\n",
    "                axes = figure.add_subplot(2, 1, 1)\n",
    "                axes.matshow([episode_qvalues[z]])\n",
    "                axes.set_xticks(range(len(env.action_names[0])))\n",
    "                actions_names = [\"Straight\", \"Back\", \"Right\", \"Left\"]\n",
    "                axes.set_xticklabels(actions_names)\n",
    "                plt.show()\n",
    "\n",
    "                print(\"         \" + \"          \".join(str(qval) for qval in episode_qvalues[z]))\n",
    "                print(\"Obtained reward : \" + str(episode_rewards[z]))\n",
    "                \n",
    "        if i % (5000)== 0 and i != 0:\n",
    "            #Save all the other important values\n",
    "            saver.save(sess, path_to_save + str(i) + '.ckpt')\n",
    "            with open(path_to_save + str(i) + \".pickle\", 'wb') as file:\n",
    "                dictionnary = {\n",
    "                    \"epsilon\": e,\n",
    "                    \"Total_steps\": total_steps,\n",
    "                    \"Buffer\": myBuffer,\n",
    "                    \"rList\": rList,\n",
    "                    \"Num Episodes\": i,\n",
    "                    \"jList\": jList}\n",
    "                \n",
    "                pickle.dump(dictionnary, file, protocol = pickle.HIGHEST_PROTOCOL)\n",
    "                \n",
    "    saver.save(sess, path_to_save + str(i) + '.ckpt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_episodes_by_test = 100\n",
    "nb_tests = 3\n",
    "path_to_saved_model = \"saves/LSTM/15000.ckpt\"\n",
    "test_print_freq = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "NetType = ChooseNetwork.LSTM\n",
    "trace_length = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading Model...\n",
      "INFO:tensorflow:Restoring parameters from saves/LSTM/15000.ckpt\n",
      "#### EPISODE 0 (TEST 0) MOVES ####\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292057ca240>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 0 : move 1\n",
      "ZPos : 1.5\n",
      "XPos : 2.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920564c4a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         -0.0048752762          -0.020699807          -0.021458482          -0.0075348485\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205730588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 1 : move 1\n",
      "ZPos : 2.5\n",
      "XPos : 2.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208c300b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.17361453          0.136208          0.14837226          0.1469975\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29203674e80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 2 : move 1\n",
      "ZPos : 3.5\n",
      "XPos : 2.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920553a780>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.5812345          0.51350856          0.49602133          0.5126799\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208c30978>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 3 : move 1\n",
      "ZPos : 4.5\n",
      "XPos : 2.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208eff6d8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.92984325          0.83695024          0.82298034          0.83863384\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205862ac8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 4 : move 1\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29209053630>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         1.121196          0.9962611          1.0000938          1.0154471\n",
      "Obtained reward : 0.9900000095367432\n",
      "State error : State did not changed though the action was move 1\n",
      "Max steps reached\n",
      "Max steps reached\n",
      "Max steps reached\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "Max steps reached\n",
      "#### EPISODE 50 (TEST 0) MOVES ####\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ebfc00fNOnTq12GZNNB27jPjFD4IGEg0/CBpINPwgaCCmFVgvJVOmTElcYYZDVx/72Meyz6qvwj61hkweeuihYlsXRtR0VUZDfTqtlBdS0DTNo0ePFtsaTtHQGoey1B69F9ZEfXENrbEm6hN+5jOfKba9xSLVJtVEp/4uX7682FZNFLZvJJrwc9FnNFxN9DyvvfZats82aWhZQ3+siVby0SnGfC5N592zZ8+g1x8MHlfge9m5cydOnz5tg32HiV/8IGgg7SyaucDMfmxmPWa2w8w+3/r7DDN7ycz2tP5/ddW5giAYH7Tzi38ewN+mlG4EcDuAvzSzmwA8CeDllFI3gJdb+0EQ/A7Qzmq5RwAcaW2fMrMeAPMAPAbg3tbHngawHsAXvXNNmDAh813Yh9Xpllp6iP1ob7qlxrM1NVN9MEanVHJFVI1Zsx+q4xH6WY65qv+qvpz69cxQSlJ5mmgM28sdGIom+sy4wrF+ltFnpJpwjkdVmS7Wz9NEx2U0z4A10bwCtYE10bEDvQ5r4pVOU010LI7vk+3x3h9mSD6+mS0CsBLABgBzWv8o9P/jMLv8m0EQjCfabvhmdiWAfwfw1yml8oqGA7+31sw2mdkm79c2CIL6aCtl18wuQ1+j/5eU0n+0/nzUzLpSSkfMrAvAscG+m1JaB2Ad0BfO4y4MzxrThS+0G8RhJu1qcfdGu5SahsvoWvTardUwDsNdL+3e6T9w3OXULpxekzXRlE7tumrojRmKJnxe1UQXk9AFJBm1l3VRTdgm1Vk14a623otq4rkUrImmWetsOK9yrVbrYU287juQa6Ln9TRRuBoyf2/UuvrWd6ZvAuhJKf0THXoewJrW9hoAz7V1xSAIxpx2fvHvAvAXALaZWf/6T18C8GUA3zWzJwC8BeBPLo2JQRCMNu2M6v83gLL+w6dG15wgCOqg9kUz2WecN29esV21Kgv7ZJqmyf6jVsbxptNqmEarxm7cuLHYVt/8k5/8JMrQyjRsr07T1HAPa6Ls3r0722dNdHUXTxMNDe3YsaPYVk10kUoOc6kmqjUvQqqacLWjqhAY+816L6oJj3t4mmja7bJly7J91kQr8HiaqO067sGavPLKK9kxHl9RTTQMzPazJmy3R6TsBkEDiYYfBA0kGn4QNJBap+VOmzYt3XXXXcU+r0JatQIIT+fVGCz7WJqOqn4nH9d8AC1Rxb7lnXfemR3jOK/a/v3vfz/bZ79ZfTeNm7MmXokxINdE4fJQqonGmtmHrUqJZZtUEy11xc9pJJqwn6wrDakmt9xyS7Gt5apYE71PrvoL5JpoOi9PnwUGPqcye4Bck+9973vZsauuuqrYVk047RvI7WdNDhw4gDNnzsS03CAIBhINPwgaSO0LavBMthtuuKHY3rlzp/s97iJxdxjIu5xa6WXLli0oQ8M0d999d7a/YcOGYlu7czybUNNGV65cme1zaibP5AIGzuxjTRTViDXRajOrVq0qtlUThUNiqgmfB/A1UReMZ8ONRBOGq90AviaaBs73ot13tZ01Uf08TRRPk+uvvz47xrMSVROFXRPWRO+5jPjFD4IGEg0/CBpINPwgaCC1+vgXL17M0jw5VKSpjhoiYz9aQ5B8Hk3pVF+OwyBayUdDRVwFVavUcOqvV2UFyNMrNSVW0175XnTapt43a6IpnZ4m3oKRI9FEfX62VzVhH7VKE34XdEqxasL2e5po6FH9eNZE08k17MuaqO2qJ9ur4zJ8nqqqP5yazJpoCLOM+MUPggYSDT8IGkitXf0LFy5k3VPOVNJFCzWkw9lbut47L26h4RPtInH3ryrLj7t0GkLs7u4utjV7jGf1AQMrtjAaCmRNtOusC1iwJprt5mmi981dTj2mGWO8EIZqovqxi6GasO2qn2rC3XCtzqOa8HcXLFiQHfM00ZmZrElVFh1ropWGtEoSa6LvPKOa6HmmTZtWbLMmVRWAis+19akgCH6viIYfBA0kGn4QNJBaffwJEyZk/hr73+q7aeUSTmfUY+xbegsjALlvp6EPDcvxNTW05oVN9LO8r2FK9umBXBP1LXW8gu3T8QBPE8XTRMNKniYaEuPQln6Wz6thLdWE761KE/ZxPU28GXVAromOe3jPQcN5nibqj3ua6NhBmSaXZEGNIAh+P4iGHwQNJBp+EDSQ2lN2OR7Jfp+mVypeeiqvKqIpkhy7BXIfW/0vjZVyWq7GhLmai+YcqO/GlXXVd9PPsibqF3tpzBr7Zr9UNdExEk6nVU20KrCniT4Xvle9T06D1WvoZ3lf71M14VRgHd9hTfQamprMmmhFXn0u3qo3niZqu6eJ+vg8rsCaeFOamfjFD4IGEg0/CBpIrcU2J02alHghBe6uzJkzJ/ushlu4e6rdp3vuuafY3rdvX3ZMiyhy6EOLJmrxSm/WGtvghY30uIaj2GUAck04LRMYGKphTbT7zgt+qCaacsqzwlQTDQVy91410e4od2u9hU30PlUT7lrrgiOqCXefvUVQ1GXQMCZrsn///uyYhhtZk6qFTlkT1ZbTj6uePX+WNenp6cHp06ej2GYQBANpZ7XcyWa20czeMLMdZvaPrb8vNrMNZrbHzL5jZuXrEwdBMK5o5xf/LID7Ukq3AlgB4CEzux3AVwB8PaXUDeAEgCcunZlBEIwmQ/LxzexyAP8N4HMA/hPA3JTSeTO7A8A/pJT+0Pt+Z2dnmj9/frHvTSHUtEje12ouXK1U/TqF71f9Jvbr9Lj65lzNRTXU1F9G7dPz1qGJ+vgcRhqJJrrf29tbbKsm3nPS8/DYgY4VqCZsn4bzWBO9hsKaeBrovtqj982a6Gd1fIDRd4yvyZr09vaO3oIaZtZhZlsAHAPwEoB9AE6mlPqfyCEA5cu8BkEwrmir4aeULqSUVgCYD+A2ADcO9rHBvmtma81sk5lt0sSHIAjGhiGN6qeUTgJYD+B2ANPNrL+/NB/A4ZLvrEsprU4prW63OkgQBJeWypRdM5sF4FxK6aSZTQFwP/oG9n4M4LMAngGwBsBzlRebODFLr+VYpZZx0nRG9ml0tRfuSejqJAcPHsz2vXJVc+fOzfY5fqv/aPFYhfpfWpWVp1tqjHrmzJnZvsZvGa3uOlxNFLZ3JJpouihrpJrwe1ClCfv4ekw14endniaahqu6e5roNF3WRP109eNZE33nFy5cWGyrJtoeWD/WRFPUy2gnV78LwNNm1oG+HsJ3U0ovmNlOAM+Y2f8C8DqAb7Z1xSAIxpzKhp9S2gpg5SB/348+fz8Igt8xaq+yy2mevPCkhj20y8RVbfUYd5m0260z7jiMo6m/2r3ic3GqMZCnbao9W7duzfY5bVjTezXtlTXR0JWmlbImOnDqaeKFhlQTb/EI1US74ay9asKfVf1UEy9Ep5pwV1/DlqyJPmtNGedr6iIeuqAla6Lv24oVK7L9soUwlCqXgc/DmlRVFuonRtuCoIFEww+CBhINPwgaSK0+fkdHR+Ybs39WtQIOhzPUn+WwkufTA7nfp9dQ/5H9PJ3ey7arPZrSyX6XXlOnePJ5q/w8DfEwniYKh6f0Gt4UY08TINdFNeHPVmnC19QKRvpdDsupv8uaqD2aQsyaaFUinWrrTbvWZ+glsXma8HRtvQ5r0m6SXPziB0EDiYYfBA0kGn4QNJBaS291dnYmXumW/Sj1r7WME6eO8pRYALjjjjuKbfXrNA7NaaVa+kjj0jyFUtM/uXSU+rZ6XvbzNH6sPvRQKsGyJlrpddWqVcW2aqLPnEtzqe3qx7P9qon6pZyToOdlm/S+VBP2Z/U8+l1+hpomzJqoturjsya6mpC+J6yJjlVxGi6Qa6Jpwjxmo/elz4zHHViTnTt3RumtIAgGJxp+EDSQWsN5ZpZ127h7qmEPr7qLV6lWq6VoN4275Ro20m44d/+0++kd4+4ckHffNSyjeIszKKyDduc9TTQ9mu1XTTSt1Ltv7T7zdVQT7uZWacLH1SVU+JqeJhri1JRdvjd1YTxNVFtvX++Fr6ma6L2UaaIuchnxix8EDSQafhA0kGj4QdBAavXxU0pZuIOroKhvq9VcONTB1UeAvMJs1QoknNJYVZHXm1bKq6foNbTqD9ukKZV6n6yJhoY0PDVcTXS6qqeJjg94mujYC4duVRP2m6sq+fB9qr+tmvAYjvrtrIn6wlpVhzVR/9pbbFVt11Ava8Kr4QD5eFRVmboyTfRdLCN+8YOggUTDD4IGUns4j7uOXO3F65Lrvs7Q4u6xdsu0ogyHDblaix4D8u6Wdr3YBu02aiFHriijYSTNAlN7GU8T/R6HhlQTDUdxF3gkmqh93hrz7Maoe6GacLe2arabV03IC5d5mixZsiQ7pu4F66AzJj1N1C3wNNFnyGFB1iRm5wVBUEo0/CBoINHwg6CB1Do7r6OjI3HIgsM/OtNL7Tp16lSxrSGSm2++udjWEImmlTKe/wrkix9o5Vf2H/V7Gu7hmVZV1YRZEx330JAda6JjB7fcckuxrZoorNFINFG4Us1INPFCf8PVRMOCGm5kTfQ8ai9roj62jjOwJjqewotheAuvAvl4AGty8uRJnD9/PmbnBUEwkGj4QdBAouEHQQOpNY4P5L67pngy7U4v1PPoOXWaqRfnnTdvXqmt6luyD6ixW4258riG+pY6XsH265RiTxNNrfU0UX+b/UnVRG3wNPHyDLw4tMbUVRPWV9NwVRPW19NE4+Sa1sz2aVVd1ZM18SofA7km+i54muhnuQoVa8JjHB5t/+KbWYeZvW5mL7T2F5vZBjPbY2bfMbPyUbQgCMYVQ+nqfx5AD+1/BcDXU0rdAE4AeGI0DQuC4NLRVjjPzOYDeBrA/wbwNwD+CMA7AOamlM6b2R0A/iGl9IfeeTScx4UcvVRVRbuNHE7Rrp/X9dHunXbhvGozHMrSsJt2gTk8pV1BDduwJvpZDXMx2j3mIo+qic764/OqJtrV51mUqoleh8Ng3gy8qnAZh720y6uaeG6LFr5kVD8+r96nphSzJlWz6oaricIuLGty/PhxfPTRR6MWzvsGgL8H0O+gzARwMqXU/6QPAZg32BeDIBh/VDZ8M3sEwLGU0mb+8yAfHbTrYGZrzWyTmW2qM1koCIJy2hnVvwvAo2b2MIDJAKairwcw3cwmtn715wM4PNiXU0rrAKwD+rr6o2J1EAQjorLhp5SeAvAUAJjZvQD+LqX052b2bwA+C+AZAGsAPFd1ro6Ojsxn5IUA1KdRX479R65iAuQhEk3hVL+dr6Mpk11dXdk+L8ah/rZO1WR4IQ61Sccc1D5dMILRhURYEx7nAHxNNOzG1WdVE/VnObSlmqjPunjx4mJbNfGqzagmfHzmzJnZMdWEbfI00WtomJA10XEPTxN9b3U8ijU5cOBAdoyv440TAfm4A2tSlUZdnL+tTw3OFwH8jZntRZ/P/80RnCsIghoZUgJPSmk9gPWt7f0Abht9k4IguNREym4QNJDaq+xyrHLv3r2/NcTxxQHguuuuK7bV39FSXIzGZ9kH03j2rl27Btjbj/p5XrklXSGFv6uf1ZJUrInGj/W+WRP123mKp6Kf9TTZtm1bts9x8qrqvWyvasJjGeoHqyb8zuiKPKoJj2eon8ya6DF9LqyJ5id4mqg93d3dpfZqZWl+31QTtZefE2tSlTJcnK+tTwVB8HtFNPwgaCC1dvUnTJiQhYs4PfXtt9/OPqtdf6+LxCESnZGlXUPuwmlqqIa9OJynqatcLdUL2QB5V1XDTxo+0/XoGdWINdEZZIsWLSq2VROFbVJNli5dmu3v37+/2FZNdMYbV65VTdjd0MVKVRPWT/VRTfhe9T1hTdSdUPeHNdGZclotijVR/TxNNHzMtqsm3oKkrIm6tmXEL34QNJBo+EHQQKLhB0EDqdXHv3jxYuYPcxhOfXoNK7HvopN9eGUTTelUf5Gvo36xhgX5szrFk8ONVQs5clhO7dHxAS80qffNmqhv52mi12T7VBNNAfU00ZVh2F7VhKsAVWnC74JeUzVhn9rTxJvOC+Sa6Hk0NMmaeO+t2utNR1ZNdOo3n5c1iXBeEASlRMMPggZSe7FNzoLiro2XYQfkoSPtzngLRKoLwcc1NKTdSO42agjHc1O0C8fdNu2yaVaGH8TSAAAL7UlEQVSYt5CC3jdrotf0NPFmkI1EEw058TP1urVVmnA4TcNjqglr7S2aqeeZMWNGts+aeEU79Vx6TLMbWRN91nwvqom6EJwlydfX75URv/hB0ECi4QdBA4mGHwQNpNZFMydPnpx4RhlTVZ2U0WopjPo4ns9Tld7oLfjBeCG4KhsULxwzXI30+t4z9/z0KnTsgK+rGg1FE6bdcBXg6zV37ty2z1Nl61A04ndKn0O71XM89u3bhw8//DAWzQyCYCDR8IOggUTDD4IGUnsFnnYXw9QKqXoexlt1RBeIZLTCjZ5HY6eM59dpjNhDp3x64wpDOS9rpHp5qxapX6xTbz1UP88Gb5xhJPp58Hm1wq23yo6iac0enn56nzpV2PvsSIlf/CBoINHwg6CB1BrOu/zyy9MNN9xQ7HvdWu1yeqEZPqaVVDw0ndL77u7du7N9z3YN/3if9dwJXhixCs92dWk8LTWNVNN9PXQWoPdu9fT8duFltU9hPatmcXoMRU8PT08NaSo6g7GModjKFYAOHTqEM2fORDgvCIKBRMMPggYSDT8IGkitPv7UqVPT6tWri33PHxpuimfV/XDIRKuwKByy8/x0rS7j3Ve7Ph4wsAqLl66q4U/Pb1Zth2uvHtMFNRgN9XnhUNXaCz+q7e3qq9oqPHbghZaBfFxktNK39T327OV73rZtG95///3w8YMgGEhbCTxmdgDAKQAXAJxPKa02sxkAvgNgEYADAP40peT/cxcEwbhgKL/4f5BSWpFS6u+rPwng5ZRSN4CXW/tBEPwOMJKU3ccA3Nvafhp9y2d/0fvChQsXBvjD/aj/6vnUGrP2fED1dT0fUFe5YTT1cijxbY9Tp05l++zbaZqml556+PDhbJ9LSalfqbZXrbRThk4j9VJZ1f/XSrXeZ4cyLuLBWqvuOlbAWh88eDA75o2feAuHKvqOeynOXgVopt2U+HZ/8ROA/zKzzWa2tvW3OSmlIy0jjwCYPdgXzWytmW0ys03tGhUEwaWl3V/8u1JKh81sNoCXzOzNdi+QUloHYB0AXHHFFfWFEIIgKKWthp9SOtz6/zEzexbAbQCOmllXSumImXUBOOaeBH1dtrJuuc6i87pIXiioqtvK3WfPRQDaD9OoG6DdNA4NVc2y8rqR+l0vfOZdR7vknlvlhRC1B+edR7Vm2/WY50ZV6ceprmo7vxt6nqG4E/rd6dOnF9teZV/Af3f5PVL3TM9T1j7aDc9XdvXN7Aozu6p/G8CDALYDeB7AmtbH1gB4rq0rBkEw5rTziz8HwLOtf4EmAvh2SukHZvYqgO+a2RMA3gLwJ5fOzCAIRpPKhp9S2g/g1kH+/i6AT10Ko4IguLTUmrI7adKkVFYht6rSi3ecfdaqNFzPh1b/jMOGOl3VC115Ybeq6Zac2lpVvXXZsmWlx44dKx9y0fTP9957r/Sz3jiCTmvWEBnjVUmqSp/l8ZWqKbzeuI1XjUmPeWMJ3jvW3d3tWOevwMRUjT+VtYcDBw5Eld0gCAYnGn4QNJBo+EHQQGr18Ts7O9O1115b7Hu+nZflpzZ7/rY3DbIqduvFk9k/q8od8O7Fs6+qmqs3XZSvWTXu4a3Q61V+rSp7xfem6b18rKpclaef94y8+9JnNtxnBPjPacGCBaXH9Jr8nPSa3ipF3I7Cxw+CoJRo+EHQQGpdUKOzsxOLFy8e9Fhvb6/7Xa5kquEyDpEdPXrUPY8XTnv33Xezfe7Oe+FEngk3GDpzjlF3g+9TF3bU8A9/VkNr7A7pfWn32EsV9RYo1UUptDvKKbNqH6e5Kt5MQ52ZqS4Ed581RMf3NpJnpq4m66fvpmrN3XJ1Ra655ppiu+o9LguztlvhJ37xg6CBRMMPggYSDT8IGkjtKbuzZs0a9BiH+QbDs3Pr1q2lx4ayIon6R+yvqW/J6alqm7eqzbx587J99fH5XHoenRrMPr6Xjqq+t/qW/F0N0WnFpKEUU+HxHP0e36dqsGPHjmyfn0vVIpneqjbsX3vPU+2raiM6FsPodXhfxycYHRNRynz5w4cP4+zZsxHOC4JgINHwg6CB1BrOU2bP/m2ZPu0KajeIu6DarV2yZEnpeRTuVmqYy1vDXUNpV199dekx7ZKz7ZpFpwtYcpdTQ0HLly9HGVpthnXQmXpqA+/r7DddCJPdBC2YqfZyRpu3SIbOhOPnCeTdWnVF1E3gZ6qa8GfVDdDwHh/X90SvydfxFiQF8uerbsBNN91U+j3vvebQ46hV4AmC4PePaPhB0ECi4QdBA6k1nDdlypS0dOnSYp+vrf6i5yup78b+mIZ71B9jf1KvoX4f+5PqY7F/pv6r+qGcXqnHtDKNN1NN/VCeOacpxey36+wxvQbbr/6/2svPTMcD1MfntFx9ZmyTPgevsq9eU8d7+Ls6duCF+vQ+vZmHai+/GxqG0/ePfXw9j46nMPqO8fPmd3HPnj344IMPIpwXBMFAouEHQQOJhh8EDaRWH3/ixImJ/b6y9F1goP/DU0DVb2JfV6crqr/oVaPx0l4VPk9VWiv7Z95UTCCPWaut6kPzfXvTZ/V7mp7KPqL6mRprZt9c8xVUa68CDvvqVdOP9+3bV3oNreLE9utn2efX995L7dbP6nPh56/PXjXge9MxG37nq8ZaGB5XOHLkSKTsBkEwONHwg6CB1Jqy29HRkXWpvG63pklyF8qbDaWhK68Yo1aB0a4hd6XV1qGEnLyZVhpy4m6ldmPVBnYTtDvqzfzS83R1dRXbqp/azl1X7R57i0Bo+IxTnquKinL3WHXn8wD+IiheV1rh4xoiVlfEK0iqi6KwfXqM7dPZd967wK5Gu4t/xi9+EDSQaPhB0ECi4QdBA6k1nGdm7wA4COAaAMcrPl4nYY/PeLMHGH82jRd7rksplcfJW9Ta8IuLmm1KKa2u/cIlhD0+480eYPzZNN7sqSK6+kHQQKLhB0EDGauGv26MrltG2OMz3uwBxp9N480elzHx8YMgGFuiqx8EDaTWhm9mD5nZLjPba2ZP1nltsuFbZnbMzLbT32aY2Utmtqf1/6u9c4yyPQvM7Mdm1mNmO8zs82Npk5lNNrONZvZGy55/bP19sZltaNnzHTObVHWuUbarw8xeN7MXxtoeMztgZtvMbIuZbWr9bczeoeFQW8M3sw4A/xfApwHcBOBxMyuvJ3zp+GcAD8nfngTwckqpG8DLrf26OA/gb1NKNwK4HcBftnQZK5vOArgvpXQrgBUAHjKz2wF8BcDXW/acAPBETfb083kAPbQ/1vb8QUppBYXwxvIdGjoppVr+A3AHgBdp/ykAT9V1fbFlEYDttL8LQFdruwvArrGwq3X95wA8MB5sAnA5gNcAfBJ9ySkTB3uWNdgxH32N6T4ALwCwMbbnAIBr5G9j/ryG8l+dXf15AHpp/1Drb+OBOSmlIwDQ+v/sis9fEsxsEYCVADaMpU2tbvUWAMcAvARgH4CTKaX+KZJ1P7tvAPh7AP1T22aOsT0JwH+Z2WYzW9v627h4h9qlzmm5g1UFiZBCCzO7EsC/A/jrlNJvyhZFrIOU0gUAK8xsOoBnAdw42MfqsMXMHgFwLKW02czu7f/zWNnT4q6U0mEzmw3gJTN7s8Zrjwp1/uIfArCA9ucDOFzy2bo5amZdAND6/7GKz48qZnYZ+hr9v6SU/mM82AQAKaWTANajb+xhupn1/1DU+ezuAvComR0A8Az6uvvfGEN7kFI63Pr/MfT9w3gbxsHzGgp1NvxXAXS3RmMnAfgzAM/XeH2P5wGsaW2vQZ+fXQvW99P+TQA9KaV/GmubzGxW65ceZjYFwP3oG1T7MYDP1m1PSumplNL8lNIi9L0zP0op/flY2WNmV5jZVf3bAB4EsB1j+A4NizoHFAA8DGA3+nzG/zEWgxoA/hXAEQDn0NcLeQJ9PuPLAPa0/j+jRnvuRl83dSuALa3/Hh4rmwAsB/B6y57tAP5n6+9LAGwEsBfAvwHoHINndy+AF8bSntZ132j9t6P/PR7Ld2g4/0XmXhA0kMjcC4IGEg0/CBpINPwgaCDR8IOggUTDD4IGEg0/CBpINPwgaCDR8IOggfx/rIcd+NHdpTIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208efff28>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 0 : move -1\n",
      "ZPos : 0.5\n",
      "XPos : 2.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205b91908>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         -0.015060563          -0.0037234202          -0.007293904          -0.0061369594\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205bd8630>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 1 : move 1\n",
      "ZPos : 1.5\n",
      "XPos : 2.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292083d5588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.17622453          0.16019526          0.15182744          0.14970034\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208505320>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 2 : move 1\n",
      "ZPos : 2.5\n",
      "XPos : 2.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208585c50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.56801134          0.4864059          0.5029998          0.5053405\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292085b32b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 3 : move 1\n",
      "ZPos : 3.5\n",
      "XPos : 2.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208592390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.8838819          0.7784146          0.7755548          0.79002124\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208663828>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 4 : move 1\n",
      "ZPos : 4.5\n",
      "XPos : 2.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292086b2438>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         1.0454884          0.9031947          0.93247586          0.9367656\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920872ca20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 5 : move 1\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208787cc0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         1.1465069          0.9691676          0.9984922          1.009605\n",
      "Obtained reward : 0.9900000095367432\n",
      "Max steps reached\n",
      "Max steps reached\n",
      "State error : State did not changed though the action was move -1\n",
      "State error : State did not changed though the action was move 1\n",
      "Max steps reached\n",
      "Max steps reached\n",
      "Max steps reached\n",
      "#### EPISODE 0 (TEST 1) MOVES ####\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292085e70f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 0 : move 1\n",
      "ZPos : 1.5\n",
      "XPos : 2.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920882df28>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         -0.0049881972          -0.02067656          -0.021935089          -0.008064445\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920888c588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 1 : move 1\n",
      "ZPos : 2.5\n",
      "XPos : 2.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292088e6da0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.1375314          0.12155777          0.11324316          0.12710097\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292089392e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 2 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 2.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292089714a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.42945966          0.42350274          0.42676696          0.45446837\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292089c2588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 3 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 2.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208a20518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.73064417          0.68977827          0.6407144          0.7723988\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292088ef908>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 4 : move -1\n",
      "ZPos : 3.5\n",
      "XPos : 2.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920a99b390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.8414725          0.92052877          0.84840876          0.8555122\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920a9af358>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 5 : move -1\n",
      "ZPos : 4.5\n",
      "XPos : 2.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920aa495c0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.8232383          0.87351125          0.8327056          0.7269993\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920aa7e780>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 6 : move -1\n",
      "ZPos : 5.5\n",
      "XPos : 2.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920aae3780>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.76999485          0.7969271          0.6902639          0.6506409\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920aae3160>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 7 : move 1\n",
      "ZPos : 4.5\n",
      "XPos : 2.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920aa59160>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.7651015          0.6149964          0.6779234          0.5610142\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920a9afa90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 8 : move 1\n",
      "ZPos : 3.5\n",
      "XPos : 2.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205cdd2b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.7203061          0.6959126          0.60772985          0.67030674\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292035b2f98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 9 : move -1\n",
      "ZPos : 4.5\n",
      "XPos : 2.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2927f7e24e0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.75459164          0.76070225          0.69648325          0.7230748\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292047e9668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 10 : move 1\n",
      "ZPos : 3.5\n",
      "XPos : 2.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205cdd1d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.87013745          0.83000964          0.7548664          0.7799605\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2927f5636a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 11 : move 1\n",
      "ZPos : 2.5\n",
      "XPos : 2.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204800860>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.8893788          0.830266          0.79696566          0.8123006\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920aa2eb00>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 12 : move 1\n",
      "ZPos : 1.5\n",
      "XPos : 2.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208aad898>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.87213326          0.747356          0.7441885          0.8232696\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205d142b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 13 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 2.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208ef82e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.67029196          0.6239449          0.7481741          0.8341504\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208a952e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 14 : move 1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204bd5c88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.70667803          0.62989956          0.6729214          0.7050143\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204a2bcf8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 15 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292087e9780>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.6225857          0.54272985          0.6168799          0.73174095\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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z3X1OqVTzSUMmbM6oecfpoJdddll2TCu/cMrkxz/+8eyYhqfYlNUwEae9qimtqaIcptEqK/3qCeS6NqEnkOvq6Qnkujahpx7X0NbSpUu722oS6ww33tfraIiOzW5N/eVFMoG8HbRt2QXTd0pdD5aff1AHtmimTd7hOwB2TXX6Do8C2N7Z3g7gkdK1giCYH/SSwHMLgD8C8LyZPdv57K8AfB3A983sHgD7Adw9NyIGQTBoehnV/ymAKtPhk4MVJwiCJmh80Uz2LzmNU31AHQjktFOvGqleR303rsKiU2Q1FMghKG9hR/WZSws7Mv3qCeS6NqEngMrnp9dRmtBTqRPOU/n4Pjp2of42L4SpaCUdRse1eCxBQ6V6HU677mfQPFJ2g6CFRMcPghYSHT8IWkjjVXarVhrRzzXGySuJ6FRRjnm+9tprrgw8dXQ2K/SuW7euu71nzx73nlw19pVXXsmO9asn4Os6F3oCvq5aHZd1bULP0rmM+sV1pg1reStu3zpTf7nkGZDnJGhatY7L9Fpiq4r4xQ+CFhIdPwhaSOOLZnLogyuH7ty5MztXZ4VVpSgCwK5du7rbampt2LBhmgxTzGYBy9HR0e52nYU6NSW2Xz2BXNcm9ATqLWDJujahp6LPhdOENdVXq+Gw+T4bs1rdLA5j6mxGTlXWNGGdschtwteMKrtBEFQSHT8IWkh0/CBoIUNdNJNDFOovqs/FaaeaVsp+n4ZzNAzCU0C3bdvm3nMuFuoclJ5ArmsTegL1FrBkXZvQE8h93zopux7qX+v4gDfOUBW+BqanKnMb6T11qjLrzfcPHz8Igkqi4wdBC2l80cyqBRB1Vp1WLuFQjJqNXpFHPZfNIi+cAgxuoU6+p87kej/pCfgLWKrJy7o2oWcJzhbUcJmayKynLvDhodfx2kiLtPI9VT6dIchZf3zNXjMH4xc/CFpIdPwgaCHR8YOghQw1ZZfTOLV6i4Y62FfSMA37i3odDYtwpdVhLNSp1Xr61RPIdW1CT6DeApasaxN6Avm4gvrbOrbBeL6xt8AHkLeftxBHCb5PqRJS1ffCxw+CoJLo+EHQQqLjB0ELadTHTyn1vNifxi15Kuny5cuzY1xpVeO66qOyD6g+XxMLdQ5KTyDXtQk9gXoLWLKuTeg5SDhfQHMHvOm+PEUWmD62wdWFdIyExwq4ii4wfXovT7XuJzU5fvGDoIVExw+CFtLTopmD4tJLL008U4zNPTXRdUFGL2THIRQt3KhmI5tTW7duzY41sVCnmn796gnkujahJ1BvAUvWtQk99bimvfJ11IXR58Lvo5r6+l02tVUeDcvxvp7L99y8eXN2TF2IqjTrsbExnDlzZvaLZgZB8MGjl9VyF5vZz83sl2b2gpn9XefzTWb2lJntNbPvmZm/DlIQBPOGXn7x3wVwW0rpowBuAHCnmd0M4BsAvplS2gLgOIB75k7MIAgGSS+r5SYAUw7Gos6/BOA2AF/qfP4ggL8F8M/etd57773KBRA1LKMhE/YDvWmS6kuqn8chlGEs1Kl+cL96ArmuTeip1yotYMm6NqEn0Htoq3Qej32pj6/jYuyrazq0nsvjIqq398z0utwO3O4qaxU9+fhmNmJmzwI4AuBxAC8BOJFSmnqDxgGsq/p+EATzi546fkrpXErpBgDrAdwEYOtMp830XTO718xGzWy0179GQRDMLbVG9VNKJwA8CeBmAEvNbMrWWw/gQMV3HkgpbUspbfMKDwZB0BxFH9/MVgB4L6V0wsyWALgdkwN7PwFwF4CHAWwH8EgP15rmF06hfxQ0fnzs2LHutsZ5eUHGQ4cOuTJwyuQwFupU+frVc6ZrVck3KD2BegtYsnxN6AlM940ZHmfQNvH2OWYO5LLPBq/0lqLWMrcJp1H3alX3kqu/BsCDZjaCSQvh+ymlx8xsJ4CHzexrAJ4B8J2e7hgEwdDpZVT/OQA3zvD5y5j094MgeJ/R6Oy88+fPZ+EMXjhB143XdEYOv6g5w99Vc0lnOfHxQS1gWWehTl33vF899b5N6KnXLS1gybo2oWdTqLvK8uuMRXWd2E3Q6/B3tS01LZyrHfE1S67uFJGyGwQtJDp+ELSQ6PhB0EIaX0mHfTsOQ6i/qHCYRv0oDuloOMdLFb3mmmuyY00s1DkoPfXcJvQE6i1g6ek6F3oC/fv8qgunLmuasIbh6lD1/gP5eIDqqWMk+t26xC9+ELSQ6PhB0EKi4wdBC2ncx2fY19QqrJomyT6X+qg6FbfqHkAeHx3GCr06LfeDqieQ69qEniV4Gqz66erHe9WEPdQ317g6j0HoykN8TPuDphTzOEhVGrxH/OIHQQuJjh8ELaTxlF02/ziMpFVEvbRINcvYhNPraIosh0xms1Ann6tmGesF5As76syufvUEcl2b0BPIdfX0BPz01LnQU8+tY6KrC8HyamUhdVv4nnXMbm8mXalCEFf65efbq+sTv/hB0EKi4wdBC4mOHwQtpFEf/+KLL8YnPvGJvr773HPPdbc1ZMJ+p+erAXmVU638UmdhRx6r0PRJvae3sKNOkWV/UX1mvQ/r2oSeKsN807N0T6/0m1ddWN833Wc0ZVirCXGoUt9VTctl1OevGsvQ51VF/OIHQQuJjh8ELaTRRTMXL16ceMYWh3E0pKNysamjx9h8uvDCC7Njaj5xxtaVV15ZeR1gbhbq9MJjgB+a1DAS69qEnkC9BSy9kOdc6DnTcUYzFhl9p3hfn5lnoqsrom4Luwn63rCZri6CnsvX4bDqyZMnMTExEYtmBkEwnej4QdBCouMHQQsZ6uw8L4TijT14aYnqS3rVU4axUKf6pHrPOumfrGsTegL1FrBkGZrQU9EQGL9T3jFFZS89w17htFvd16q6ijeW0Qvxix8ELSQ6fhC0kOj4QdBCGvXxFyxYkMVkPd/Im5aoxzjmqZVeFT53GAt16rRcvSf72xrP1liup+tc6AnUW8CSr9uEnnpPD/WvvXGGOhVtNcavYyY8PqAVjdlv19WPtI2q7lGazjtFz7/4ZjZiZs+Y2WOd/U1m9pSZ7TWz75lZ9UhdEATzijqm/lcA8EJq3wDwzZTSFgDHAdwzSMGCIJg7ejL1zWw9gM8A+HsAf2aT9sRtAL7UOeVBAH8L4J+966SUMvPeC6/UgVMWNSyjBQ35/oNawLLOQp2aEuuZkeoKacUbr3DjXOip9ywtYMm6NqHnbND3j+VVV0nDyew2aMq4wqa+Phd2N9QN0FmI7A7Npan/LQB/CWCqla8AcCKlNNUC4wDW9XitIAiGTLHjm9nvAjiSUvoFfzzDqTNmQJjZvWY2amajXo2xIAiaoxdT/xYAv2dmnwawGMClmLQAlprZws6v/noAB2b6ckrpAQAPAMCSJUuaX8w8CIJpFDt+Sul+APcDgJndCuAvUkpfNrP/AHAXgIcBbAfwyGwEKU0P9qblLlu2rLut4Ry1MjhVdBgLda5evTo75k0x1lAQ6wnkujahp55bWsCSdW1CzxL9jgeoT++9q3rMSylWvb3p0SoDVy3isYGBh/Nm4KuYHOgbw6TP/51ZXCsIggaplcCTUnoSwJOd7ZcB3DR4kYIgmGsiZTcIWshQp+UydeL4XrxYUb9u2At1agxdfWr2+97PegK5rk3oqXi+uMbmvfevNDbAumiVW9Wbr+VVZ9YU4jqLg/ZC/OIHQQuJjh8ELWSopn6/abpeaKPEli1bKo+pSefNiGJKoUivuquiqZpMnXAUp7Zqmqvi6VmnCrOGKr1Q21zoqegMNzafS6azt0iFVwFaqdN+bPrXSXZjl6rXtotf/CBoIdHxg6CFRMcPghYyVB9fK6Yy6qt4Ppm3GKL6rzyFslTd1bunVyFY8aakejKU/NBSRdwpvLRRwNfF+66GqpReK9doG+h74bVDr22g6PulfjrLVFp0lN+p0rgVV8/VKkDcXvpMdLovhzy9flRF/OIHQQuJjh8ELWTeZO6p6aVhED6u53rhvDozqdTk9FwIDy0OWcelYRlK7oSnmxas9PBCR17YrQTP7PMKZpZCUNx+JfeM22Tt2rXZMa1iw2gbeDPe9L3g785mEVr+rjdDEcifC7tjvd4/fvGDoIVExw+CFhIdPwhayLzx8etUOVF4UUj9Xh0fXylVTJ2itIhDv5VfVL5eU4gB32+vs5BJHdm99tIFNbywV78z7hRNleb03j179rjX5fdRQ4ZemNKbjQfk6b/eDEEdF1q+fHm2z+nQ/YwxxC9+ELSQ6PhB0EKi4wdBCxmqj68pix7s/6gfVWeVljp46apeeqXKw75cyYf2qgnXaS+O4+t1vPZS+epMD/XaSxe39NAqRezvlvI9vOvwuRs3bsyO7du3r+fr1EHHGTjV1luRR9Fj7OP3k28Sv/hB0EKi4wdBC2nc1GfzxqvQouEML3WUzdMzZ8649/cKGnozvXTRDC8NV10Rb3aZV3CxNOvKS+n1TEEvLKgmpe57baT3Yfk45ArkupX0ZHNZ5Tl16lTl91RWL933uuuuy/b37t3b3VZ3Qp9vnQo4vc6k04Kj+j1Oh2b5enUH4xc/CFpIdPwgaCHR8YOghdhsphHWZfHixWn9+vXdfb53yTfyQhbsu9VZfLNUIYZ9N89PV9k9P1h9NU9ePdfz33RqsneuyuuNn9SZUuydqyGxlStXVp6rYcE604Z7rWhUmt7L8r7yyivZMdWbx5V03MUbI/HeG72Ovid8nL93+PBhnD17tli+On7xg6CF9DSqb2avAjgJ4ByAiZTSNjNbBuB7AK4C8CqAP0gpVa+3FATBvKHOL/5vp5RuSClt6+zfB+CJlNIWAE909oMgeB/Qk4/f+cXfllI6Sp/tBnBrSumgma0B8GRK6VrvOosWLUpVK5+oL+RN21Rf0hsf0Ji159draSZuG43deumppVJNjCe7+nmer6v38Px2zXXwfHPPF1Z56uQHeOMgOr3Xi9XXkd1rP62k6z1fLSPG5+pz0PeN3ymdquy9C6onP0N+1idPnsTExMTAfPwE4L/N7Bdmdm/ns1UppYMA0Pl/xtEaM7vXzEbNbHQ2efNBEAyOXjP3bkkpHTCzlQAeN7MXe71BSukBAA8Ak7/4fcgYBMGA6anjp5QOdP4/YmY/BHATgMNmtoZM/SM9XKcyzFQnzdU7prOh1Ozm+6jJpueyqe2ZfmpW63XY3CuFEL0FNtV0rWMaeqiZy3h6q+ns3dNzA0phQZ5pqGne6qryuSo7t58e00UyeOELlUddkbGxMVShbctuS52ZmfqOsXyvv/565TWrKL4dZnaRmV0ytQ3gUwB2AHgUwPbOadsBPNLTHYMgGDq9/OKvAvDDzl+jhQD+PaX0X2b2NIDvm9k9APYDuHvuxAyCYJAUO35K6WUAH53h818B+ORcCBUEwdzSaMruwoULk4YwpiitGuP5xqxDaYFIz9f0QojeKiyKt5hkaSHMOrqsW7eu8piny/HjeZ6Vd67XXl6oSvGmS2/evLnyGJDLd/ToUedMX5c6Yxn96sJVdIHp4yB8XR0rYNk1hMk+PZCPV/BzePPNNwcazguC4ANEdPwgaCHR8YOghTRaemtkZCSrtsr+kPpm3rRSL71SU0G9FXpKMXU+V6/LYxXq1ymeLp6fV2dlWMWrDKsxYV2ZlfHKo2n76TPkmLqXoluKPbMudVYT0tVnGI3bK6yL+tc6BZr9cR3D4hg74D8z7z1SefmZcZ6BN8bBxC9+ELSQ6PhB0ELmTTivtHADm73ebDdNw1W8hR3rVMNhSiYv66bH1ATmc9XU90KBbFYDuS6lhUQ981Dvye2gx7yUZz2X9VZ59F2oemdm+i7ve8+sNBvUa2tvYcySezY+Pt7dVpeLXVZdgERDsOzucnjx7NmzOH/+fITzgiCYTnT8IGgh0fGDoIU06uMvWrQoVYVYSj6++rDMwYMHK495qcBeai3gh1fYry9NoWRKKbvsz5YWzeTj3j31mPq+umoL44VDvXvO9F2GfVjV00ufLYXzvGnNuppPr/f03j3ADwt7i4W+9NJL2T6/b2vXrnXvyanLPMYQPn4QBJVExw+CFtKoqT8yMpLYFPNCa2r6s9nohc9KpjSjprOGYjxXgMNRajofO3as8ly9phdaU/NTTU4Oa3qZenr9votxAAAFlUlEQVQdNdE9d0iPsS7qYul1WVfVk9tMZ7tpZhzrqedqGK6qCCXguwn6XPhd0Ow7PZff41J/8mZfHjp0qLut2amaPcjmPbtqp0+fxrlz58LUD4JgOtHxg6CFRMcPghbS6Oy8RYsWZYslsr+jfrH6zezneSm7XoVbIB87UF9N/Sq+j16HfUAN+6kv7vmAmorJPrSm89ZZ3JJl0rELb/ENvaf6ody+GjrT73pjOG+88UZ3W8cRPD31Ht7MPh3v4WeoacAqA4+Z8EKvwPRqTNz2Xuo0kPvx3oxFRSvysLxeaLmK+MUPghYSHT8IWkh0/CBoIY36+BMTE5lvx36U+iZe/Fj9bU4DLvnQjMZ11V/0fF+uTKPf8+L/mp+g/jbLpL6uxrC5yq7n27FfCfgxf21bjR/zWIzqrc/MmxbLi6dqyrDmV3CbqK+7adOmbJ/b4bXXXsuOeXrrPVl2HcvQcQ++rh7TcY7Vq1d3t1Xv66+/vru9f//+SlmB6unbpapSU8QvfhC0kOj4QdBCGk/ZrVrUwAuXAblZqSY6m8saWvNCJqXKOWzueVVXSiZvnbXq2WzUe6q8fFzP9WYWei6E1waAn/Zap2oNvwdqvqvsfE81yb2Zh/oc2D1TE1zvydfRd1G/6+mt7wZ/V2Vn+fS90LRhbhN2L95+++1I2Q2CYGai4wdBC4mOHwQtpFEf38zeALAPwHIA/uqHzRLy+Mw3eYD5J9N8kefKlNKK0kmNdvzuTc1GU0rbGr9xBSGPz3yTB5h/Ms03eUqEqR8ELSQ6fhC0kGF1/AeGdN8qQh6f+SYPMP9kmm/yuAzFxw+CYLiEqR8ELaTRjm9md5rZbjMbM7P7mrw3yfBdMztiZjvos2Vm9riZ7e38X70KwuDl2WBmPzGzXWb2gpl9ZZgymdliM/u5mf2yI8/fdT7fZGZPdeT5nplVl+adG7lGzOwZM3ts2PKY2atm9ryZPWtmo53PhvYO9UNjHd/MRgD8E4DfAfBhAF80sw83dX/iXwDcKZ/dB+CJlNIWAE909ptiAsCfp5S2ArgZwB932mVYMr0L4LaU0kcB3ADgTjO7GcA3AHyzI89xAPc0JM8UXwGwi/aHLc9vp5RuoBDeMN+h+qSUGvkH4DcB/Jj27wdwf1P3F1muArCD9ncDWNPZXgNg9zDk6tz/EQB3zAeZAFwI4P8AfByTySkLZ3qWDcixHpOd6TYAjwGwIcvzKoDl8tnQn1edf02a+usAcGWE8c5n84FVKaWDAND5f2Xh/DnBzK4CcCOAp4YpU8esfhbAEQCPA3gJwImU0tQ0tKaf3bcA/CWAqaluVwxZngTgv83sF2Z2b+ezefEO9UqTFXhmmioYIYUOZnYxgP8E8Kcppbd0emyTpJTOAbjBzJYC+CGArTOd1oQsZva7AI6klH5hZrdOfTwseTrcklI6YGYrATxuZi82eO+B0OQv/jiADbS/HsCBBu/vcdjM1gBA5/8jTd7czBZhstP/W0rpB/NBJgBIKZ0A8CQmxx6WmtnUD0WTz+4WAL9nZq8CeBiT5v63higPUkoHOv8fweQfxpswD55XHZrs+E8D2NIZjb0AwB8CeLTB+3s8CmB7Z3s7Jv3sRrDJn/bvANiVUvrHYctkZis6v/QwsyUAbsfkoNpPANzVtDwppftTSutTSldh8p35n5TSl4clj5ldZGaXTG0D+BSAHRjiO9QXTQ4oAPg0gD2Y9Bn/ehiDGgAeAnAQwHuYtELuwaTP+ASAvZ3/lzUozycwaaY+B+DZzr9PD0smAB8B8ExHnh0A/qbz+WYAPwcwBuA/AHxoCM/uVgCPDVOezn1/2fn3wtR7PMx3qJ9/kbkXBC0kMveCoIVExw+CFhIdPwhaSHT8IGgh0fGDoIVExw+CFhIdPwhaSHT8IGgh/w+iWKHl978BQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29203801668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 16 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29203659cc0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.6292542          0.4487195          0.57522714          0.7577869\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204838518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 17 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208d3c668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.69477886          0.45382297          0.51497537          0.7192771\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292049cda90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 18 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292048da240>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.6117135          0.56380486          0.60937446          0.7563107\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208dad3c8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 19 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAB2CAYAAAAgPJMbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAC75JREFUeJzt3X2MXFUdxvHv07JdaZdSl1asWqkiQgpotSuKb4CUBIkCfyBgMJQErYoGiGLEoIRAVIQoxigJRU2xKJRC1MqLIpWaoNCwSCkvolSEgBBKCxILdFvrzz/uWTKOsy/de3fu7pznk2z23jtn7zlz5u4zd87M3KOIwMzM8jKl7gaYmVn7OfzNzDLk8Dczy5DD38wsQw5/M7MMOfzNzDKUbfhLOk/Sg5I2SFov6d2SzpY0fQz7ulDS4hHKXCDpnBbbZ0k6Y1frnEwk7Ux9fJ+kP0l67xj3s1zSCVW3bzJp6MsHJP1K0qy0/XWSrh/F328dYvvxkhZU3d7JZKi+GaJst6Tb0mNx0lizo05Zhr+kQ4GPAO+MiLcBi4EngLOBlg+gpKlD7S8izo+I28bYnFlAR4c/8HJELIyItwNfAb5Zd4MmscG+PAh4DvgcQEQ8FRFlnhiPB7IO/130DqArPRYrGSY7Jqoswx+YC2yOiAGAiNgMnAC8Drhd0u1QnAmks/p1wKGSzpd0dzrrWiZJqdwrZ6SSjpH0sKQ7JH1P0o0N9S6QtFbSo5LOTNsuBvZNZxCXtufu12om8DyApB5Ja9KrgfslHTdYSNKp6VXZfZJWNO9E0kWp33M9hgHuBF4PIGm+pAfS8nRJ16X+WylpnaS+wT+S9PXUr3dJ2ju9EjsWuDQdh/vWcm8mIElzJN2Q/u/vlvQ+Sa8BrgYWpv46i6bsmBQiIrsfoAdYD/wVuBw4LG1/DJjdUC6AExvWexuWVwAfTcvLKZ48XkXxCuJNafs1wI1p+QLgj0A3MBvYAnQB84EH6u6Tce7vnam/HwZeABal7bsBM9PybGAjIOBA4C+Dj8Vgvzf08yXAFYDqvm819OXW9HsqsAo4Oq2/chwB5wBXpOWDgH8DfWk9Go7bS4CvNvZt3fdvIvRt07afAe9Py28E/pyWDx/8307r/5Mdk+Eny7OmiNgKLAKWAs8CKyWd1qLoTuCGhvUj0lnU/cCHKEKq0QHAoxHx97R+TdPtN0XEQBSvNDYBe5e7J5PG4FDFAcDRwE/SqyYB35C0AbiN4ix2b4q+vT71ExHxXMO+vgbMiohPR/qvy8zuktZTnDz0Ar9tUeb9wLUAEfEAsKHhtu3A4KvReyieNGxoi4Hvpz5fDcyUtEfNbarEbnU3oC4RsRNYC6xNYb6kRbFtqRySXkXxKqEvIp6QdAHFmX4jjVDtQMPyTjLs/4i4U9JsYA5wTPq9KCJ2SHqMok9FcYbayt3AIkm9TU8KuXg5IhZK2pMixD8HfK+pzHDH4Y6GJ80sj8FdNAU4NCJebtyYRnwntSzP/CXtL2m/hk0LgceBfwFDPasPBv1mST0Uww/NHgbeLGl+Wj9pFM0Zrs6OI+kAiiGLLcCewKYU/EcA+6Ria4ATJe2V/qa3YRe/pnif5KZOOQMbi4h4ATgTOEdSV9PNdwAnAqRP8Bw8il1mdRzugluBzw+uSFo4RLlJ139Zhj/FmP9Vkh5KQw4LKMbklwG3tHrTJiL+CVwJ3A/8guIMtLnMyxSf3Pm1pDuAZyjGuIcUEVuAP6Q3kTv1Dd/d0xtj64GVwJL0iuqnQJ+kfuAUiidPIuJB4OvA7yXdB3yncWcRsYrisVgtafc23o8JJSLuBe4DTm666XJgTjq2v0wx7DPscUgxTPQlSfdm/IbvdElPNvx8geIJti+9ef4Q8Jkh/nbI7JiolOew6fiR1BMRW9OY9g+ARyLisrrbZflIH0vuiohtKcjXAG+NiO01N80mEI/3Ve9TkpYA04B7KT6VYtZO0yk+dthFMf7/WQe/NfOZv5lZhnId8zczy5rD38wsQw5/M7MMOfxLkrS07jZ0Evdntdyf1em0vnT4l9dRB8QE4P6slvuzOh3Vlw5/M7MMTdiPenZ1z4juGb0jF6zZjoGtdHX31N2MEU15/sW6mzAqOxigi+66mzGs2HPyXLZ9x/YX6Zo2o+5mDGv/fTbX3YRReXbLTubsNeS0HhPGPRsGNkfEnJHKTdgveXXP6OXgo86quxkdo2fVurqb0DEGPvCuupvQUdZeeWXdTegoU+dufHw05TzsY2aWIYe/mVmGHP5mZhkqFf6SeiX9VtIj6ferhyk7U9I/JH2/TJ1mZlZe2TP/c4E1EbEfxWVjzx2m7EXA70vWZ2ZmFSgb/scBV6Xlq4DjWxWStIhibtZbS9ZnZmYVKBv+e0fE0wDp92uaC0iaAnwb+NJIO5O0VFK/pP4dA1tLNs3MzIYy4uf8Jd0GvLbFTeeNso4zgJvTpOfDFoyIZRTTodHTO29ifvvMzKwDjBj+EbF4qNskPSNpbkQ8LWkusKlFsUOBD0g6g2Lu3GmStkbEcO8PmJnZOCr7Dd/VwBLg4vT7l80FIuKUwWVJpwF9Dn4zs3qVHfO/GDhK0iPAUWkdSX2Sfli2cWZmNj5KnflHxBbgyBbb+4FPtti+HFhepk4zMyvP3/A1M8uQw9/MLEMOfzOzDDn8zcwy5PA3M8uQw9/MLEMOfzOzDI379fwlLZR0p6QHJW2QdFKZOs3MrLx2XM//JeDUiDgQOBr4rqRZJes1M7MSxv16/hHx14h4JC0/RXHxtzkl6zUzsxLG/Xr+jSQdAkwD/layXjMzK6Ed1/Mf3M9cYAWwJCL+M0SZpcBSgGnTPTJkZjZe2nE9fyTNBG4CvhoRdw1TlydzMTNrg7LDPoPX84chrucvaRrwc+AnEbGqZH1mZlaBdlzP/0Tgg8Bpktann4Ul6zUzsxLG/Xr+EXE1cHWZeszMrFr+hq+ZWYYc/mZmGXL4m5llyOFvZpYhh7+ZWYYc/mZmGXL4m5llyOFvZpahSsJf0tGS/iJpo6T/u6a/pG5JK9Pt6yTNr6JeMzMbm9LhL2kq8APgw8AC4OOSFjQVOx14PiLeAlwGfKtsvWZmNnZVnPkfAmyMiEcjYjtwLcUkL40aJ325HjhSkiqo28zMxqCK8H898ETD+pNpW8syEfFv4AVgr+YdSVoqqV9S/46BrRU0zczMWqki/FudwTdfi380ZYiIZRHRFxF9Xd09FTTNzMxaqSL8nwTmNay/AXhqqDKSdgP2BJ6roG4zMxuDKsL/bmA/SW9KE7ecTDHJS6PGSV9OAH4XEZ6py8ysJqWu5w/FGL6kzwO/AaYCP46IByVdCPRHxGrgR8AKSRspzvhPLluvmZmNXenwB4iIm4Gbm7ad37C8DfhYFXWZmVl5/oavmVmGHP5mZhly+JuZZcjhb2aWIYe/mVmGHP5mZhly+JuZZcjhb2aWoXZN5vIFSQ9J2iBpjaR9qqjXzMzGpl2TudwL9EXE2yiu539J2XrNzGzs2jKZS0TcHhEvpdW7KK78aWZmNWnXZC6NTgduaXWDJ3MxM2uPKi7sNqqJWgAkfQLoAw5rdXtELAOWAfT0zvMln83MxkkV4T+ayVyQtBg4DzgsIgYqqNfMzMaoLZO5SHoHcAVwbERsqqBOMzMroXT4pwnZBydz+TNw3eBkLpKOTcUuBXqAVZLWS2qe6cvMzNqoXZO5LK6iHjMzq4a/4WtmliGHv5lZhhz+ZmYZcvibmWXI4W9mliGHv5lZhhz+ZmYZcvibmWWoLZO5NJQ7QVJI6quiXjMzG5t2TeaCpD2AM4F1Zes0M7Ny2jKZS3IRxQxe2yqo08zMSmjLZC7pqp7zIuLG4XbkyVzMzNqjivAfdjIXSVOAy4AvjrSjiFgWEX0R0dfV3VNB08zMrJUqwn+kyVz2AA4C1kp6DHgPsNpv+pqZ1WfcJ3OJiBciYnZEzI+I+RQTuB8bEf0V1G1mZmPQrslczMxsAmnLZC5N2w+vok4zMxs7f8PXzCxDDn8zswwpIkYuVQNJzwKP192OUZgNbK67ER3E/Vkt92d1Jktf7hMRc0YqNGHDf7KQ1B8R/thqRdyf1XJ/VqfT+tLDPmZmGXL4m5llyOFf3rK6G9Bh3J/Vcn9Wp6P60mP+ZmYZ8pm/mVmGHP5mZhly+JuZZcjhb2aWIYe/mVmG/gv/4SgF5qOz+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292036bd710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.6107116          0.5644218          0.6050484          0.7379067\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292091ca438>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 20 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208e105f8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.5967042          0.47974646          0.56515145          0.7569632\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208e3c2b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 21 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204cd6470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.6369876          0.4587311          0.48740909          0.6915267\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920585b550>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 22 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208f61fd0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.5542367          0.54093945          0.55827904          0.70667267\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208e1c400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 23 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204894cc0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.56505114          0.54841614          0.5739519          0.70202494\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2927f5998d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 24 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208fb28d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.5753959          0.49234378          0.5665609          0.7521526\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292037aa0f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 25 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292091ae978>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.6173325          0.4729225          0.5029006          0.68815804\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292035b2588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 26 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205c06ef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.55454475          0.5488378          0.5766532          0.70761406\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292058b5908>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 27 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292036c6278>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.5630735          0.5528239          0.5891559          0.70320565\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29206d78c18>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 28 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205cb69e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.5690731          0.4955197          0.57413834          0.75315523\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208ed4400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 29 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205b3a400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.60320324          0.4697662          0.50884104          0.678068\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204a0ba20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 30 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208ad7390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.547371          0.5389491          0.575545          0.6929567\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292048697b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 31 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292047f4048>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.555755          0.5435618          0.5884728          0.68845856\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204b62b38>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 32 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204a2e5f8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.56470895          0.49295175          0.5817229          0.7474533\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29203758518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 33 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208c4a320>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.59544          0.46616793          0.51626426          0.6741896\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208d2a550>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 34 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920369f208>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.53865236          0.533173          0.57530576          0.68528795\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208e525c0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 35 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29206d53f60>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.55614537          0.535156          0.5923602          0.6792625\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920a9825c0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 36 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292087c3198>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.56875044          0.49535716          0.5959705          0.7487944\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208f3a5f8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 37 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208debf98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.600092          0.47263962          0.53330004          0.6760751\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208dec898>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 38 : turn -1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208ddb080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.5442949          0.532316          0.58558524          0.6870612\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208e70b70>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 39 : turn -1\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205c06ef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.5533017          0.5298365          0.6005514          0.68137527\n",
      "Obtained reward : -1.0099999904632568\n",
      "Max steps reached\n",
      "State error : State did not changed though the action was move 1\n",
      "Max steps reached\n",
      "Max steps reached\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "Max steps reached\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "Max steps reached\n",
      "Max steps reached\n",
      "Max steps reached\n",
      "State error : State did not changed though the action was move 1\n",
      "Max steps reached\n",
      "State error : State did not changed though the action was move 1\n",
      "#### EPISODE 50 (TEST 1) MOVES ####\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29209149668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 0 : move 1\n",
      "ZPos : 1.5\n",
      "XPos : 2.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292047fe320>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         -0.0064058825          -0.016639844          -0.021032693          -0.014159033\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2927f7c4ba8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 1 : move 1\n",
      "ZPos : 2.5\n",
      "XPos : 2.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292058030f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.17744733          0.14666988          0.16159007          0.1575389\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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PiYYfBA0kGn4QNBBr1yYYDWbNmpU4owu7RdauXZsdqxlR2K5S+4evo1lr1fXCqD2mLhR2B6kdytlvdJrmAw88kJV5cQZdEFKz2nJ99borVqzIyqwJ26RAbuuqJkon00o5+4xqovA4yEg04ZBUfUfD1USvowuvsCaa4VZDZFkTHe/RcQZvbIjvU7VwCLcPHrM5cuQILly4UOnTi1/8IGgg7SyaudDMfmpme81sj5l9pfX368xsi5ntb/37waprBUEwMWjnF78PwF+llFYAuBPAX5jZSgCPANiaUloGYGurHATB+4B2Vss9AeBEa/ucme0FMB/AAwDubh32GIBtAL7mXaurqysL62TbRG0s9RGzvfvmm29m+7z0RmqzemMaalOz/13rw9N79Zq9vb1ZmX3GGlegYa5eyKVOG+b6aiiwxiQwmmJMfc2MPrenierHdVBNOHWZTjlVTbxQVtWEvwVPE62rp4mG2eo74jrodVUjroN+t/yceh1OGwZUj69U0ZGNb2aLAawF8CyAOa3/KQz8z2F2+ZlBEEwk2m74ZjYDwA8B/GVKqfwnYvB5D5tZj5n1tJvzOwiCsaWtkF0zm4L+Rv94SunJ1p9PmtnclNIJM5sLYMi+R0ppE4BNQL87j7tQS5cuLbZ5phngd9+1+8RuEe0Kel1e7T7pddWNU4bWVV2RXAft1qqbhjXRLKzes3gmjGqiXWAOZVVNNIRXXWSM6sW6qCacbcZzzwK5qaT7vMxNnibqSlOtWRM9lrMHAbkm+i3o++b9Wj9vsReFteb6qIlQRjuj+gbguwD2ppT+jnZtBrChtb0BwFNt3TEIgnGnnV/8dQD+I4CXzGxgsvN/BfANAD8ws40AXgfw4NhUMQiC0aadUf1nAJQNNd8zutUJgqAOas/AwxlUeJFKddFpdlJ2dejCm7/4xS+KbbW/9Dps36odtWTJkqy8Z8+eYlvdgrfddhvK0LGCW265pdjW6Z9aP124k3nhhReyMmty1113Zfs8TXQVIM5cq5rouWxTV4U833rrrcW2asLX9TLR6nVXrlyZ7VNN+P16mmh9NMsua6KLg3qaeFmcgFyT3bt3owydZn3kyJHSY4dDhOwGQQOJhh8EDSQafhA0kNpXy2Wf7NatW4vtquCe22+/vdjWdEu8uqr6ktXm4lVZFLYBgTw0c9WqVdk+roPaY/v27cvKbCNqfdSmZ03UB6znsibqa/Y00ZBTTxOePgvk00U9TYD8naomPH22apyDfeybN2/O9qkmPO1bffOsifrbNZSaNdFwXk8TheMygFwTfS88vqLjMJoFmPXj7y9SbwVBUEo0/CBoILV29YG8W9Ld3V1sb9++3T2PQz41o8zNN99cbGs2F+1icsiwdp+46wzk7jwNOeXZUbo4w/r160vrvmPHjmyfmgmsiaIa8XU1lNbTRDl8+HCxrZrwdQBfE30WXgB0JJpwt/xTn/pUtk814a60usD4WXQGoN6TNdEwYU8TxdNEF2Xhb6oqKxEznCxa8YsfBA0kGn4QNJBo+EHQQGq18S9fvpzZ2BwWqXaUhoNyWKTaNGz/aAZUXYGEpy1q5hnNasJuJp3+yaGhOhVSpxizvabhn1oH1qQKrpNObWVXkWqi2rL2I9FEbX52SakmvDpNlSY8hsI28lBw6LeniY7LaKiyl3lYs0WxJuraU5cda6LX5e9orLNfxy9+EDSQaPhB0EBq7er39fVlCwywS0W7T+oG4a61rnvOCzAq3lrw6tJRONJL3SkLFy4svcfLL7+clXkmnz6XLrjAddKuoCaWZE10ZqHXJdbn9jRRM4G1Vk00Gm7BggXFtmoyb968YltNJdWEzTWN8lNN+Fxe6ALINVEzQKMFWRONKtWsRKyJRlCqSeFpojMGPbh+bF6ouVVG/OIHQQOJhh8EDSQafhA0kFpt/EmTJmX2Gs+s0hBFtY14hpTOWmMbWxc/0IyybAOp7aYzvdilo7Ol1P72YNeMnqf2ItdB66Nl1kRnxnmaqKvI00Tfi6eJ3kfrxPAsTc2+pJpwnao04fECTxPF00THPXQsw3P9daIJn6vPpW5Crl8nYwMDxC9+EDSQaPhB0ECi4QdBA6nVxr9y5UqWFYV92t60QyAP49Rj2f7REEn117JtpGGaan/xPTk7MJD7i9U3r6vRcDyA2rPqw+ZrqU9Y7Ueun4a5epqor5f1VE00M42niWa84fEV1YTvqZmHVRO2zfUeqsns2e8t4ajX9TLl6HfiaaLv21t0VMejdMxpNGAb3xvHYOIXPwgaSDT8IGggNtazgJirr746cRglu0k4lBEY7FbiLp52P9n9o6Gq6hpiV4xmaNE6cLiqdufYLNC6ahYd7u6p64UzvQC5Jto91m4ca6LuT3YxqSbqYuIwWNVEZzfOmTOn2FZN1L3H+1UT7oZr1h/VhK+ryStVE34vauKwJmrWqcnAmhw7dizbpy5O1kTNCX0vnlnAx2r99J3xe+Hv6/Tp03j33Xcr+/vxix8EDaSd1XKnm9lzZvaime0xs79t/X2JmT1rZvvN7PtmNrXqWkEQTAza+cW/CODTKaXVANYAuN/M7gTwTQDfSiktA3AGwMaxq2YQBKNJRza+mV0N4BkA/wnAvwD4UEqpz8zuAvA3KaXPeufPmDEjrV69mq835DYw2KXDtrFmc2G7vZNQWkWnoDIaQqluOYYXRlTU7tRppp47RuvAmqhtztdRTdRuVxcZ04kmWnfO3qOaqA6MasJjKJqpSevA3wK7UbV+nssQyDXxwoIV3afhvayJ7mO7Xvfpe2CXLB975swZXLp0aXRsfDPrMrOdAE4B2ALgAICzKaUBh+ZRAPPLzg+CYGLRVsNPKV1OKa0BsADAHQBWDHXYUOea2cNm1mNmPe0mCQiCYGzpaFQ/pXQWwDYAdwK41swG+kALABwvOWdTSqk7pdQ9nFlEQRCMPpUhu2Z2I4BLKaWzZnYVgHvRP7D3UwB/DOAJABsAPFV1rSlTpmSrh7D/9sCBA9mxapeyHbNo0aJsH9u3N910U7aPFxcEcnuRwzuBwYtHsv9bbTc9l1GfK0+fVT+vhr2qT5vRFVtYE04FBviaKGzPjkQTDTHma6km/Nw6ZqOacE9R96km7GP3NNEwXK0fa6LxFDylGMg10V6t2uqsiWYw5rEEHcvQhTs1PVmntBOrPxfAY2bWhf4ewg9SSk+b2csAnjCz/wHgBQDfHVFNgiCojcqGn1LaBWDtEH8/iH57PwiC9xm1Z9llNxgvaKnuCp3VtGbNmtJ92mViNGSSu6fqxvKuo907LlctuMCLR2goproFWRPt7ml9WRMNl9WFKBntknv30HBVHqdRTdQsYO1VE+6y6z1VE3a1qYtOz+UusWbOYU10Np4u6sHowiHqOmVN9FtYvnx5VmZN9Nv0XKejTYTsBkEDiYYfBA0kGn4QNJBabfyurq7MLmT7rCrjLdtOGmbMNqBn0wN5uKq6WrQOvF+n93qhwRr+6WVPVTuZNdHxAE8Trw6qidaPbWHVRO/J7qqRaOKFxKomrIPa5nou2+pell2tj7pZWROdhqvXZU30e9Nvql10HEazCfF+dhG2m+EnfvGDoIFEww+CBhINPwgaSK2pt2bMmJFWrVpVlNm/XZURlcNONcSTfbcaMql+X35enZ6qtiWH+6qPmlMoqR23bt26rMw2oaaV0tBR1kTtVw1rZk3U983nqib6zlkj1UTLbGOrJjo+cPLkyWJbNWHNDh48mO1TTfhZWB9gsCbz5783SVRTtPF1tK46PsGaaDyFfiesidrmnJYLyDXR8QCur/r0VRPOAszjGm+88QYuXrwYqbeCIBhMNPwgaCC1uvOAvHvD3VMNvdSuDnfTdHovd7W0W6auGO7S6bHqKuJj9Z6ei+7VV1/Nytzd066z4mX2URcU31dddmwq6XNqN5ffiR6r4aqeJl4WJdWEZ7xVacKmnJqEek+uv6eJuuR0ViTXXbvknWjiZe9R84L3qTnmZecdDvGLHwQNJBp+EDSQaPhB0EBqtfFTSpkbh7OeqD2mWVh5Kq6ueMPTJNVe9LLWVmVL4bqq24jtdr2H2oDs9tIFF9X9w5qom1DHIFgTdmMBwCuvvFJsV7noGNVEx1o484tq4mWbUU3Y9lX9VBOdhs2oJvzdaAYe1kTvoS46RjVR1xprot+xvsN2M/B4mXyBfAygbNsjfvGDoIFEww+CBlJrV3/SpEmZu4UjlbSb6K1lrgk0udtWtWghd73U9aLdMm82F7tXtNuoEW3cHdUur5otGm3GeJlzNPmip4neg6PARqKJZgHi66om7MpSV5W3gKq64VQT/o48TbS77mmi9dH6sg5aH08TzwzV87zFN9h8VFOyjPjFD4IGEg0/CBpINPwgaCC1zs6bOnVq4uyqbE/qIhlqq7BdpeG9bK/x7Ce9h6JuGrW52GWnmU3YxaT2F89ABIDXXnut2NYFIdVVxfXV6+piErwAaSdoHTyq3EqMNwbh8ZOf/CQrqyZcBw2BVU34O1FbnF2l+lw6i5PfQ1UmJK6Dfrf6LHwtb7xCxyD0W+Xrssv14sWLuHLlSszOC4JgMNHwg6CBRMMPggZS+7Rchu0o9Vt6/kg91ptCqdNyeb/GA+iCkTz+odflumtd1ffN4aBqH3o2voaRrl07aCWzttB7dsJw7XZg8Hsq47Of/WxWfvLJJ7My27ea0Ua153ERDTfmd6ixDRrGzOMgGguievJ3UvXMfKzWj78bHZ9Qm3+ktP2Lb2ZdZvaCmT3dKi8xs2fNbL+Zfd/M2h8xCoJgXOmkq/8VALwA2jcBfCultAzAGQAbR7NiQRCMHW31Ac1sAYD/AOB/Aviq9fu9Pg3gz1qHPAbgbwD8fcV1MpcZd680gaa6shjNXMLdNnVPanee0TXH9bocxqluQXYpqhuQwzKB3G2jZoC61lgTNQPaDcdUvNBQoP0ZXZ3ihV1799T6eppoN5zfk5dsU+/vfVP6jjRLEX9jVVrz+6+qA6PHcgYj/r48zZl2f/G/DeCvAQwYMNcDOJtSGvgSjwKYP9SJQRBMPCobvpmtB3AqpcTrLg/1v7Uh/xduZg+bWY+Z9YxkoCgIgtGjna7+OgB/aGafBzAdwAfQ3wO41swmt371FwA4PtTJKaVNADYBwLRp0+oLEwyCoJSOQnbN7G4A/yWltN7M/hnAD1NKT5jZdwDsSin9H+/8q666Ki1durQo83THQ4cOZceq+4xtF3W7cSYYdYGpW4TtIV4YEcjtJgA4duxYsa32tR7L6HgA26heJhog12TXrl3ZPm+xUMVzcer4CV9H7+FND9X7a9k71kPDU3ksZu7cudk+HRtim3/evHnZPv5O9B2p25ez46irT78//k5Ua30Wnp589OjRbB/3iFV3rS8fq9mWL1++PKYhu19D/0BfL/pt/u+O4FpBENRIR5EdKaVtALa1tg8CuGP0qxQEwVgTIbtB0EBqD9llW/mll14qtr2VagBg+fLlxbb6br10Veo3ZxtM7Xa1F9nO0vEAtlnVjtPMr7yoodaHp4oCuSaej78TPJseyO16b5/SybHthu8Cg/XkMmfKBQZ/JzxFVp/bS/Wm75e/E/VG9fb2ZmW2sXXKOC9sCuTvX+vObUBjBTRkl6eJc/0iy24QBKVEww+CBlJ7ll3u2vJMK10jXbv+3L3XhQjYbaMznnSRRS+bC3fJgdzdol0v7jZqmOStt96aldmk4MU/gMFdfdZE3T0jmWXHdOIW7KQ7P1awyXPjjTdm+/S7YT01XJu/E32f+lxsruk3pRmD+T3pO/LCwGfNmpXt42+8yjQqC2lv120av/hB0ECi4QdBA4mGHwQNpFYb/8qVK5nNw3aUhjqqq43tH7XH2IWiNpbaZ2wbaZZTXaiTr6XTQbmsdddpuWyjqltGbUDWZLRs+iq8kF2PTo71pllXwe9XtdU68DvUd8ZuQR2XUdcpa6/HasZlPla/W32/bLvrd1MWhgsMfm4+Vscr2iF+8YOggUTDD4IGUmtXP6WUdYXYZacRT+rO48gqL+uKdsu8xTjVneItfqCuPs8tqNfhrliVScPPot3YTtxwoxVx553byXmdRO55GXj0O9FISDalvMVAtAuuXX0vA4++b/5O9J4abcnBWbmLAAALXUlEQVT11brzt6p66XXY3OBn1muWEb/4QdBAouEHQQOJhh8EDaTWRTNnzJiRdEHJATTjrcJ2n86iY6rsYqbK7vRmw7E9ptlblE6y43J9dZafauQ9m5ffcCSuNaaTrDqdfGc6RsJhup1cR7MxMbyQaRVV30kn4xfeN7V373vZ6/Ud6fvkMo9/vfPOO+jr64tFM4MgGEw0/CBoINHwg6CB1OrHN7PSMFQNn1V/LaMhipqxhRmJreutbLJ48eLSffosnj+5k7UGRitE1sucW0Undn27aH30Ob2QVG/8RMcK+LrXX399tk/fmUe7vnJg8Pv1vqklS5aU7tPpxyMlfvGDoIFEww+CBlJ7V5+7bbqIhh7LeG4QPlbDebUbyeGXVaGY3AXWfbpQB6NuN69rqPVlfaoWrGi36z+a7iimykQYLbOANdGusupXdh4wOAyc0UUy9u/fX3qsfgscMqumhx7rzSRltO4rV67MyvzuuR1Fss0gCEqJhh8EDSQafhA0kFpt/EuXLmWZTz03jbpBPLcN20q6MILCdqlO4dV78AKNnrtHz9O6c/3UrvPszqAfXWiUUbdcu67TquxGvHipd38g/4703es4B3+f3hhJla3O12XXsjc2wcQvfhA0kLZ+8c3sMIBzAC4D6EspdZvZdQC+D2AxgMMA/iSlVL6WVRAEE4ZOfvH/IKW0JqXU3So/AmBrSmkZgK2tchAE7wPampbb+sXvTimdpr/tA3B3SumEmc0FsC2ldLN3nalTpyZe1JBRG9qzf7zspIpOmfXGCtSvz2gcgefH1/p5drwXbtxJLIPCvnnVspNpwnWgcQSeb17xbHrV3dNaYy/Ybtf3oJmSvetoRl5Gxxm4rJpoWVPBDbB9+3a8/fbbozYtNwH4dzPbYWYPt/42J6V0AgBa/84e6kQze9jMesysZ7iBIkEQjC7tjuqvSykdN7PZALaY2SuVZ7RIKW0CsAno/8UfRh2DIBhl2mr4KaXjrX9PmdmPANwB4KSZzaWu/in3Iv3nl7rFdPFIdbW1u6+sCzRAJ4tUcNfac+lophftWnPXtWoWGGebqXIjDRfVYLhZdqtoO3xU9PLWrldN1MzjBS29bMwa+qvXveGGG4ptz5xQ9J66wCYvmql15+dU7byFV5h2zbjKrr6ZXWNmMwe2AdwHYDeAzQA2tA7bAOCptu4YBMG4087P3xwAP2r9n34ygO+llP7VzJ4H8AMz2wjgdQAPjl01gyAYTSobfkrpIIDVQ/z9LQD3jEWlgiAYW2oN2fXQEX+1Q3lqq2dznThxwr3PihUrSvd1kimH7VC224DBLh0vZFfhxRFHkg13vLPq6HU9j06V64pDu6syFr311lul+/i9qGtUx170nTK6uCo/Z3d3tx6eMX/+/GJbs0Wz3V71/spC09t97xGyGwQNJBp+EDSQaPhB0EBqtfEnT56MOXPmFGUvk67n71b7hn2cGnarKZXYfvQynuqxGmfAtppnVwKDbUKG/fZKma92oqK+Z7bHOwkTVhuffez6Hjz4HSlV74xhn/5QZf6ODx8+nO1btGhRVuZn09WavfELfRbWmseF2h2viV/8IGgg0fCDoIHUumjmlClTUtlChlVdQe7Ce7Pd9Dr6fBpC2W4dPDecmgzqFvRmeunsQT5WXTOdzM4bK1hPr2uvZU9bfU7Vk5+7avGNdrPsdPLOlGnTppXWoSqjkqcDm8FV7aEse9WOHTtw7ty5WDQzCILBRMMPggYSDT8IGkit7rxJkyaVumM8mwrwbXN2/an95dnFGlqr9rfnTuPpl2pn6j05Y4valmrLsfvn6NGjpfcf6r5ljCQk17Ohq0JtGS8cuuo5WOuqLMr8Tr33qTayl/Wnaqo3h5Prs7Ddrug9+Tr6bSplY2XtTjuPX/wgaCDR8IOggdTqzps8eXLirprXbdMuMHeDNHKKu5FV0XiMRg5q908jqxjuvmv3qre3t/RYfWbtHvOz6PrunUStjdV75frqO/K6/p3URxcZZTNPk5yqK5A106SY3vemx/I7PXDgQLZPv5Mq84NhU0mjU3kBFzVTPFOYE3oePnwYFy5cCHdeEASDiYYfBA0kGn4QNJBa3XlTpkwpnTHFi2kC/mID6spg+1FtI7Wj2FZSl4nO5OMwYb0Oh2by7Cggt9UA3x106NChrMz2o2e/jhWdLNbYCd55ek+dYcllHd/xQlt1AQ0e/9Hr6D35fS9dujTbp9l5+D1Vuf74favrmRcArdKZZxfyWFS7GYHjFz8IGkg0/CBoINHwg6CB1O7HZ3vEs1nVV8o2mPp5582bV2zr85w8eTIrs41f5X/1wn05K4zWR89jW05tUl1UkfXR1V00RoHHErxwWQ091vp64aG6j+1btaE1zsBbhJTP1XgKTz8da1m4cGFWZj+5jhvxc3sxGorWR98ZX1c1UDue66eZmfhcPc+LT+Ew75deegnnz58PP34QBIOJhh8EDaT2rn5Z91m7jZrJhLvIeg129Wn3qSxTCTC4++Qt3qghndy11swzek/NssPoDC3u9uqzqJnAXWnPHaVo/VgHdSOpJqyDfjuea027rnwdratqwufqd6LPwvVXTdi09DTQ66hrWc/1zCz9Nvi59TpcP62PHlum9enTp3Hp0qXo6gdBMJho+EHQQKLhB0EDqdXGN7M3AbwG4AYAE2m1iKiPz0SrDzDx6jRR6nNTSql8lZYWtTb84qZmPSklf1nRGon6+Ey0+gATr04TrT5VRFc/CBpINPwgaCDj1fA3jdN9y4j6+Ey0+gATr04TrT4u42LjB0EwvkRXPwgaSK0N38zuN7N9ZtZrZo/UeW+qw6NmdsrMdtPfrjOzLWa2v/Wvn0ZldOuz0Mx+amZ7zWyPmX1lPOtkZtPN7Dkze7FVn79t/X2JmT3bqs/3zax86t3Y1KvLzF4ws6fHuz5mdtjMXjKznWbW0/rbuH1Dw6G2hm9mXQD+N4DPAVgJ4CEzW1nX/Yl/AHC//O0RAFtTSssAbG2V66IPwF+llFYAuBPAX7R0Ga86XQTw6ZTSagBrANxvZncC+CaAb7XqcwbAxprqM8BXAOyl8njX5w9SSmvIhTee31DnpJRq+Q/AXQD+jcpfB/D1uu4vdVkMYDeV9wGY29qeC2DfeNSrdf+nAHxmItQJwNUAfg3g4+gPTpk81LusoR4L0N+YPg3gaQA2zvU5DOAG+du4v69O/quzqz8fwBEqH239bSIwJ6V0AgBa/86uOH5MMLPFANYCeHY869TqVu8EcArAFgAHAJxNKQ1MCav73X0bwF8DGJgGd/041ycB+Hcz22FmD7f+NiG+oXapM8vuUFMFw6XQwsxmAPghgL9MKb0zkoUuR0pK6TKANWZ2LYAfAVgx1GF11MXM1gM4lVLaYWZ3D/x5vOrTYl1K6biZzQawxcxeqfHeo0Kdv/hHAXCepAUAjtd4f4+TZjYXAFr/nqrz5mY2Bf2N/vGU0pMToU4AkFI6C2Ab+scerjWzgR+KOt/dOgB/aGaHATyB/u7+t8exPkgpHW/9ewr9/2O8AxPgfXVCnQ3/eQDLWqOxUwH8KYDNNd7fYzOADa3tDei3s2vB+n/avwtgb0rp78a7TmZ2Y+uXHmZ2FYB70T+o9lMAf1x3fVJKX08pLUgpLUb/N/N/U0p/Pl71MbNrzGzmwDaA+wDsxjh+Q8OizgEFAJ8H8Cr6bcb/Nh6DGgD+CcAJAJfQ3wvZiH6bcSuA/a1/r6uxPp9Afzd1F4Cdrf8+P151AnA7gBda9dkN4L+3/v5hAM8B6AXwzwCmjcO7uxvA0+NZn9Z9X2z9t2fgOx7Pb2g4/0XkXhA0kIjcC4IGEg0/CBpINPwgaCDR8IOggUTDD4IGEg0/CBpINPwgaCDR8IOggfw/0ij0txPTeA8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208dc1b70>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 2 : move 1\n",
      "ZPos : 3.5\n",
      "XPos : 2.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292037c9e80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.58364743          0.5115441          0.50363594          0.5187611\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29203674080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 3 : move 1\n",
      "ZPos : 4.5\n",
      "XPos : 2.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205621f60>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.9323906          0.834167          0.82854277          0.8393517\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205b91358>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 4 : move 1\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208f9a470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         1.1281135          0.9955444          1.0132586          1.0248151\n",
      "Obtained reward : 0.9900000095367432\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "Max steps reached\n",
      "State error : State did not changed though the action was move -1\n",
      "State error : State did not changed though the action was move -1\n",
      "State error : State did not changed though the action was move 1\n",
      "Max steps reached\n",
      "Max steps reached\n",
      "Max steps reached\n",
      "State error : State did not changed though the action was move 1\n",
      "Max steps reached\n",
      "#### EPISODE 0 (TEST 2) MOVES ####\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208c1a1d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 0 : move 1\n",
      "ZPos : 1.5\n",
      "XPos : 1.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205b00240>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         -0.01023468          -0.010384083          -0.014136972          -0.015700953\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208db9400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 1 : move 1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204bb81d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.12438382          0.11598678          0.11986804          0.11963372\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292047e44a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 2 : turn 1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204845898>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.37339154          0.36374015          0.3975409          0.38160807\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920900c668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 3 : turn 1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292036e0c18>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.5649372          0.5973622          0.67457235          0.6011988\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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W8NFGAHjooYeiPheM0OwyqZDdsWPHRn3OkHL33XfnPk/np2h2HJ7fqFGjorHLLrss6nPBCoWvw8KFC6MxtVG5WIm6n84666yoP3fu3Kx9yy235L4/ENubmjmHUVeuzo9Du1PaA/HnrkZ71ZvR7Dgp/WuhPX9mPXKdh3/jO04BqaRo5rFmtsLM1pvZOjP7dunnfc3sUTPbXPq/T7nXchynY1DJN/4BAP8aQjgZwBQA/2xmowAsALA8hDACwPJS33GcvwMqqZbbCKCx1N5rZusBDAJwEYAZpYctBLASwDUVvF7W3rdvX9bu1Cn+G6Q2Iftg2a8LxDai2r16zJTTOK1evToaU780o9l72V87bNiwaGzr1q1Rn21W9YvrMWL2Yau/XX27jY2NufNl+1qPiuox4sceeyxra8onzSDMr6V2qL7uww8/nLVTx1zff//9qP/hhx9G/Uq1B2L9q9E+VURTffWsf0p7INa/vbRnrTUGIo9W2fhmNgzAOACrAAwo/VE4+Meh8gPMjuPUlYoXvpl9BsA9AP4lhPB+ucfT8+abWYOZNegdd8dx6kNFp/PMrCuABwA8EkL499LPNgKYEUJoNLOBAFaGEEamXqdXr17hxBNPzPochliuaCZnd1FXDIeK6tZUXSbsttHPrnXkGQ41BmIXim55NVMth3/yKcOW6NPn03ukI0aMiMbY7QY035YzvJXl01tAc3ODr4OGFKc+96RJk6IxPl2m78Pb7HKoWcDb7pT2QKx/Ndrr52b0C4z1T2kPpPVvq/asdbudzrOmK/BrAOsPLvoS9wOYV2rPA7C03Gs5jtMxqCSAZxqAywC8YmYHE5X/TwA/AnCXmV0J4A0A6YPmjuN0GCq5q/8UgLytwzntOx3HcWpBXUN2ORuPunQ4TBMARo789PaBHh1lG0tdOApnadXw1Isvvjj3eevWrcsdK5dtdubMmVlb711ouCfbbmoX33fffVF/6NChuXNi+1rdY5s2bcp97OTJk6MxdU8xN954Y9RX25xfK+XOW7FiRdTX34WTTz45a6e0B9L6t0b7lN4K65/SHoj1T2k/fvz4aOyuu+6K+nxtuTipZqvOw0N2HaeA+MJ3nALiC99xCkhNs+x26dIlsO3O9o7aahqaySG9O3bsiMY4vHf27NnRmB6L3LBhQ9ZWW1LtW0ZDgdn+1kqwWhGX00Vpmin1hT/77LNZO+UXB5r7opmpU6dmbbXxNYMrh3yyPQ00DwFlP7SmS9MMx5wGS+/ZMCeddFLU11gMnr+G1mpoN+tfjfapI89674X1T2kPxPqr9hzXotdWNePPzUd2v//97+P111/3LLuO4zTHF77jFJCaF83krQ+7HsoV+2M3iW7vzjnn03AC3ZqqO4rNBM0uk3JdsTsRiMM/NXuLblU5/HPChAnR2JYtW6L+W2+9lbW1cIOaCamii7zNVpNBT0LOmTMnd+7qVuITeBrWqplqefzzn/987lzVdcbXAIh/N1LaA7H+1WivejOqN/dT2gOx/qo9c8opp0R9/dysPWfnrdR09298xykgvvAdp4D4wnecAlJTG/+TTz6J3CTsvlAbJpWd1HGc6vBvfMcpIL7wHaeA1LVoJicpLFcPnDObaDYSdldp0UxFM/0wvXr1yh176aWXcsf0PTWCjaPWtMijusvYxfnmm29GY1/5yleivp4KZDQ5I6ORhoxe92eeeSb3sXrSUKPhONIwVeDzzDPPjPp6wmznzp1ZWyMz1VWZ0r812qf0Vvg9U9oDsf6qPc9P318/N2vPWnMC2xT+je84BcQXvuMUEF/4jlNAano677DDDgscssjZStXG19BbLWLA8MkqtYv55BkQny5LhWUqaquxPasZWjUk9vzzz8/aTzzxRPJ92GbVz6yFHM8999zc1+FwUA2H1tNl48aNy9oaYqphwnxqjDPaAM1t80svvTRra/agFHxCEYhDUjlLM9D8VB3rX432qdOEei+D9U9pD6T159fVLLsKa89a33vvvdi1a5efznMcpzm+8B2ngPjCd5wCUtcsu1zhRUN2e/bsGfX53oBmolm6tG21PHbt2hX1U/cRnn766ajPPuyLLrooGtPKNWyDDRo0KBrTI6h8xFMz0+gx3Ntuuy13vq2B56Dze/DBB6M+Z0a66qqrorF333036vP9iuOPPz73/bX6jGbK5d8FPYZ7qLRXvRmNX2D9U9oD8fVV7bkq0Nlnnx2N6TXJ014Lcebh3/iOU0B84TtOAan56Tx2LXE2HnWfaOgjb8XWrFkTjfEWiZNMtvQ6fCJw2bJl0ViqaCYXLQCAuXPnZm3N3qIhsfzZ1BWkW2AuEKmZXp577rmonyqaOWvWrKytxSM1LHfRokVZW110WiBi/vz5WVsLc+h14O2pJo9kNNMQZ/kB4sKTmq1Hi2ay/tVor3ozrD0Qf+6U9kCsv2rPrkj9XBrqzdqz1tddd13uvBn/xnecAlJJtdweZvacmb1kZuvM7N9KPx9uZqvMbLOZ3Wlm3cq9luM4HYNKvvH3A5gZQhgL4FQAs8xsCoCbAPwkhDACwLsArjx003Qcpz2ppFpuAHDQWOpa+hcAzATw30s/XwjgOgD/t8xrRW47bqt7Rd17q1evztrqztu+fXvWVpeS2lycAUjtKA7pVLQoxeLFi7O2usDUruPn6tz1+CXPT+3OE044IeqrG4zho6ypAhpAXLyU7zHofADgZz/7WdZWG1U/Gx8RTblK1X7V+bENrfccWHsg1r8a7VVvhrUHYv1T2gPxNVLtOTT58ccfj8bUncfas9Ya6p5HRTa+mXU2szUAdgJ4FMCrAPaEEA7OdDuAQXnPdxynY1HRwg8hfBxCOBXAYACTAZzc0sNaeq6ZzTezBjNr4L9ojuPUj1bd1Q8h7AGwEsAUAEea2UFTYTCAFvfJIYRbQwgTQwgTtR6e4zj1oexKNLOjAHwUQthjZj0BnIumG3srAHwBwGIA8wCUjZ08cOBAlF6I/fh6PDhVNFPDItl+VNsoFcKoYcF67JUZMmRI1Gcfth5z1bRX7DdXm1SLKvJz9Yis+sK1gCRzzz33ZG21mfX+Cc9v+vTp0Zja8S+//HLW1qoxGsrKR3o3b96cO1eNK2B/OxDbySntgVj/arRXvRmNX2D9U9oDsf6qPccr6PHelPastV6fPCr5Ch4IYKGZdUbTDuGuEMIDZvZHAIvN7H8DeBHAryt6R8dx6k4ld/VfBjCuhZ+/hiZ733GcvzNqmoGne/fuYfDgwVm/f//+WVvdcDov3hbpCa0UXBMdiLdlF1xwQTSmLkVG3SmMuo3UJTZ69OisreGzugXm+apr6Prrr8+dQwoNcdYt+uWXX5619cQYa6Som1BDbadMmZK1tW48o26tF154IepzmOvXv/713NdRqtE+pbfC+qe0B2L9VXs2wQYMGBCNpQqkMjfccAO2bdvmGXgcx2mOL3zHKSC+8B2ngNTVsc5uEbWFFL4HoLYb2/8aYpo67rtixYpoLFVJJ2XzqV2nmWkfeuih3NdJ3a/QMFI9MqsuKYZdWRpKqy6fJUuWZG09Cpyy8dWmV7v09ttvz9op16Par+rGZJ1S2gOx/tVo3xobn/VPaa+vm9J+7dq1UT+lPWvtGXgcx8nFF77jFBBf+I5TQGpq43fq1CmyTdjW1BBFTVHFj92/f380NmrUqNz31GypbIPpoSG9P8BotlkOZVW7Tiu49unTJ2uXi0Fgf7KG2qrdx37yFA8//HDU1zBXvg76nmynA3GaLLXptXovp6+q1A8NNI8lYF30fsqh0l71ZjSMmV83pT2Q1p/9+hr3oLZ7pdrn4d/4jlNAfOE7TgGp6Vb/o48+ik4npVwmqdN6Xbt2jcY45FNP9ek2fNq0aVmbT0MBzUNZGXUb8bZbt3P6npwFVd1RehKNwza1CKXOQTPXMHyNdGt/4YUXRn124TU0NERjeo14O6+vq30umqlhuHnvD8TFVIHYDExpD8T6V6O9XmtGTS7WP6U9EOuv2vPn1t/jlPZ8Tdyd5zhOLr7wHaeA+MJ3nAJSUxvfzCJb5cQTT8za5TKHsE2mrgzOLKr3Bp566qmoz+4gzVqrNhczYsSIqD9p0qSsvXz58tzn6fwUdZ+xO0/txfPPPz/qq1uTYXtRw0bVRh0zZkzWVntbM9PMmDEja9999925719ufoweVVZ3Hmf2Of3006MxvbasfzXaq94Maw+k9W+N9pw5d+jQodGY3ivga8taq7szD//Gd5wC4gvfcQpIzbf67JrhbZBm4NHIvVRBBsdxWod/4ztOAfGF7zgFxBe+4xSQumbg2bFjR9ZWN5wWfeBwSw1nZHdQqkghELuK1q1bF42lss1oJl12e2l2mcbGxqjPJ7a0wIKeCuPPzW42oHnYJhcnUfh1NTRaC4BwQVL9LOoe4nstw4YNi8a4uCUQh/eqDoyGmar27HrTghXqCkwVKG2N9qo3oy5PvmYp7YFYf9WeTwxqWLX+zrP2/DqVlqnzb3zHKSC+8B2ngPjCd5wCUtNKOt26dQv9+vXL+hwmqbaaHr/kbC6aAYVfU+3Mffv2RX3OpqqfXZ/L6BFZDpMcPnx4NKYhp5x1KFWtB4htS7VRL7vssqh/zDHH5L4OHzPVa6s2Kl+HjRs3RmOa8ZY/t2a/4ZBTIA6z1ozBKbRCD8d0zJ49Oxpj7YFYw2q0V70ZPTbM+qe0B9L6c1Zgvdei93tYe9b6nnvuwc6dO9uvko6ZdTazF83sgVJ/uJmtMrPNZnanmXUr9xqO43QMWrPV/zYAzlZwE4CfhBBGAHgXwJXtOTHHcQ4dFW31zWwwgIUAbgDwHQCfB7ALwNEhhANmNhXAdSGE/5Z6ndRWX08q6WkpLrihxQV4q6XbY3WZ8FZWHzt16tTcuadOPanLhs0SADjttNOyttaQ15NdvBXULbpuP1NFKnhbqyfE3nzzzajPLibdvqe26OoW1ExE/FrqjmKef/75qJ8K19ZroGYWa1qN9pWecgNi/VPaA7H+qv2gQYOy9siRI6MxPWnI15bdtXfccQd27NjRblv9nwL4HoCDDtbPAtgTQjjoNNwOYFBLT3Qcp+NRduGb2YUAdoYQ+M9yS39RWtw6mNl8M2swswYNzHAcpz5UErk3DcAcM5sNoAeAw9G0AzjSzLqUvvUHA2hxTxhCuBXArUDTVr9dZu04TlW0yp1nZjMAfDeEcKGZLQFwTwhhsZn9AsDLIYT/k3p+t27dAhdhYLfcO++8Ez02ZRNqsQHeSZxxxhnRmNq37LbhIopAXCxC0VBgdtto6Kza5mzXqStNi4VySKoWdty7d2/UT+2g+LNoGCeHSgOxLayZX/S5/FnVFlf3GWuYKkiqdrq6cnkOKe2BWP9qtFe9GXXZ8TVJaQ/E+qe012Pqqhl/bnZx/uY3v0FjY2P7ufNa4BoA3zGzLWiy+X9dxWs5jlNDWnVIJ4SwEsDKUvs1AJPbf0qO4xxqPGTXcQpITY/lHjhwADt37sz6bMeo/1PhcbWp2Veq4ZRqj/G9hAkTJkRjXOVHUTuUbVY9npryAaufXMNTGc0oqyGxqQKMnOJM56MFSs8666ysraGr6k9W3z2j1Wg43FfvvTDlfOZ8LyGlPRDrX432qjej9ytY/3KfhfVPaT9kyJCor/cyWPtUluk8/BvfcQqIL3zHKSA1z7LL7o3UCSjNyrJt27asfeqpp0ZjXFdcXUGKukmYlMuJ30NR16POncOPtXjkSSedFPW5sIhu7XXrr6HCTOo6pDL36DXQopSp91AXGZ82SxX4HD9+fHJ+lWrf0pyY1mif0lth/VPaA7H+7aW9F810HKcifOE7TgHxhe84BaSmGXg6d+4c2JZiG19DWdUe03GGwy01O6oezeRjr+qG02wzjNpqbM+qC0zdZWyf6RFUhV9XXTrK5Mn58VN89FaPPOtRW3XZMWynA7FrTbP16OfmIp9PPPFE7nsoGgqcyhyrobasfzXapwqo6r0M1j+lPZDWv63as9bPPvss3nvvvUMasus4zt8pvvAdp4DUdKvfqVOnwK4Hjo7iiL7SY6M+R1ppUYqVK1e2aT76Olp4gtmyZUvu/GbOnBmN6VYw5T7TU1e8xdRtomaNWbp0ae7rtoa+fftmbS1Oqi5DdsfV/c+kAAAMSklEQVRedNFF0Ri7o4B0UQpGI+w4Saf2a6W96s3o7ybrX2/tt2/fjv379/tW33Gc5vjCd5wC4gvfcQpIXYtmMnr6SO89cAZXdSOxXaqFBzSEkQscPPPMM9FYqqCGZpDl01HqHlO7jm1UtQEHDx4c9dkuVnt7zZo1UT9VUIOzxup7qqtq2bJlWVuvrZ6anDt3btbW66U2PbsC1UXHaIEUzQJcqfZArH812qvejJ6KZP1T2gOxFu2lPWv929/+NnfejH/jO04B8YXvOAXEF77jFJCa+vF79uwZOFSS7R0Ny9RwS7aH1G7iTCZjx46NxrRIIWdP1eO0GktQKZw5GGheKJE/W48ePaKxTZs2RX3OpqLhqOrLVXuXYT9wuTBmvg6tKW7JlV+A5uG9fK31czN69Fd/Fzh2IKU9EOtfC+2BWP+U9kB8HdpLe9b6vvvuw65du9yP7zhOc3zhO04BqelWv3v37iHPBaXbUZ0XnyBT1x9vp3R7t3///qjPz9VtmWZEYfr37x/12eWkiRp1G8nbU/2cugXmvrq59HOnCmrw/MplZeHroKaSnhLj7akm3uSCEEAcFptKZKrXQLPhsL4p7YFY/2q0V70ZdTfyZ0tpD8T6t5f2+rvoIbuO47SIL3zHKSC+8B2ngNQ1ZDeVWSWVgddxnOrwb3zHKSAVfeOb2VYAewF8DOBACGGimfUFcCeAYQC2AvinEEJ+4nLHcToMrfnGPzuEcGoIYWKpvwDA8hDCCADLS33Hcf4OqMiPX/rGnxhC+DP9bCOAGSGERjMbCGBlCCE/XSuAbt26BQ5vZD/r3r17o8dyOiggTnekhRJTcBUWIPa5Tp8+PRpLVVpJZfnlYoxAcz/viBEjsrYekdWQU/bP6jVIFclMsWHDhqiv/vcLLrgga2scRCodmcYkaMHI0aNHZ+333nsv93W0ao36rHfv3p21a6V9Sm+F9U9pD8T6Hwrt77jjDuzYsaPd/PgBwDIze97M5pd+NiCE0AgApf9bjHgws/lm1mBmDamAE8dxakeld/WnhRDeNrP+AB41sw1ln1EihHArgFuBpm/8NszRcZx2pqKFH0J4u/T/TjO7F8BkADvMbCBt9cseb/rkk0+iLX2/fv2ythY71JNeqQIW/Fw+4QQ0D83k8NVVq1ZFY6mimamtn27v9CTVU089lTuW2rrqFli37KkTb3wCL3UtAWDFihVZW69BaquvW3vdnj700EMVvU65beySJUuydkp7INa/Gu1bs9Vn/VPa63h7ac9a68nLPMpu9c2st5kddrAN4B8ArAVwP4B5pYfNA9A+uZ4dxznkVPKNPwDAvaUSQ10A/L8QwsNmthrAXWZ2JYA3AHzx0E3TcZz2pOzCDyG8BmBsCz9/B0DbbjM7jlNXanost0uXLoFtHLa59Pin2uqcvVRt2xNOOCFrq42jFVG0gorML3dMC0+mihqm3rOcO4pdUJoVRm3WUaNG5b4OXwethqO2OMPZboDm2XH1uCij78Nan3feebnPU9S+ffLJJ7N2Snsg/tzVaK96M23VHkjr31bt+TM//fTTXjTTcZyW8YXvOAXEF77jFJCaHsvt1KlTlJ4plRLq448/jvpsg6nvln2cWslUbaxx48ZlbbWb1C/NqG3Jtpy+h/anTZuWtdUPrXPgey5qd+rn1uy0DD9XbXqeDxCnh1q/fn00pp977dq1WVurzej7zJo1K2urH5rRyj5t1R6I9a9G+1SMRMqOT2kPxPq3l/b8WE03lod/4ztOAfGF7zgFpKZb/QMHDkRuOd4aajYePS3FWyg+9QXEW0M9CKTbYQ6vVLeMbjkZLgQCAKecckrW1vBPJZVpSM0dPrE1cODAaGzKlClRX12eDG8bNWxUt7XsEtNroKfLJk2alLWXL1+e+/7l5seo6+zVV1+N+rwlTmkPxPpXo73qzbD2QFr/WmjPWutJ0Tz8G99xCogvfMcpIL7wHaeA1DXLLlcraWxsjMbUVmdXjLo62K7hwohAc9uN7zFoJprUUcwdO3bkPpaPF+t7AHEIqh7/VDuYP7e6gvRzp7La8HXQo6JvvfVW1F+3bl3W1goymmWHP7faoaohh/CmwmU1zFrt4kq1B+LPXY32qnfqsax/Snsg1r+9tOfPrPc88vBvfMcpIL7wHaeA1PR0XufOnQNHRA0fPjxr65ZSI5DY9aeuGI4g0/ru6iri7Z9+9lRteI0I5G2Z1onXbSJHK+7Zsyf3PXR+akLoyTgdZzhCbN++fdGYmht8HTSyUAub8FaS9QOamxDshtMknik0YxAn20xpD8QaVqO96s2oGcr6p7QH0vq3VXvWzE/nOY6Tiy98xykgvvAdp4DU3J3HthNn3NXwRbWx2HXE7icgtrH4pBnQ3KXD9pC6mCZPnpw7b7VfGT2tpXYnh3jqKSsN92R32rHHHhuNrV69Ouqrjc2oC4/ZuHFj7mM1423qxKJm3Dn66KOj/sSJE7O2ZvZhNPRX3YRsJ6e0B2L9q9E+pbfC+qe0B2L920t71i91b4Lxb3zHKSC+8B2ngPjCd5wCUnM/PvuQ2V+r/k21CTlsU+8HHHXUUVlb/bxajJN9woralqlrw8eG1fet/mP+zOUy5fLn1gy3elSZfbkTJkzInateWy4eCcQhsiNHxnVP1WfNYbl6r0Ar9KSOObMtrplnVfulSz+t1VKuuOpJJ52U+1jOXKufa/Dgwblz1cdqqDSH/2r8iWby4b5eaw7L1WupsRgM/84vWrSoXYtmOo7zXwhf+I5TQOp6Oo9RNwS7goB42zZ+/Pjc19FtrG6XGS3GoFs4LvSo22Wej5oEenpr6NChWbtUiixDT3MxqZBcABgzZkzW1u2ouhgZ/SxsJugJO81Ew6aKupy03jujbkzO7FPOdcbbY92+aygrn3hTU4O30qmTjUB8jVQH/SypAiV6epDNKjU7eX76e6K/Uzx/vu56OjUP/8Z3nALiC99xCogvfMcpIDV155nZLgDbAPQDkG/c1h6fT5qONh+g482po8xnaAjhqHIPqunCz97UrCGEMLH8I2uDzydNR5sP0PHm1NHmUw7f6jtOAfGF7zgFpF4L/9Y6vW8ePp80HW0+QMebU0ebT5K62PiO49QX3+o7TgGp6cI3s1lmttHMtpjZglq+N83hNjPbaWZr6Wd9zexRM9tc+r9P6jXaeT7HmtkKM1tvZuvM7Nv1nJOZ9TCz58zspdJ8/q308+Fmtqo0nzvNLD+lzqGZV2cze9HMHqj3fMxsq5m9YmZrzKyh9LO6/Q61hZotfDPrDOA/AJwPYBSAL5vZqPSzDgm/ATBLfrYAwPIQwggAy0v9WnEAwL+GEE4GMAXAP5euS73mtB/AzBDCWACnAphlZlMA3ATgJ6X5vAvgyhrN5yDfBrCe+vWez9khhFPJhVfP36HWE0KoyT8AUwE8Qv1rAVxbq/eXuQwDsJb6GwEMLLUHAthYj3mV3n8pgPM6wpwA9ALwAoDT0BSc0qUlLWswj8FoWkwzATwAwOo8n60A+snP6q5Xa/7Vcqs/CMCb1N9e+llHYEAIoREASv/3L/P4Q4KZDQMwDsCqes6ptK1eA2AngEcBvApgTwjh4NGyWmv3UwDfA3DwCOJn6zyfAGCZmT1vZvNLP+sQv0OVUstjuS1lBXGXQgkz+wyAewD8SwjhfT2WWUtCCB8DONXMjgRwL4CTW3pYLeZiZhcC2BlCeN7MZhz8cb3mU2JaCOFtM+sP4FEz21DD924XavmNvx0AH+AeDCC/ZlVt2WFmAwGg9P/OMo9vV8ysK5oW/R0hhN91hDkBQAhhD4CVaLr3cKSZHfyiqKV20wDMMbOtABajabv/0zrOByGEt0v/70TTH8bJ6AB6tYZaLvzVAEaU7sZ2A/AlAPfX8P1T3A9gXqk9D012dk2wpq/2XwNYH0L493rPycyOKn3Tw8x6AjgXTTfVVgD4Qq3nE0K4NoQwOIQwDE2/M4+HEC6t13zMrLeZHXawDeAfAKxFHX+H2kQtbygAmA1gE5psxv9Vj5saABYBaATwEZp2IVeiyWZcDmBz6f++NZzPGWjapr4MYE3p3+x6zQnAGAAvluazFsD3Sz8/DsBzALYAWAKgex20mwHggXrOp/S+L5X+rTv4e1zP36G2/PPIPccpIB655zgFxBe+4xQQX/iOU0B84TtOAfGF7zgFxBe+4xQQX/iOU0B84TtOAfn/N6UErkw2URAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204b954a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 4 : turn 1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920365b940>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.7007811          0.73582274          0.7368033          0.67675656\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920a990748>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 5 : move 1\n",
      "ZPos : 2.5\n",
      "XPos : 2.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208c7e390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.7948217          0.5151268          0.57444775          0.6577866\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208e564a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 6 : move 1\n",
      "ZPos : 2.5\n",
      "XPos : 3.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208f582e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.7452236          0.52771807          0.5470571          0.7218135\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208f62cc0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 7 : move 1\n",
      "ZPos : 2.5\n",
      "XPos : 4.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920919c160>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.71468675          0.5916548          0.49317244          0.6883854\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204cb67b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 8 : move -1\n",
      "ZPos : 2.5\n",
      "XPos : 3.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920919c9b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.6009901          0.6268113          0.62655234          0.6104225\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208e1ef98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 9 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 3.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292087839e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.6442043          0.5927776          0.58046985          0.663287\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292742004e0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 10 : move -1\n",
      "ZPos : 3.5\n",
      "XPos : 3.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920585e6d8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.6155041          0.681524          0.62582463          0.678826\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292048003c8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 11 : move -1\n",
      "ZPos : 4.5\n",
      "XPos : 3.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205787390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.71281886          0.7680779          0.69803536          0.68494844\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2927f5a8f28>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 12 : move -1\n",
      "ZPos : 5.5\n",
      "XPos : 3.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208f984a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.8042075          0.8196425          0.74745286          0.7057151\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208e6ce48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 13 : move 1\n",
      "ZPos : 4.5\n",
      "XPos : 3.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205b5dbe0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.8380068          0.72449297          0.78618336          0.78304935\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292741f0ef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 14 : move 1\n",
      "ZPos : 3.5\n",
      "XPos : 3.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208e4cba8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.84635633          0.7877107          0.7893769          0.8286643\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205ca93c8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 15 : move 1\n",
      "ZPos : 2.5\n",
      "XPos : 3.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208fa7ac8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.84024113          0.79415417          0.79430914          0.82211787\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204a04588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 16 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 3.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29203699c50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.78844863          0.68942446          0.705912          0.7899975\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205b0f4e0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 17 : move 1\n",
      "ZPos : 2.5\n",
      "XPos : 2.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292037ea588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.80628794          0.7116283          0.69781166          0.7765689\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208f6ce48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 18 : move 1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292049c3f60>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.741129          0.6904902          0.73035234          0.70468056\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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IlItInYi8KSJ6lA0hpM2QzhX/LIApxpihAIYBmCYiYwD8CsBvjDEDABwB8ETLDZMQ0pyIMSb9jUVyAawB8CSA9wFca4y5ICJjAfy3MeZubf+ePXuasIyqLrQQ2ry8PKd25swZp6a5wIDkYo22+w4ANm7cmFgOhqeOHDky6bMWlqu5KbVim0By0cWzZ886twsrvqmFtl4NmqvSPiZhmWFzcnKcmpaNNnjO7Gy4N998c5IWzHZsu2RdrrM4Dz/8sFPbtWuXUzt16pRT07ImA+7MvmVlZTh8+LCrunWCtJ7xRSRLRDYCOAigDMBOAEeNMfEj2wCgn2t/QkjbIi3DN8ZcNMYMA1AIYDSAQak2S7WviMwVkUoRqdSuSoSQ6Liit/rGmKMAVgMYA6C7iMRn/hUC2OfYZ74xZpQxZlSmdcQJIc1LOm/1e4tI99hyDoCpAGoBrALwSGyzOQCWtNQgCSHNSzpz9QsAvCYiWWj6R7HQGPOeiGwF8IaI/A+AKgCvtuA4CSHNSKjhG2M2AxieYv0uND3vE0K+ZkQanZeXl4fZs2en1NavX6/ua7vPgmjuHs3Vp0WlAclRdz169EjScnNzE8tBd5RIsjdFK5yoFW/8+9//ro7PdgddunTJuV2Y23L0aPf/7+LiYqcWdvy0YpL2+x4tSg3QM85q5z4YNWn3GXRxBgtY2lmUNbckAPzpT39yalq0XNDdaBN2TKZOnZpy/T//+U91vzicskuIh9DwCfEQGj4hHkLDJ8RDaPiEeAgNnxAPoeET4iGR+vE1gqGsQTR//ObNm53a7bff7tRuvPFGtU97bkEwvNf2wR4/fjxJ279/f9Ln6dOnq/240PzrQLIfXfOZz5gxQ23H9llfCYMGpYrV+hdaOLI93sGDB6vtBDPg2tgVjYME51fYIeh25Vzg8rkCrvkmqdBCzbX5KdrclCeffFLt085SbBNWwTgOr/iEeAgNnxAPaTO3+mFot/Na0YeXX37ZqfXt21ftU7vd69DhXykGly5dqrazZcsWp2ZP/Q2iZWgBkm+l7eIaQcKm/mpTeseOHavuq6E9fvTs2TOx/M4776jtaAU3ruQxyp6WG5Z1yM7so50jQD9P2iOqNpV7wYIFap+ux6iw70wcXvEJ8RAaPiEeQsMnxENo+IR4CA2fEA+h4RPiIZG687744gvMnz8/pTZ8+GXZvZLQXHYff/yxU9NcdlcyO2vDhg1Jn+vr6xPLwSwrwZroWlGPESNGpD2GIPbfHcwQZBNWsOL06dNObdGiRU4trG687bILYrtnNZcXkPnMx+CsPntGZdD9GZzJV1VVlVi+8847M+of0F2Vffr0cWrnzp1T2z1x4kTK9VomJhte8QnxEBo+IR5CwyfEQ2j4hHgIDZ8QD6HhE+IhNHxCPCRSP37Hjh3Rv3//lJrmiwd0n6emzZs3z6mtXbtW7dMOfbx48WKSZmfgCWY9ad++fdJnrUrwunXrnFqwuksQ23fvqqwChFfS0UKetZBUbT9An1tgaw899JDaTnV1tVPT/rbg/AXtnAWPtV2hZ8WKFer47BDtINp389FHH3Vq2rEDgE8++STl+nQrUqd9xReRLBGpEpH3Yp/7i0i5iNSJyJsi4v7rCSFtiiu51X8aTeWx4/wKwG+MMQMAHAHwRHMOjBDScqRl+CJSCOBeAK/EPguAKQDejm3yGoAHW2KAhJDmJ90r/osAfgwgPhE4D8BRY0z8oakBQL9mHhshpIUINXwRuQ/AQWOMnSdYUmxqUqyDiMwVkUoRqdSCQQgh0ZHOW/1xAO4XkekAsgF0RdMdQHcRaRe76hcC2JdqZ2PMfADzASA/Pz/lPwdCSLSEGr4x5nkAzwOAiEwC8B/GmFki8haARwC8AWAOgCVhbXXr1g333HNPSk0rSgCEZx11kW7W0VTYWVDtME0A2Lt3b2I5Ozs7SQu6mCZNmuTsQwu/DAuxXLVqVWJ527Ztzu1KSkrUdrp16+bUbr31Vqc2ZswYtd0PPvhA1eOcPXtW1e1CGEFc4akAUFdXl/T54MGDieXevXsnacFjYLsYw0Jkg65eGy2sWSuoERau7XL3RVFQ41kA/y4iO9D0zP/qVbRFCImQK5rAY4xZDWB1bHkXAL3GEyGkTcIpu4R4CA2fEA+h4RPiITR8Qjwk0ui8Y8eOOQs45ufnq/tqGV2DbhubNWvWODUtCyyQnD036NKxXThBd1kwUk5zsQSzu9qEuZFsl46rXjoQHkWnRZBpxSVdhRvjFBUVpdXupk2b1Ha6dOni1IIZjm3saDwg2T06cuTIJC0YKWdH3NluwFRoE9O0DMLa99YVfRensLAw5XrteNjwik+Ih9DwCfEQGj4hHkLDJ8RDaPiEeAgNnxAPidSdd+7cuaSoNpvdu3er+w4dOjSjPg8fPuzUtOioIMFoLjsCb/v27UlaMPniwIEDne1qbkwt4i44huD4bK677jq1nV27djk1LXKusbFRbfeGG25Q9Th2FGQqwo6Di2AEm51AM3jug+fMdve5XGdxtmzZ4tS0yMfrr7/eqd18881qn64EpCyaSQhxQsMnxENo+IR4SKTP+Dk5OWpGFw2t4Ib2rDRt2jSnphVCAJKz/tx0001JWn19fWI5WEAj+GyrTb/UpiKHTYm1qa2tdWphxRlcWZGAzJ+vAX2qcEFBQWJ57NixajvaedKy/ASfze33S3379k3SBg8enPT5/fffTywPGTJEHV/YdHMX5eXlGbc5e/bslOu19w02vOIT4iE0fEI8hIZPiIdE+oz/TWXnzp2qbr8PCHL8+HGn1qlTJ7Vd28e+f/9+53a2/zoVWvjvgQMH1H01GhoanJodjhxWbyH4DsXms88+c2rBcFr7PASf8X2DV3xCPISGT4iHRH6rf801qf/XhLmNtKmIlZWVTq2mpsap9erVS+1z3LhxieVgkYwZM2YkloNjD95iagUXtFv0sEIYNtrtfFgmH23KbLBuvI2WPSgM++8Oy3CjPfL06+cu2RisFT9o0KDEsu1OBC7PAmQfs4qKCnV89nchiPa91TJAhR1bl72cOXNG3S8Or/iEeAgNnxAPoeET4iF05zUDt9xyi6p/+eWXTk17/h8/frzarv0cumPHDud2WmFJABg+fHhafQTRMvsC+rO7/V4h+CweRJu6rIVr5+bmOjUtXNsHeMUnxEPSuuKLSD2AEwAuArhgjBklIj0BvAmgGEA9gMeMMUdaZpiEkObkSq74k40xw4wxo2KfnwOw0hgzAMDK2GdCyNeAq3nGfwDApNjya2gqn/2stkNWVpYzFVFYCKqma6GIrnkDADBmzBi1z61bt6p6nKCPP4g27XXChAlOTXv+B5Kr0Wh+3zvuuCPtdoLceOONTk2rcBOm2+nKtEpDgP4cr02H1tDCoYFk//v06dPVbc+fP+/UtOnc2hyO0tJStU9Xu1qqNJt0r/gGwHIRWS8ic2Pr8o0xjQAQ+52yDpOIzBWRShGpTLe8DyGkZUn3ij/OGLNPRPoAKBMRd2REAGPMfADzAaCoqMhkMEZCSDOT1hXfGLMv9vsggHcAjAZwQEQKACD2W593SQhpM4Qavoh0EpEu8WUA3wZQDeBdAHNim80BsKSlBkkIaV7SudXPB/COiMS3f90Ys1REKgAsFJEnAOwB8KjSBiGkDRFq+MaYXQAue61qjDkE4K6WGBQhpGWJdMruqVOnsG7duoz2zc7OdmpfffWVU+vatatTW7Fihdqn7e7Tpt2GZeAZPXq0U9PCkcPcXDZXEyKrhfSm69JMhZYd99SpU4nlMLfgp59+6tS0DLhauHHY3zV58uTEcjBkN4jmMtYIC0fWcB2zdMfCKbuEeAgNnxAPoeET4iE0fEI8hIZPiIfQ8AnxkEjdee3atXNmttXcZQBQVVXl1DQXhhbltGzZMrXPtWvXJpa1whdhaC5MLfvNvn371HbtoCfN9adl5wnbV3OHhh0TrZCnfc40dyegu/O0yEwt81D//v3VPletWpVY1iIoAT1CUHM1a4VCwopfurIfX7hwQd0vDq/4hHgIDZ8QD6HhE+IhNHxCPISGT4iH0PAJ8RAxJrpsWDk5OcaVvDEsEm3mzJlO7dixY04trOiDhp2EUosgcyUQTWffMJdicxCWyDQvL8+paS45LfoOAO6++26nZrvatGSkYTQ2Njo1LQnqlClT1HbtY7Zw4cIrH9hVElbQtXfv3inXL1u2DIcOHZKw9nnFJ8RDaPiEeAgNnxAPoeET4iE0fEI8hIZPiIfQ8AnxkEjDctu3b4+CgoKU2qxZs9R9tUy2mq9e84eGhQKXlJQklnfv3u3cTvODA0B1dbVTcx0PABgxYoTarp2pVsuUu3r1arUd7dj26ZOyJCKA8GKSWjFO23evhf4CevhvcXGxU9PCmvPz89U+7VDqoqIidduJEyc6NS0sV5tnsHjxYrVP1/yK06dPq/vF4RWfEA+h4RPiIZFO2e3SpYsZNmxYRvtqt1valFStdrl2ewwAFRUViWUts0nHjh3VdrQpu1p2nLC69vbt8rlz55zbhU2H1qa9Xg3aY4x9TLSxA/p50jLV2DXug2iZj4Dk71RNTY26rV18I8jevXudmlbLPqwwhusxauPGjThx4gSn7BJCLoeGT4iH0PAJ8ZBIn/FF5AsAuwH0AqD70qKF49Fpa+MB2t6Y2sp4rjfGpI7ZtYjU8BOdilQaY0ZF3rEDjkenrY0HaHtjamvjCYO3+oR4CA2fEA9pLcOf30r9uuB4dNraeIC2N6a2Nh6VVnnGJ4S0LrzVJ8RDIjV8EZkmIttEZIeIPBdl39YYfi8iB0Wk2lrXU0TKRKQu9rtHhOMpEpFVIlIrIjUi8nRrjklEskVknYhsio3n57H1/UWkPDaeN0VET7Hb/OPKEpEqEXmvtccjIvUiskVENopIZWxdq32HMiEywxeRLAD/C+AeALcAeFxEbomqf4s/ApgWWPccgJXGmAEAVsY+R8UFAM8YYwYBGAPgh7Hj0lpjOgtgijFmKIBhAKaJyBgAvwLwm9h4jgB4IqLxxHkagB2L2trjmWyMGWa58FrzO3TlGGMi+QEwFsAy6/PzAJ6Pqv/AWIoBVFuftwEoiC0XANjWGuOK9b8EQGlbGBOAXAAbANyBpskp7VKdywjGUYgmY5oC4D0A0srjqQfQK7Cu1c/XlfxEeavfD4AdUtQQW9cWyDfGNAJA7Lc7+0QLIiLFAIYDKG/NMcVuqzcCOAigDMBOAEeNMfEQxajP3YsAfgwgHm6X18rjMQCWi8h6EZkbW9cmvkPpEmUGnlShgnQpxBCRzgD+CuBHxpjjIqGRlS2GMeYigGEi0h3AOwAGpdosirGIyH0ADhpj1ovIpPjq1hpPjHHGmH0i0gdAmYh8FmHfzUKUV/wGAHZQfSEAd26kaDkgIgUAEPt9MMrORaQ9moz+z8aYRW1hTABgjDkKYDWa3j10F5H4hSLKczcOwP0iUg/gDTTd7r/YiuOBMWZf7PdBNP1jHI02cL6uhCgNvwLAgNjb2A4AZgJ4N8L+Nd4FMCe2PAdNz9mRIE2X9lcB1Bpjft3aYxKR3rErPUQkB8BUNL1UWwXgkajHY4x53hhTaIwpRtN35kNjzKzWGo+IdBKRLvFlAN8GUI1W/A5lRJQvFABMB7AdTc+MP2mNlxoA/gKgEcB5NN2FPIGmZ8aVAOpiv3tGOJ7xaLpN3QxgY+xnemuNCcBtAKpi46kG8F+x9TcAWAdgB4C3AHRshXM3CcB7rTmeWL+bYj818e9xa36HMvnhzD1CPIQz9wjxEBo+IR5CwyfEQ2j4hHgIDZ8QD6HhE+IhNHxCPISGT4iH/D/7QvHNZWqcHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292049a8cf8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 19 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920374d8d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.63602084          0.6057773          0.6685065          0.7768396\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292037e5160>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 20 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208c75e80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.65757364          0.5300633          0.65100133          0.77805555\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920373deb8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 21 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29206d8e630>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.73746055          0.4857679          0.5503859          0.74612224\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208df8588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 22 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208c80e80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.6993543          0.61543834          0.6312532          0.7438049\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292087f2588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 23 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292090892e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.61715853          0.5642345          0.6286442          0.74764276\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208cfe128>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 24 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208ca2f98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.6093929          0.487118          0.5924345          0.75638354\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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93dccssvXRllZ2L3nixa8/fbb2TmfvZdDQ3kMvk/enddEYUd2VfUqJ48hkhPIZW2qgKWXddTk5DH1I6dX771c+/btw+nTp/vPsmuzRtMPAeyaW/QdHgKwrfN6G4AH+bNCiNGkSgDPdQD+K4Bnzezpznv/A8C3AfzEzO4E8BqA2xZmiEKIQVPlqf7/B1CkOtw02OEIIZpgqCG7HnZlvPDCC9mxz7rLdrt3YXAhzugZBrteOHuqtwPZpeNhW5LdZz7r0KAKdQL1CjtGfVaVE8hljeQEclkl5yxVi6+Wyen78XItiDtPCPHRQAtfiBaihS9EC2nUxh8fH8e6deu6x97/zv5Ofx2Qb+GNtluW2W7Lly/vvuZMvv4ckNtn7Of1cQfsu2X8lkq2F3m75aAquvqssVyhtVc5gVhWlsXL2oScQC7rqMkJ5LL2I6f383u5lHpLCFGIFr4QLaTxghp+R5LPsstuEMa7Kdgl5rOesPuEi296uB0OxfRqU1QogcfO6p4/P4wCloOSk9uN5ASGU8DSyzpqcgK5rP3I6Yt6VFXvPfrFF6KFaOEL0UK08IVoISMTssuwG8TD9pl357Grj+1HH2555ZVXhu16e5FDLf22YbahuU/vetm8eXN47UIUdhyUnHxtJCeQy9pUAUsv66jJCeSy9iOndz/6sSrLrhCiEC18IVpIo6r+9PR0pib5VFysIrHK7t10HGXlVR0uUsBuEN8u787z7kUgVwejXVesXkVFNIdRwJIjFHuVk/ssKxY6jAKWXtZRk5Pb6kdO78JT0UwhRCW08IVoIVr4QrSQodr43kVx+eWXZ9dyVlFv8/A5T1QYAchtfLZ9+bkC21UevwuQ2+HP+YxBgyrUCVQv7MgFGHuVE8hljeQEclmbKmDpZf0oy+nXkR+f3HlCiEK08IVoIVr4QrSQRm38lFLptsV+iQoPlsElvvyzBLaxvBxlW4p9hlS2D5sq7OjpVU4glpVl8bJKzlm8rP3I6be3e7nKvotz6BdfiBaihS9EC6lUNHNQXHTRRWnTpk3d46hwIqvEPjkih1d6FYnVO1aZuCa5h3dd+VBIVuF8OCiHCbMK5/vcsGFDdq6Jwo7sNupVTiCXNZITyGVtqoCll3XU5ARyWfuR0+8y9XK9+eabOHv2bP9FM4UQHz2qVMtdamZPmNkzZvacmf1V5/2Pm9njZvaSmd1vZnEdJCHEyFDlF/8DADemlK4GsAXArWZ2LYDvAPh+SukqAMcB3LlwwxRCDJIq1XITgDmDaKzzLwG4EcAfdd6/F8BfAvjbkrYKs+WyLcTbC71bhG0s78Lg8Eq28f0zDbaj+HmHtwPZTvawLclhm368gyrUCVQv7Mjj6VVOIJc1khPIZW2qgKUf06jJCVQvvlomp5fNyzVQd56ZLTKzpwEcAfAIgJcBnEgpzT1VOwBgXdHnhRCjRaWFn1KaTiltATAF4BoAm+a7bL7PmtldZrbDzHb0E1wjhBgctZ7qp5ROAHgUwLUAVpjZnL40BeBgwWfuSSltTSltLVOvhBDNULoSzWw1gLMppRNmdiGAmzH7YO+XAP4QwH0AtgF4sKytsbGxrCqJr0DCKYtWrlyZHftiiFzZ5PXXX8/6iPDxAWw38Ri8ncd+Xn8txxUwXhau7NNEYUfO0NqrnDw+hu+Zl7WpApZe1lGTE8hl7UdO/931clVNvVXlJ3gSwL1mtgizGsJPUkoPm9nzAO4zs/8F4CkAP6zUoxBi6FR5qv8bAJ+b5/29mLX3hRAfMkbW6I5CiaMsJ6zqc7ZSf55dLZwtJSqy6Asl8Fh9EU8+P4wCloOSE8hlieQEhlPA0ss6anJyv4OSU0UzhRCV0MIXooVo4QvRQhq18c+cOZPZUt4NwsE9r7zySnbMbjDPpZde2n3N4bMcO+BtMM7sy3aUD8XkZwfe7mOXDbfjQyoHWcDSZ5SNCjsOSk4glzWSExhcAcuqcgK5rKMmJ5DL2o+c3v3o5VKWXSFEIVr4QrQQLXwhWkijNv7Y2BjWrl3bPY5CbdmP6e2fKLMq+/ijarmcAdWnAgPy5wNsO7FvPOrT232DqtDLRBVdOR1ZE3ICuaxNyAnkso6anNxWP3L6814uVcsVQhSihS9ECxmZkF1WbXhHmYdVr2iffxRCySowu/58Pzw+b5pwO6zO+4ytgyrUyW1FhR1Zje1VTiCXNZKTx9dUAUsv60dZTj++SK4i9IsvRAvRwheihWjhC9FCGi+a6TOUcmbdCG//cLYUb9exjVUHzp7qs5ywjeXdK+xq4fBi3w67mJoq7OjpVU4+juQEclkl5yxe1n7k9M+1/HhUNFMIUYgWvhAtpPGimZs3b+4eexWFXS8cgeRdKnytb4flibL1cB+sTnl3C6t33kzhaC1WMX2fk5OT2bkmCjuy26hXOXkMkZxALmtTBSy9LKMmJ4+pHzmL7u/JkycxPT2toplCiPPRwheihWjhC9FChhqy6+12zoDCIYuRm8Lb/GwbRe69sqKZ3g6MMgCxLRk9V+BrmyjsyDZpr3Ly+CI5+dqmClj6MYyanED14qtlcvp+5M4TQlRCC1+IFqKFL0QLadTGn5mZCUMsPWwr+Uy6vLXVhy9GlXOA3FfK4ZVcvNE/A2A/r28nyt4C5IUUOXagicKOPL5e5ZyvLQ8XjPSyNlXA0o9v1OQEcln7kdPb/F4u/v4XUfkX38wWmdlTZvZw5/jjZva4mb1kZveb2XhZG0KI0aCOqv81ALvc8XcAfD+ldBWA4wDuHOTAhBALRyVV38ymAPxnAP8bwH+3Wd3mRgB/1LnkXgB/CeBvo3ZmZmYyV4hXmY4fP55dy2qaV8ujrCbsLolCktmdx5l8vArHbhKf/DMyS/j8MApYDkpObjeSExhOAUsv66jJCeSy9iOnN5kXsmjmDwD8OYC52VoF4ERKaW5mDwBYV7t3IcRQKF34ZvYlAEdSSr/yb89z6bw/rWZ2l5ntMLMdVYMLhBALSxVV/zoAf2BmXwSwFMByzGoAK8xscedXfwrAwfk+nFK6B8A9ALBkyZLmtgIKIQqptS3XzK4H8GcppS+Z2T8DeCCldJ+Z/R2A36SU/ib6/JIlS9LU1FT32IfXsm3OmWq8tsDnfGFC1io4NNPLWxYW7N2G7P7xY2C7nbO7+JDKOgUs2c3Fx979w3ZoFBLbq5xALmskJ1C9gOWg5ARyWUdNTiCXrR85iwrDnDt3DimlBd2W+w3MPujbg1mb/4d9tCWEaJBaATwppUcBPNp5vRfANYMfkhBioVHIrhAtpPFtuUVP9jlkku2qqFBm5NfnZxje5mf/NqdJiooscthw1KeXeRiFOnlLZxNy8mebKmDpZR01ObmtfuQsKpo58JBdIcRHBy18IVpIo1l2ly1bljZu3Ng99mG67NpgNdy7LzhE0atF7Grha9lN0gSD6jMKzYzuY9nuwYWiV7l7lZMZhtz93OteQm+B3Azev38/Tp8+rSy7Qojz0cIXooVo4QvRQoaaZddTtoHH23bRVkeGM5d4eAslE2VE5S2fvbbDWYGjz7F9W9V1U5ZRlt190Rg8ZXNQlj13jmgOytqJ5qBOAdXou1Amh5+HY8eOZefWrl1beQzRfYjuoXd9V5VZv/hCtBAtfCFaSKPuvCVLliS/k8mr7KxOsXvPu2ZYneGdVr0SqY2ctDMicumUqed1chZwZJonStxYh1GYE0/Z/Ax7TpjITIjmpM73wF/7wgsv4L333pM7TwhxPlr4QrQQLXwhWkij7rxFixZhxYoV3WOfOYfdFVyIwD8PWL16dWEfZS46Dxc0YMrcTHNwaCi7dKLdg4zPLsxuy8jdw3DIc6/j4WctfkzcTjT3ddyf0fg4+3I0R9F8RfNTZzzA+XPk4TnxbZVlboooms+q86xffCFaiBa+EC1EC1+IFjLUkN3IDxzZ8Rx74O28yI/LsA3I9lGdKjyeOnEG7K8dlJ0cjb3OHLHPOvIv87leY0Si+eI5qOPv9p/lPriAqoefP/Fx5NePvqv8OX6u5eFCnUXxAVW39uoXX4gWooUvRAtpVNVPKWXq1uTkZOG1rNJFKrBXmeqEkXIf0c6mOm5CvjZS0aOQzjI1NlIbfbvsjoqKSfK1VXfYzdeun09OlnrZZZcV9smy+GPuo874ovnkJJmR+s5j8NeWhfP672eklnM7UbJZP5dS9YUQhWjhC9FCtPCFaCGNh+z6MNnI3dOr7VZmi/uQ4TL3mA8pjihzj0WFHKKwUj9WIJYtKgjBzy74OYi3sdnNFdnFPB52ORX1AeRzX2aXcpiuh+cv+k55u7ksG64fbxQ+DuT3l58VRFmTornlPqKwYF9QUwU1hBCFVPpZNbNXAbwLYBrAuZTSVjObAHA/gA0AXgXwX1JKx4vaEEKMDnV+8W9IKW1JKW3tHN8NYHtK6SoA2zvHQogPAZVSb3V+8bemlN507+0GcH1K6ZCZTQJ4NKW0sagNAFi6dGmampqa9xzb29FWSM6cG9mI3v4B6oV4esrCe6NzkT0ZjZ3DgqOxcxhptKWY24nmOrLbeexcxcjD37MoJJvHHs01j92Piee9zlxH6yJqh78n0T1jezx6lsF9Ft3f999/H9PT0wNLvZUA/JuZ/crM7uq8tzaldAgAOn/XFAz4LjPbYWY76uQqE0IsHFUfnV+XUjpoZmsAPGJmL1TtIKV0D4B7gNlf/B7GKIQYMJUWfkrpYOfvETP7KYBrABw2s0mn6h+p0E6hCsWqDGfHiQpqeFgtjFxtdYoqcp/eJcbqHKvdkbuM8eMvcw15N1OdrDrsKo1ClSM1m92CkWzR2Ou4Q6P7AOT3IjIDuE/OfuPlLjOHo111PLf+mL8nkWuX5Sxy71XNGlWq6pvZRWZ2ydxrAL8HYCeAhwBs61y2DcCDlXoUQgydKr/4awH8tPO/5WIA/5hS+lczexLAT8zsTgCvAbht4YYphBgkpQs/pbQXwNXzvH8MwE0LMSghxMLSeAaeIvcG2ztsW3rbpU6GVLbPIhso8jqsWrUqO64TIutlPn48jnGquq0SqLd11MPu0Gg+oz44a0009n4qNvkxRG5AILe3o23CPHa2r+vI4p9BlGVC9mHOkZuXx1c181DVeVbIrhAtRAtfiBaihS9EC2m0Wu7SpUvT+vXru8fexmGb6vDhw9mxHydvV63jR/WUbeGtum2ybIsny+KJPhvJBcRpxrxsvYYpA3HI7tGjRyt/lkOnPZyCrc5zmTpbWz08773eIyB+3sPPrjz8/es1THjZsmXd10ePHsWZM2dULVcIcT5a+EK0kEZV/QsvvDB94hOfmPdcWTYcr9JFRQrK1NqonSiTKYe5+s9yuCePwbujWPXjMfgwUzZT2LXm2+I+/XGkigK5bNxOFMrK1/IcReGz0djXrMn3e3m5B3XPOCQ8yuzL94HHEH2nIvhaf1xm5hVlqDp16tRAd+cJIT5CaOEL0UK08IVoIY2G7M7MzBSGh5a51qKtm1FYaWTfsv3Kdp93QbGN6sfDmWfYZo0q1dTZ7sv4OeMxeHuRt/eyfeg/y3MZFZPka+tk0o22WXM7fh4ityCQZ7Hh5yn+u8Ch09H3K7qfQL2ty1G7dSga78C25QohPnpo4QvRQoZaNNMX+4sKD/AxqzNevWPVnhMYejWX1UZWDaOMLRMTE93XbKaw2u13bHmZgXoFK1hun4WF2/EqJs8Jz62fe1ZNOWrNq+FlO9z8LsBIXWY5I5diJCeQu8HYpInuJ0eD+u8pm6c8Xt7tGOHnOkowy98T/k4VzZ925wkhCtHCF6KFaOEL0UIatfEXL15cmEGFM95GdijbllHGVnYNeZdd5NYC8lBNziDr7WYOG2V3zxVXXIEiuM/IfcayeJufx+ftUs4exDaqb5fnPbqWbV+W2x+zLe4/y88uOETWz1EUrs3H/jkMkD+D4OcR/BzEzyd/T/jZkN/Zx/eax+f7ie5nWSYmP0e9hN3rF1+IFqKFL0QL0cIXooUMNQNPBNus3h/PdnGUgTfaKsq2JfvxfWYTxtvNbJezr/mtt97qvub55j59eC2H2rJs3kaMQln5uQePwdu7PB7+rJc7KpIJxJWK/BjYpud77+eB+2RfuL8XvW4TZngOOLQ78qNH94XteD9eLi4bheLjYL/ZAAAEKUlEQVT6c8eOHcPZs2e1LVcIcT5a+EK0kMZDdotCVFnd46KAXsWr4waJdl3VqWXOLjHfD6uUrJJHJgOrgl5VLSuw6V1kUbYZdtHx3EaJOasmeeR2gNgc8uPjOWB1ObqHLLcP7412LPLOzKioSJlrLbpPbEL472pkcr3yyivZuY997GPZsV8D3hSKkst69IsvRAvRwheihWjhC9FCGnXnmdlRAPsAXAbgzcY6LkfjiRm18QCjN6ZRGc+VKaW4sigaXvjdTs12pJS2Nt5xARpPzKiNBxi9MY3aeMqQqi9EC9HCF6KFDGvh3zOkfovQeGJGbTzA6I1p1MYTMhQbXwgxXKTqC9FCGl34Znarme02sz1mdneTfbsx/MjMjpjZTvfehJk9YmYvdf5WT5va/3jWm9kvzWyXmT1nZl8b5pjMbKmZPWFmz3TG81ed9z9uZo93xnO/mRXH0S7MuBaZ2VNm9vCwx2Nmr5rZs2b2tJnt6Lw3tO9QLzS28M1sEYD/A+D3AWwG8FUz29xU/46/B3ArvXc3gO0ppasAbO8cN8U5AH+aUtoE4FoAf9yZl2GN6QMAN6aUrgawBcCtZnYtgO8A+H5nPMcB3NnQeOb4GoBd7njY47khpbTFufCG+R2qT0qpkX8A/iOAn7vjbwL4ZlP901g2ANjpjncDmOy8ngSwexjj6vT/IIBbRmFMAJYB+DWA/4DZ4JTF893LBsYxhdnFdCOAhwHYkMfzKoDL6L2h3686/5pU9dcB2O+OD3TeGwXWppQOAUDn75qS6xcEM9sA4HMAHh/mmDpq9dMAjgB4BMDLAE6klOayXjR9734A4M8BzG0dXDXk8SQA/2ZmvzKzuzrvjcR3qCpNbsudb2+jXAodzOxiAA8A+JOU0jtlW0EXkpTSNIAtZrYCwE8BbJrvsibGYmZfAnAkpfQrM7t+7u1hjafDdSmlg2a2BsAjZvZCg30PhCZ/8Q8A8Hm3pgAcbLD/iMNmNgkAnb9HmuzczMYwu+j/IaX0L6MwJgBIKZ0A8Chmnz2sMLO5H4om7911AP7AzF4FcB9m1f0fDHE8SCkd7Pw9gtn/GK/BCNyvOjS58J8EcFXnaew4gNsBPNRg/xEPAdjWeb0Ns3Z2I9jsT/sPAexKKf31sMdkZqs7v/QwswsB3IzZh2q/BPCHTY8npfTNlNJUSmkDZr8z/zeldMewxmNmF5nZJXOvAfwegJ0Y4neoJ5p8oADgiwBexKzN+D+H8VADwD8BOATgLGa1kDsxazNuB/BS5+9Eg+P5T5hVU38D4OnOvy8Oa0wAfgfAU53x7ATwF533PwHgCQB7APwzgCVDuHfXA3h4mOPp9PtM599zc9/jYX6HevmnyD0hWogi94RoIVr4QrQQLXwhWogWvhAtRAtfiBaihS9EC9HCF6KFaOEL0UL+HROtlRHVDohPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205aa2d30>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 25 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292036f3fd0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.694058          0.46785718          0.50680125          0.7282144\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205b5d358>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 26 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2927f7e2518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.6684116          0.59303415          0.606502          0.7264242\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208c2d128>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 27 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208fa9588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.603234          0.5659321          0.6253745          0.75925577\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292048c4cc0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 28 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2927e4b59e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.61000085          0.50291604          0.6059289          0.7813138\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204c441d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 29 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292049c7ef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.70082194          0.48498613          0.5281513          0.748972\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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25DnnnBO18fbbutnmQw89FNVXrFiRlTW7jGZ+YUaPHh3VJ0+enJXvvvvu3M9p/xTd2JH7N2zYsKhNl5J+8MEHueflTDUzZsyI2tRGPeWUU7LywQcfHLWdeuqpUf2iiy7KyjfffHPu9YF4A4uU21jf72j/OENQSnsg1r8a7VVvhrUH0vrXQ3vWetGiRbnXY/wb33EKSCWbZvY3s3lm9qKZrTSz75Z+3sfMHjWztaX/D2zrXI7jdA4q+cbfDeD7IYShACYA+JaZDQNwNYA5IYTBAOaU6o7jfAaoZLfcZgDNpfI7ZvYigCMATAVweumwGQDmA7iqlVPkwj7HLl3iv0FqE7IPVn2cbCOq3avLTDds2JCV1XerS4HzPqfnHThwYPLYp59+OiurLan9ZR+2+tt1d6FUqjC2r3VZri4jfuyxx7KypnzSY/ldi9qheuzDDz+c2wdGswDv3LkzqleqPRA/z2q0Vw0ZPS/rn9IeiPWvlfasdSrzMtMuG9/MBgIYDWAhgMNKfxT2/HHIV9ZxnE5FxQPfzD4H4B4A/xlCSCdqjz833cyWmNkSfePuOE5jqGh1npl1A/AggEdCCP9V+tlqAKeHEJrNrC+A+SGEY1Pn6dWrVxgyZEhW57DItjbNHDBgQFbW1VEcKqqbW2hGEg4F1nvXfeQZs3jBE/8R0ynvSy+9FNU5W0pqeg7E4avHHHNM1MZuN6B8Ws7wVFYz+2pmGnYHaUixmmB83+PHj4/ali9fHtV5Wtue/ec1iw1Pn1PaA7H+1WivejP6Bcb6p7QH0vp3VHvWumar86zlCdwK4MU9g77ELADTSuVpAB5o61yO43QOKgngmQjgGwCWm9mefZD+N4AbANxlZpcD2Aig8oTnjuM0lEre6j8JIG/qcGZtu+M4Tj1oaMjuUUcdlZXVpaO20eDBg7OyLh0dPnx4Vlb7VeHdVTQ8lcNRlZUrV+a2aYipZpQ944wzsrIuK9VwT3YN8Q4yAPDAAw/kHqvwkk91j+kOOGyLa0ablFvrhhtuiOq6JJXPdfjhh+eeZ+7cuVFdsygdd9xxWTmlPZDWvz3ap/RWWP+U9kCsf0p7DRm+6667onres9Vs1Xl4yK7jFBAf+I5TQHzgO04BqWuW3X322Sew7cm++qFDh0bHamgm+5Obm5ujNr6H8847L2pT+3bVqlVZWd8rqH3L6PJK9tdqf9Rvzru9aJop9YXz7jScdRUot19T2nE2XO37xo0bozqHjh57bByKkcp2rOGyapuzhqldilR7Df3lMFT18eszYP2r0T61nFZ99ax/Snsg1l+157gWfbavvvpqVOdYAl6y+6Mf/Qjr16/3LLuO45TjA99xCkjdN83kqQ9P39va7I9DdnV6d+aZn4YT6NRU3VFbtmzJyqeddlrUpu4zhl1KQOwu02ypGjbM4Z9jx46N2tatWxfVOSuRhqeqmZDanIHdOhpKq2G4U6dOzco6zR4zZkxU13tjdKNHvg6vsFPUdaaZmdiESGkPxPpXo73qzajeXE9pD8T6q/aMuin1Xlh7NoUqNd39G99xCogPfMcpID7wHaeA1NXG/+STT/Dhhx9mdXZf8NJQoNwV4zhO7fBvfMcpID7wHaeA1H11Hrs32EWnkUm6Om/t2rVZWbORsLtKI/4UzfTDpDZK1Cg6RrMFaQQbR63pJo/qLmMXmLq1dANGTeTI6CpA5vXXX89t480rAOCpp57KPVbvW6PhONIwtcGnbtrBm2QCwNatW7PyueeeG7WpqzKlf3u0T+mt8HNIaQ/E+qv2HK2n19f7Zu1Za068mcK/8R2ngPjAd5wC4gPfcQpIXVfn7bvvvoFDFnmFkdr4ujGAZkxheGVVKtwTiFeX6Uo0DWVl1FbjDQ81Q6ueh+2zxx9/PPcaQGyz6iYUutGjhqsyvIJMw6E1PJWzvWiIqYYJsx3KGW1a+yyvGrv//vtz+6rouwv+PeFMTED5qjrWvxrtU6sJdbNL1j+lPZDWn9+R6H0qrD1rfd9992Hbtm2+Os9xnHJ84DtOAfGB7zgFpKE2PtuLO3bsiI5VHzEvo9SNJzX7bKVoRpvUxo7qL+b+8bJWoPxeUn7zzZs3R3V+1zFixIio7dJLL43qt956a+552wPft24eqRmE+b6vuOKKqG379u1RPZWhl9GMNhyzAcRZbNRm3lvaq96M/m6y/tVoz+8Vzj///KhN33Hlab9ixQq8++67buM7jlOOD3zHKSB1X53HriWewmmIru4dzps1LF26NGrjjRM1nPe9996L6k1NTVl59uzZUVtq00xN8njhhRdm5VdeeSVq0+kdZ2hRV9DRRx8d1dnVppleFi1aFNVTm2ZOmTIl95rqqpo5c2ZWVhdd//79o/r06dOzsj4vndrzPvKaPJLRbDOaBYinuZqtRzfNZP2r0V71Zlh7INY/pT0Qa6HacxJUva8nn3wyqrP2rPW1116b22/Gv/Edp4BUsltuTzNbZGbPm9lKM/tx6eeDzGyhma01szvNrHtb53Icp3NQyTf+TgBnhBBGAhgFYIqZTQBwI4CfhxAGA3gTwOV7r5uO49SSSnbLDQD2xD52K/0LAM4A8B+ln88AcC2A/9eei3NYrtqA6mZcvHhxVlZ3HttG6lJSm4szAKkdxSGdqb4CwJ133pmV1QWmS1s5m5D2XW1qfrehdqfahOoGY3gpq76D0KWbvHnpyy+/HLXx8wKAX/7yl7n9URcZX0c31GTUftX+cf/1nYNuDsL6V6O96s2w9kCsf0p7INZftecsVPPmzYva9Nmy9qx1qt9MRTa+mXU1s6UAtgJ4FMBLAHaEEPbkMt4E4Ii8zzuO07moaOCHED4OIYwC0A/AiQCGtnZYa581s+lmtsTMlmjOe8dxGkO73uqHEHYAmA9gAoADzGyPqdAPQKvz5BDCLSGEcSGEcZpZxXGcxtDmSDSzQwDsCiHsMLMmAGeh5cXePABfBfAnANMAtBk7uWvXrsge0Q0Fo47JHwneGUbDItnnOnfu3KhNbVR+d8B+XSBenqr069cvqvMuKGpra9or9pvrsmF9BvxZXeKpGzKqncrcc889WVn92fr+hGMoJk2aFLWpHb9s2bKsrLvG6H2zXaphuIzGFfTq1Suqs92a0h6I9a9Ge9Wb0R1wWP+U9kCsv2rPOrRHe9Zan08elXwF9wUww8y6omWGcFcI4UEzewHAn8zs/wJ4DkBtAscdx9nrVPJWfxmAsq/CEMLLaLH3Hcf5jFHX1Xk9evQIPIXiFVE6ldF+8bQolXlGWbNmTVTnaZlu5MhmiMLhp4q6jdQlxqvsNHxWp8DswlPX0HXXXZfbhxTqNtIpOmfKUbdWKnRV3YQaanvSSSdlZdWXYfMBAJ599tmozplpvvnNb+aeR6lG+5TeCuuf0h6I9Vft2Z2nzz21QSpz/fXXY8OGDb46z3GccnzgO04B8YHvOAWkoY51tnGOP/745LFsI65atSpqY/tfXThqJ3PmVXX9qRuJSdl8atdpZtqHHnoo9zyp9xXNzc1RXW3flDuUn4OGe6rL5957783K6n5K2fhq0+uxt99+e1bu27dv7nnasl/nz5+flVPaA/F9V6N9e2x81j+lvZ43pf2KFSuiekp7vmffScdxnFx84DtOAfGB7zgFpK42fpcuXaJQSfZpaoii7rzKdqkudUy9H1iwYEFUZxtMFw3p+wHmr3/9a1TnUFa16zRDK9vYbcUgbNu2LStrqK3a1LqLTB5qZ+oS2Y8++igrq434hz/8Iarzs1abXjPysl1fqR8aKM8+y0uV9X3K3tJe9WY0jJnPm9IeSOvP77x0ebn+zleqfR7+je84BcQHvuMUkLpO9Xft2hWtTkq5TDRkl1frdevWLWrjkFRd1afTcF591rt376hNp9KMuo0426tm1dFrchZUzaqjbiS+b16RCJSv/HriiSdy+8vPSKf2Gq7KmWA50xFQ/ox4Oq/n1foll1ySldUdlXd9IN4kE4jNwJT2QKx/NdqnXKWa6Zf1T2kPxPqr9nzf+nuc0p6fiWanzsO/8R2ngPjAd5wC4gPfcQpIXW18M4vceUOGDMnKbWUO4ewkEyZMiNo4Q4u+G9AMrrw0U90yupsPM3jw4Kg+fvz4rDxnzpzcz7XVP3XZ8fJQfQdyzjnnRHV2wylsL6o7T8NBTzjhhKys7ys0M83kyZOz8t133517/bb6x+hSZV0azPat7pSkWWX5+VajverNsPZAWv9U/1R77t+AAQOitpT2rLW6O/Pwb3zHKSA+8B2ngNR9qs+uGY6e0gwtvLkmUL4/uOM4Hce/8R2ngPjAd5wC4gPfcQpIQzPwcHZSdXNxxlEgDrfUlXzsDlIXkoaDcsiwurU466+i2XD4vG1twMgrtjTDja4K4/tmNxtQHuKpG40yHLqpbsENGzZEdQ7T1Q1A9Vg+18CBA5PHPv3001lZ3YR5fQXKtWfXm4auqiuQ9a9Ge9Wb0fOy/intgVh/1Z7feWlYtYYqs/b8/Crdps6/8R2ngPjAd5wC4gPfcQpIXXfS6d69e+Clm2yzasYdXZZ4+OGHZ2UNt2T7TEMWNaMMX1PvXW1URpfIcpjkoEGDojbNIMNhypxhpzX4vYdmb7n00kujemrTTN4tR2Mi9D0DPwddNmwWb8rC9z1s2LCoTTcP5TBYtX1TbNy4Marz78Z5550XtaltzvpXo73qzeiyYdY/pT2Q1p+zAuu7Fs00xNqz1vfccw+2bt1au510zKyrmT1nZg+W6oPMbKGZrTWzO82se6XnchynsbRnqv9dALzp2o0Afh5CGAzgTQCX17JjjuPsPSqa6ptZPwAzAFwP4HsA/geAbQAODyHsNrOTAVwbQjgncRp07949cOgtZ0FJTcuAeKqzdOnSqI2nWuo2UpcJT2X12JNPPjm376lVT+qyYbMEiDeP7N49nhjpyq433ngjK6urSqefeh2Gn5+uEGO3FhBPyXX6npqi6/RYTZOhQ4dmZTXdGM3Oo/fN03l15aqZxZpWo32lq9yAWP+U9kCsv2rfv3//rKyrA3Wl4fDhw7MyP/c77rgDW7ZsqdlU/xcAfghgj4P1IAA7Qgh7nIabABzR2gcdx+l8tDnwzewCAFtDCM/wj1s5tNWpg5lNN7MlZrZEAzMcx2kMlUTuTQTwJTM7D0BPAPuhZQZwgJntU/rW7weg1TlhCOEWALcALVP9mvTacZyqaJc7z8xOB/C/QggXmNndAO4JIfzJzH4DYFkI4b9Tn+/evXvgTRiOOeaYrMy2LZC2CXWzAb6HiRMnRm1q37JdypsoAunNGTQUmN022nd1TbJdpxs5jhgxIqpzSCpvFtnaeVPa8Xm175zlB4hDPjXzi4aA8r1qf/Q9DWuoWW0ZDf3VsFzuf0p7INa/Gu1T2YPUZZd6JvpOh/VPaa8ZqTSEmO+bXZy///3v0dzcXDt3XitcBeB7ZrYOLTb/rVWcy3GcOtKuRTohhPkA5pfKLwOobh8fx3EagofsOk4Bqeuy3N27d0chi2zH6AaMCvtHdTkqbyCongO1x/izY8eOjdrUv82ov5h935qhdf369bnnYf8rUB6eyuj7Ct5UEUhvwMg+fw2lVV/4aaedlpW178cdd1xUT90bh44CcbjvF77whdzPteUzZ7s5pT0Q61+N9qo3o3pzPfV8gFj/lPb63kPfZbD2/B6m0nd2/o3vOAXEB77jFJC6Z9ll94ZOORnNysLTolGjRkVtPAXWTCVKauMODRPOu4airkftO59Xw1OPPfbY3P7pNXUzCQ0VZlLPQd2PjLrdnn/++dxj1VWlLjJeBZja4HPMmDFRXafzlWoPpO+7Pdqn9FZY/5T2QKx/rbT3TTMdx6kIH/iOU0B84DtOAalrBp6uXbsGtiHZ5aSZS3VnHW1n2EXCGWyA8qWZbN9qeKpmm2HU9mV79pVXXkmeh+0ztfEVPi8v02wNdWUx7J5S21uX2rKtqe9dNFsPL5nVbD362XPPPTcrP/7447l9VTTsVTPeMOoeZf2r0T4VYqzPk/VPaQ+k9e+o9qz1U089hbfeemuvhuw6jvMZxQe+4xSQuk71u3TpEtgFxNFJmoRQp0zjxo3Lyho51Z5pJKPn0Y0nmHXr1kV1NlN4z3igfCqYcp/pSjl2/+jaRnllAAAMfklEQVQ0UbPGPPDAA7nnbQ+cwUWTV6rLkPWbOnVq1KbuMjW78tAIOzUvUlGSe0t71ZvRTEisf6O137RpE3bu3OlTfcdxyvGB7zgFxAe+4xSQhm6ayeiqOrXx2Q5dtWpV1MaZe3WjSQ1h5A0OeFNHoNwtl3d9ADjrrLOysmZHUbuO7Um1AXXjBLaLefMRoDy7cGpDDbYR33vvvahNN3mYPXt2VlYXna6avPDCC7OyPi+16dkFq5lzGd0gRVetsWsrpT0Q61+N9qo3w9oDsf4p7YFY/1ppz1rPmDEjt9+Mf+M7TgHxge84BcQHvuMUkLr68ZuamsJRRx2V1Tk0U7O57ty5M6qzPaR2E9uEI0eOjNo0PoCzp+py2rY2tMxDfd8a7snX7NmzZ9S2Zs2aqM7PQcNR1ZerGW8YznjTVhgzP4f2bG6pNqpmEE7dN/Pcc89FdX3fw7EDKe2BWP96aA/E+qe0B+LnUCvtWev7778f27Ztcz++4zjl+MB3nAJS16l+jx49Qp4LasuWLVFd+zVkyJCsrFNBrmsYpJoMfKy6tTQjCqPT+VdffTUra6JGzQLD0z+9T908gqeCvOEIUL5KLbUlGfdPp/b6bPk5jB49Omrr169fVOdQVk3iqffCobepRKZqBmjWGp4Cp7QHYv2r0V71ZvjZAvG9pbQHYv1rpb3+LnrIruM4reID33EKiA98xykgDQ3ZTWVW0aWPjuPUDv/Gd5wCUtE3vpltAPAOgI8B7A4hjDOzPgDuBDAQwAYA/xZCeDPvHI7jdB7a840/OYQwKoSwJxXO1QDmhBAGA5hTqjuO8xmgIj9+6Rt/XAjhDfrZagCnhxCazawvgPkhhHxHOIDu3bsH3vyS/azvvPNOdKwui2SbP5VdVtHlluxzPfXUU6O27du3554nleVXd35RnzX7ZHVZrvqE2T+rzyC1SWYKDQ1V//sFF1yQlTUOIpWOTEOBdfPLESNGZOXUslzdNUZ985xxuV7ap/RWWP+U9kCs/97Q/o9//CO2bNlSMz9+ADDbzJ4xs+mlnx0WQmgGgNL/rUY8mNl0M1tiZktSASeO49SPSt/qTwwhvGZmhwJ41MxWtfmJEiGEWwDcArR843egj47j1JiKBn4I4bXS/1vN7D4AJwLYYmZ9aaq/NXkStExleErP00hdLaUrvTiLzIYNG6I2DpPctWtX1KYhk5yVZeHChVGbhnEyqamfTu/02AULFmRlXWWVmrrqFFizz6RWvHGYrj5LNTfmzp2blTVcNjXV16l9nz59ovpDDz1U0Xnamsb++c9/zsop7YFY/2q0b89Un/VPaQ/E+tdKe9Zaw7PzaHOqb2a9zWzfPWUAXwSwAsAsANNKh00DUJtcz47j7HUq+cY/DMB9pRx4+wC4I4TwsJktBnCXmV0OYCOAr+29bjqOU0vaHPghhJcBjGzl5/8E0LHXzI7jNJS6Lsvt1q1bYBuHbS7dJFBtdXaZqG3LWVrVxtEdUdTeZVJhwu+//35UT21qqNfk5aknnXRS7ueA2AWlrjW1Q48//vjc83B/dTcctcUZznYDlG9gqRlxGb0Oa3322Wfnfk5R+/bJJ5/MyintgVj/arRXvZmOag+k9e+o9tzXBQsW+KaZjuO0jg98xykgPvAdp4DUdVmumUU2JNv4XbrEf4N0yS77+dXnz7u/6HnUrhs1alRWVrtJ/dKM2nwc9qp2nF5z4sSJWXn9+vVRm/aB37nofarPWrPTMvxZteknTZoU1dnf/cILL0RtalOvXLkyK2tMgl5nypQpWVl36GE0dqCj2gOx/tVon7LxNeSZ9U9pD8T610p7PlbTjeXh3/iOU0B84DtOAanrVH/37t2RW45XIOnGA7wiC4inULoxJmdh1WnismXLojqviBowYEDUpiGezMCBA6P60KFDs/KiRYtyPweUuyYZ3diR+8crGQFgwoQJFZ+Xp43sDgPKp6rsEtMNKwYPHhzVx48fn5XnzJmTe33tX8ptrJt6av94SpzSHoj1r0Z71Zth7YG0/vXQnp+trhTNw7/xHaeA+MB3nALiA99xCkhDs+zybiWazUVtQnbFqKuDl/rquwJ1Fb3xRpZEqMy1pu4pRjdVZBeOLjlVO4tDULU/agNyshJ1u6mrMpXVhp+DLhXVZcQrVqzIyrqDTHNzc1Tnc+muSLrhJofwqsuT0TBrtdsr1R6I77sa7VObaOq9sP4p7bVPtdKe7zmVuTo6X0VHOY7zL4UPfMcpIHVdnde1a9fArplBgwZlZZ3qawQSR4Xp6ih2C+p0U10mfH2999Te8DrV4mmZ7hOvK6t4c8QdO3bkXgOI3UpqQuhKtNTGjhyJplFoOgXm56AZbnTVGk8lWT8A2Lx5c1TnZ92e/ec1+o1duyntgVjDarRXvRnNHcn6p7QH0vp3VHvW2lfnOY6Tiw98xykgPvAdp4DU3Z3HthO7YtQeUxuLQxh1BRm7lTTkVOEsJ3psKjuKusAYzbqidufw4cOzsrqjNNzzkEMOycr9+vWL2hYvXhzV1cZmUq5JXdHGx2rG29SKRc24o2Gm48aNy8qa2YfR0F99Z8J2ckp7IK1/e7RP6a2w/intgVj/WmnP+qXeTTD+je84BcQHvuMUEB/4jlNA6u7HZ18lhz5q+KnahBy2qe8D2DbS5ZYa0pnaGFNt1BTsW9bwT+0fZ7HRrCtqA/J96zPRpcrs6x07dmxuX/U8ar9yyKcuOdUQUI630HcFukNPyt7m3wPdUUa1nzVrVlZWPTXrD/dfMwSrj53RdwWMjhH1xbP+Gn+iGYy4ftxxx0VtbP/rferS5dI+FwCAI488MivPnDmzpptmOo7zL4QPfMcpIA1dnceoG0Knrjz1GTNmTO55dM9xne4xune5Tol5Kq3n4f7oVFBXb7H5ofeZCmVNbTQJACNHfrrBkYaRahYbRqeq/Kx1NZ6aThzuqy6nlAtMk0VyZp9UqDQQT491CqyhrGy2aFYdnkqnVjZqu+qgYbipDUp0tR6vPNQ2fUaMumdZQ3ZT6urUPPwb33EKiA98xykgPvAdp4DU1Z1nZtsAvALgYABvtHF4PfH+pOls/QE6X586S38GhBAOaeugug787KJmS0II49o+sj54f9J0tv4Ana9Pna0/beFTfccpID7wHaeANGrg39Kg6+bh/UnT2foDdL4+dbb+JGmIje84TmPxqb7jFJC6Dnwzm2Jmq81snZldXc9rUx9uM7OtZraCftbHzB41s7Wl/w9MnaPG/elvZvPM7EUzW2lm321kn8ysp5ktMrPnS/35cenng8xsYak/d5pZfkqdvdOvrmb2nJk92Oj+mNkGM1tuZkvNbEnpZw37HeoIdRv4ZtYVwK8BnAtgGICvm9mwel2f+D2AKfKzqwHMCSEMBjCnVK8XuwF8P4QwFMAEAN8qPZdG9WkngDNCCCMBjAIwxcwmALgRwM9L/XkTwOV16s8evgvgRao3uj+TQwijyIXXyN+h9hNCqMs/ACcDeITq1wC4pl7Xl74MBLCC6qsB9C2V+wJY3Yh+la7/AICzO0OfAPQC8CyAk9ASnLJPa1rWoR/90DKYzgDwIABrcH82ADhYftZwvdrzr55T/SMA8NK5TaWfdQYOCyE0A0Dp//ydKvYiZjYQwGgACxvZp9K0eimArQAeBfASgB0hhD1Ly+qt3S8A/BDAniWIBzW4PwHAbDN7xsyml37WKX6HKqWey3JbywriLoUSZvY5APcA+M8QwtucYaXehBA+BjDKzA4AcB+Aoa0dVo++mNkFALaGEJ4xs9P3/LhR/SkxMYTwmpkdCuBRM1tVx2vXhHp+428CwPsf9QOQXohdP7aYWV8AKP2fn6NpL2Bm3dAy6P8YQri3M/QJAEIIOwDMR8u7hwPMbM8XRT21mwjgS2a2AcCf0DLd/0UD+4MQwmul/7ei5Q/jiegEerWHeg78xQAGl97Gdgfw7wBmtfGZejELwLRSeRpa7Oy6YC1f7bcCeDGE8F+N7pOZHVL6poeZNQE4Cy0v1eYB+Gq9+xNCuCaE0C+EMBAtvzNzQwiXNKo/ZtbbzPbdUwbwRQAr0MDfoQ5RzxcKAM4DsAYtNuP/acRLDQAzATQD2IWWWcjlaLEZ5wBYW/q/Tx378wW0TFOXAVha+ndeo/oE4AQAz5X6swLAj0o/PwrAIgDrANwNoEcDtDsdwION7E/pus+X/q3c83vcyN+hjvzzyD3HKSAeuec4BcQHvuMUEB/4jlNAfOA7TgHxge84BcQHvuMUEB/4jlNAfOA7TgH5/+nOC//y2ETnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208cf67b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 30 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205c875f8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.67867446          0.6000193          0.62712157          0.7427429\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208f31550>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 31 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208ae5c18>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.6103173          0.56209296          0.63156915          0.76617855\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292047f7438>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 32 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204bd4390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.60451627          0.49278313          0.60879356          0.7840339\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208fedd30>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 33 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204824048>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.6836012          0.47984445          0.5274592          0.7420395\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292048a3518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 34 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292036692b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.6707667          0.5879343          0.62299365          0.7420535\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920488dfd0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 35 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292049ca0b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.60362107          0.55844957          0.6406177          0.77665967\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29203729860>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 36 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29203752080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.61042756          0.5013819          0.6205934          0.7925036\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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U7mMzzZt4XBjBh2J4B9Qobfj666/PzjVR2JFDVb3qyTKUpUd7XZsqYOl1HTU9gVzXfvT0x16v/fv34/Tp0/3vsmtzs/NHAHbNT/oOjwPY1nm9DcBj/FkhxGhSJYHnZgD/GsCLZvZC571/D+A7AH5qZvcBeBPA3YsjohBi0FR5qv//ABSZDrcPVhwhRBMMNWXXw6GMV155JTv2vhv7+P6zZRV5POwDcijEh0nGxsYK2+GQDe/06x9qcipmU4UdPb3qCcQVjfjhrddVes5RtfhqmZ5et0UP5wkhLgw08YVoIZr4QrSQRn38ZcuWYf369d3j1157rfvaV7gBkF0H5BVS2P/xyxLLfLcVK1Z0X3Os1J8Dcj+P47w+74Ar1TB+R1ROn+WdhgdV0fXqq6/uvuaqK73qCcS6snxe1yb0BHJdR01PINe1Hz398ymvl7beEkIUookvRAtpvKCG383Em1tRwQw+zyExv+sJ74bD6aoeDrVwKqYP40SFElh2Tjf2adHDKGA5KD2BXNdIT2A4BSy9rqOmJ5Dr2o+ePvWXw41V0C++EC1EE1+IFqKJL0QLGZmUXYYrzkRVRnw4j/0d3snH+2fsR7Gv7p8lcJjQ7+DCPjS340Mvmzdvzs41UdhxUHoCua6RnkCua1MFLL2uo6YnkOvaj55+Ca/XS7vsCiEK0cQXooU0aurPzMxkK4n8hoFlq4p8dhJnenmzjDOyIthk4+xBv5KKzT0fNiwrmumPh1HAkjMUe9WzrE8+HkYBS6/rqOnJ7fajZ1EfytwTQhSiiS9EC9HEF6KFNO7j+91NfYjiyiuvzK7l1Efv4/Cupx4ujMjHPtzHvi+HAn2f7Dv5cBDvjsKhGO/3sS8ZrTRkei3syDu/9qonkOsa6cnyNaEnkOt6Ievp51Hk/xehX3whWogmvhAtRBNfiBbSqI+fUipctlg1/lhG5FOVwbJ5Py8q6skxV/bdfDvsH/LzgcUq7OjpVU8g1zXSE8h1lZ5zeF370dM/A/B9apddIUQhmvhCtJBKRTMHxfLly9OmTZu6xz6lksNcXFDQp+Lytd684ZroHKbhMIknKsDI57zsnCbMJqbvc+PGjdk5LsbhC4IMqrBjmalaVU+WIdITyHVtQk8g1mXYegK5rv3oWXR/jx07hrNnz/ZfNFMIceFRpVruuJk9a2Y7zexlM/vrzvsfN7NnzGyPmT1iZnEdJCHEyFDlF/9DALellG4AsAXAXWZ2E4DvAvhBSulaAO8AuG/xxBRCDJIq1XITgHmHaKzzLwG4DcCfdt5/CMBfAfjbkrYKl6iWFbv06bXsY/nnFByWiQpsRIUIgdhP9rCPymmbXl5eCszLQxejsCOHSnvVE8h1jfQEcl2b0BPIdR01PYHqxVfL9PT9eL3Kdqvutl/lIjNbYmYvADgC4EkArwE4nlKaDzweBLC+6PNCiNGi0sRPKc2klLYAmAJwI4BNC1220GfN7H4z22FmO3pZTCCEGDy1nuqnlI4DeArATQBWmtm8vTQF4FDBZx5MKW1NKW0tM6+EEM1QOhPNbA2Asyml42Z2CYA7MPdg71cA/gTAwwC2AXisrK2xsbGsKomvQMJxe7+LKF/LhQn9Tqac6sj49EqO+S9fvjw79v4Zx3m9vJxXwHhd2F9sorAjy9erngu15eF75nVtqoCll2/U9ARyXfvR0/v8Xq+qqe9VfoInATxkZkswZyH8NKX0hJm9AuBhM/tPAJ4H8KNKPQohhk6Vp/q/BfC5Bd5/HXP+vhDiI8ZH0unmUIdfkcfhkiglmUNr0co+DpP4HVL5nC/iyTIMo4DloPTk85GewHAKWHpdR01PINe1Hz2jVYlVUMquEC1EE1+IFqKJL0QLadTHP3PmDA4d+kO434dBOLln37592bH3lThM40MonD7LvpH3o3hnX77Wh2I4TOhDOhz6Yf/RpwYPo4DloPQEcl0jPYHhFLD0uo6ankCuaz96+vCj10tFM4UQhWjiC9FCNPGFaCGN+vhjY2NZuq1PteVluZyi6H0lToP0cNw0givpjI+PnyfvPHXiqOyf+djtoCr0skxRRVde0tmEnkCua1OVa72uo6Ynt9WPnv5ar5eq5QohCtHEF6KFjEzKLpsyvqAmw6ZXtM6fV+B5U4hNYF427GViF8KbV9wOm/N+RdagCnUC1Qs7shnbq55ArmukJ5Dr2lQBS6/rqOkJ5Lr2o6eXz+ulcJ4QohBNfCFaiCa+EC2k8aKZfofSsp11PVHhSe/X1QnnMbx7qn8+wH16v4qfT/AuO74dDjE1VdjR06uefBzpCeS6Ss85vK796OmfHXh5BrrLrhDiwkITX4gW0njRzM2bN3ePvYlSVlTBm/OR6cX6RLv1cBiJzSkvE5t33k3hbC02MX2fk5OTYZ+LUdgxChvx+UhPliHSE8h1baqApddl1PTk8/3oWXR/T5w4gZmZGRXNFEKcjya+EC1EE1+IFjLUlF3vt/MOKBzq8GEK9vG9H1W2y6nn5MmT2TE/H/B+IPfpYb8ueq4wqEKdQPXCjuyT9qonkMsf6cnXNlXA0sswanoC1YuvlunpdVM4TwhRCU18IVqIJr4QLaRRH392djZMsfSwf+Z30uWlrT59kX0sjmH7WCkvp+Xde/0zAI7z+nbKqpp42dlfbKKwI8vXq54LteXhgpHRzsiLVcDStztqegK5rv3o6X1+rxd//4uo/ItvZkvM7Hkze6Jz/HEze8bM9pjZI2YWl6kVQowMdUz9rwPY5Y6/C+AHKaVrAbwD4L5BCiaEWDwqmfpmNgXgXwL4zwD+rc3lut4G4E87lzwE4K8A/G3UTkopC9N5U6dslxMfFuFQn4fDJVFKMofzot1SokIJ3Mfll19eKMMwClgOSk9uN9ITGE4BS6/rqOnJ/fajp3eZq26w6an6i/9DAH8JYF6y1QCOp5TmR/YggPW1exdCDIXSiW9mXwZwJKX0a//2Apcu+NNqZveb2Q4z21H1wYMQYnGpYurfDOCPzexLAMYBrMCcBbDSzJZ2fvWnABxa6MMppQcBPAgA4+PjzS0FFEIUUmtZrpndAuDfpZS+bGZ/D+DRlNLDZvbfAPw2pfRfo8+Pj4+nqamp7rEPp7FvzjvVeP+Hz0XFBjn10esbpQUDediQwz9eBg7Z8O4uPqWyTgFLfs7BevvwD/uh3gfkse1VTyDXNdITqF7AclB6Armuo6YnkOvaj54+hOj1OnfuHFJKi7os95uYe9C3F3M+/4/6aEsI0SC1EnhSSk8BeKrz+nUANw5eJCHEYqOUXSFaSOO77BY92Wc/j/0qjn97org++1ze5+f4Nhc49P4it8Npw1Gf3tccRqFOXtLZhJ782aYKWHpdR01PbqsfPX07Xq+Bp+wKIS4cNPGFaCGN77L76U9/unvsC2PyCi02w334glMUvVkUFckEzg+TNEGdPntJvywjcpMWk0jvxdCTGYbe/Xy/eh0Tr+eBAwdw+vRp7bIrhDgfTXwhWogmvhAtpPFwXtEuoPysgf0df57PRSEMXt7o4TAXE+2Iyks+I6IqQbwLUNX+geqhm7IdZaNx6GcMyqojzRONQZkM0RiU6e3pdQyA/BkTp+iuWbNmIDJEuvgdgarqrF98IVqIJr4QLaTRcN7FF1+c/Eomb7KzWcgZUL7YAJv6vNKqVyKzsarZCvQX0hmUC8EmZ0T0HRjUmPi68HX6Z/pxL6LNLOtQZ1+JyE2IxqROH378du3ahZMnTyqcJ4Q4H018IVqIJr4QLaTRcN6SJUuywgB+5xz2haLVUlGIpGyXUw8XRmDKwkzz8Corn4oMxKsHGV+QgZ9llIUfPZzy3Ks8vGoyaica+zrPLiL5uGAFj5HfjTkar2h86sgDxGPEY+LbKtu5KaJoPKuOs37xhWghmvhCtBBNfCFaSKM+PhPFXK+44orCcxz39c8HeHlvFA/1/iBQzw+Nin9yYcwoz4BTmAflJ0excd6ZJoL7jNot26mmKtF4sTx1+vCf5T74u+Dh5098f6P7EqWiRzv5MFxgsyg/oOrSXv3iC9FCNPGFaCGNr87zZhEXl+BrPZEp5s/VSd9ls5pNuOjaOu16d4P1iszuMtPem3Xs0nhTkMNRkfvD19ZJy42KVPJuON6VK5PPjxmPbR23JXIL+HsTjRHr6a8tcz3KVvoV9R/tJuTHUqa+EKIQTXwhWogmvhAtpPGUXQ5LzFO2Aw8v0/V4n6ssrdUvhSzzD31KcURZO1EhB/ZZ/Xlethk9Z4hCQfzsgv1272OzrxuF71ieonvLfQD5PSvzS/2953At3++q4caypdNePk6tZXn9/S3z4X27kazcB+vt8enHKqghhCik0i++mb0B4H0AMwDOpZS2mtkEgEcAbATwBoB/lVKqvvuDEGJo1PnFvzWltCWltLVz/ACA7SmlawFs7xwLIT4CVNp6q/OLvzWldMy9txvALSmlaTObBPBUSum6qJ3x8fE0NTW14DmOjUZLIf3S3o4shddyam2dFE/fbp30Xj7Xa0UZXjoayc67q0Z5D9xONNbR0mWWnasYefh7Fi2trjPWLLuXice9zljX8b89/Mwhumfsj/OS46jPovv7wQcfYGZmZmBbbyUA/2Bmvzaz+zvvrUspTQNA5+/aAoHvN7MdZrajzj5iQojFo+pT/ZtTSofMbC2AJ83sH6t2kFJ6EMCDwNwvfg8yCiEGTKWJn1I61Pl7xMx+BuBGAIfNbNKZ+kf6EYRNmSg0FJlabBZGKbx1iipynz4kxuYcm91RuIzxVhGHcKI68nV21SlbbeaJzGwOC0a6sex+1+Q64dDoPgD5vYjcAO6Td7+pGnYDcl0YHlt/zN+TKBTNehaF9yIXL5Oj7AIzW25ml82/BvBHAF4C8DiAbZ3LtgF4rFKPQoihU+UXfx2An3X+t1wK4H+mlP63mT0H4Kdmdh+ANwHcvXhiCiEGSenETym9DuCGBd7/PYDbF0MoIcTi0viy3CJ/if0d9i2r+i4L9Vm1nSjqsHr16uy4TopsVFQx+myZb1ln6aiHw6HRjrNRH7wLcfTspdfwGJDrUlaE0vvbPAZ+bFn2OsuuGf8Moixl3PcThXlZvqo7D1WtSqSUXSFaiCa+EC1EE1+IFtJotdzx8fG0YcOG7rH3cdjPO3z4cGE77BtFcdRomWTZc4OqY1O2xDPShf1tDy8HZSLdvC/Z6263QJyye/To0cqfjXYlnpycDNuJKhpFukXPDvie1fm+MdHzHn525YmWZDORLr6Sz9GjR3HmzBlVyxVCnI8mvhAtpFFT/5JLLkmf+MQnFjxXtqOsN+nqFClgEyna8TZK4WWz2n+W04s5BOaPo3aAPM00kh3IzUjW2x9HpijLxO1Eqax8LesWpc9Gsq9dm6/38mNUZ/fZKJ2X7xnL4PVkcz0a6zK8Lnzv/XHkvgLFbt6pU6cGujpPCHEBoYkvRAvRxBeihTSasjs7O1uYHlqWMhkt3Yx8rMhXYj+PQ1feN2Yf1ctTtlOO99X52rJlphF+zHj3G+8v8g7F7B/6z7IPHYXS+No6O+l6+fgct+OvjcKCQL6LDevp7yenTkffr7LvZp2ly55+wqxF8g5sWa4Q4sJDE1+IFjLUopm+2F9ZbXX/OTa9vCnLoSvewNCbf2x2R7ulsNk4MTGxoGwLtevNL68zEJt7rCebcX4XFm7Hm5g8JlFRSjZNOWvNm+HsBvDKNJ+VGLk/ZRls/h6y7Cyv15XDcNEOPFy8JAqHsrx+5WaZ+R6FJn27/D1hV65o/LQ6TwhRiCa+EC1EE1+IFtKoj7906dJsBxXvQ7/99tvZtdHOL1HhBvbdosKO7Lezz+rl4x1kvd/HaaMcnrrqqqsK5WVdovAZh7m8z8/y+ecMvHsQj4lvN/I7+dqy0KQ/Zl/cf5afXfCzlmiHoGiH4EhvHmf24/148veEQ4rT09Pd1+vXry+Uh/uJ7mfZrkR1dmpa8PO1PyGE+MijiS9EC9HEF6KFNL4DzzXXXFPpWvbV/S4j7Bd734j9wWipKPuWHPf1fTLef2R/kWO5/vkFjzf36XMSONWWdfP5A1EqK48ly+fj7ywPfzbSO6r0w/hr2U/n5xV+HPjecw6Fl6nXZcIMjwGnekdx9Oi+sB/v5eXislEqrr+HhK0dAAAEIklEQVR/x44dw9mzZ7UsVwhxPpr4QrSQoabsetjcYzPXm3h1wiDRqqs6tcw5NOT7YZOSTfLIZWBT0JuqZQU2fYgsMmt5zFmeaGPOqps8AuePpx8zNlW9SVy2Q1B0D1lvv2NQtGKRdxaKQoZlobXoPrEL4ceB3QI/fvv27cvOXX311dlxUSHWKLzp0S++EC1EE1+IFqKJL0QLaTScZ2ZHAewHcAWAY411XI7kiRk1eYDRk2lU5LkmpRRXFkXDE7/bqdmOlNLWxjsuQPLEjJo8wOjJNGrylCFTX4gWookvRAsZ1sR/cEj9FiF5YkZNHmD0ZBo1eUKG4uMLIYaLTH0hWkijE9/M7jKz3Wa218weaLJvJ8OPzeyImb3k3pswsyfNbE/nb3HR+sHLs8HMfmVmu8zsZTP7+jBlMrNxM3vWzHZ25PnrzvsfN7NnOvI8YmbFebSLI9cSM3vezJ4Ytjxm9oaZvWhmL5jZjs57Q/sO9UJjE9/MlgD4LwD+BYDNAL5mZpub6t/x3wHcRe89AGB7SulaANs7x01xDsBfpJQ2AbgJwJ91xmVYMn0I4LaU0g0AtgC4y8xuAvBdAD/oyPMOgPsakmeerwPY5Y6HLc+tKaUtLoQ3zO9QfVJKjfwD8E8B/MIdfwvAt5rqn2TZCOAld7wbwGTn9SSA3cOQq9P/YwDuHAWZAHwMwG8AfB5zySlLF7qXDcgxhbnJdBuAJwDYkOV5A8AV9N7Q71edf02a+usBHHDHBzvvjQLrUkrTAND5u7bk+kXBzDYC+ByAZ4YpU8esfgHAEQBPAngNwPGU0vwyv6bv3Q8B/CWA+aWDq4csTwLwD2b2azO7v/PeSHyHqtLkstyF1jYqpNDBzC4F8CiAP08pvVe2FHQxSSnNANhiZisB/AzApoUua0IWM/sygCMppV+b2S3zbw9Lng43p5QOmdlaAE+a2T822PdAaPIX/yCADe54CsChBvuPOGxmkwDQ+Xukyc7NbAxzk/5/pJT+1yjIBAAppeMAnsLcs4eVZjb/Q9HkvbsZwB+b2RsAHsacuf/DIcqDlNKhzt8jmPuP8UaMwP2qQ5MT/zkA13aexi4DcA+AxxvsP+JxANs6r7dhzs9uBJv7af8RgF0ppb8ZtkxmtqbzSw8zuwTAHZh7qPYrAH/StDwppW+llKZSShsx9535Pymle4clj5ktN7PL5l8D+CMAL2GI36GeaPKBAoAvAXgVcz7jfxjGQw0AfwdgGsBZzFkh92HOZ9wOYE/n70SD8vwzzJmpvwXwQuffl4YlE4B/AuD5jjwvAfiPnfc/AeBZAHsB/D2Ai4dw724B8MQw5en0u7Pz7+X57/Ewv0O9/FPmnhAtRJl7QrQQTXwhWogmvhAtRBNfiBaiiS9EC9HEF6KFaOIL0UI08YVoIf8f6btl404ASFgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292049ae438>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 37 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 180.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208e3d3c8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.68249077          0.4855008          0.5391947          0.7492563\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29206d5fc88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 38 : turn -1\n",
      "ZPos : 2.5\n",
      "XPos : 1.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208e8e5f8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.6749171          0.5821085          0.63571244          0.7434098\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204ca3588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 39 : turn -1\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208e8ea58>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.607374          0.5502362          0.6441366          0.7745599\n",
      "Obtained reward : -1.0099999904632568\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "Max steps reached\n",
      "State error : State did not changed though the action was move -1\n",
      "Max steps reached\n",
      "State error : State did not changed though the action was move 1\n",
      "Max steps reached\n",
      "Max steps reached\n",
      "Max steps reached\n",
      "State error : State did not changed though the action was move 1\n",
      "#### EPISODE 50 (TEST 2) MOVES ####\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29206d64208>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 0 : turn -1\n",
      "ZPos : 0.5\n",
      "XPos : 4.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208fb24a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         -0.014931422          -0.023823895          -0.019032652          -0.014841644\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205878438>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 1 : move -1\n",
      "ZPos : 0.5\n",
      "XPos : 3.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29203809588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.1227255          0.128411          0.11480569          0.12209454\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292055be588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 2 : move -1\n",
      "ZPos : 0.5\n",
      "XPos : 2.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292057ca4e0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.39373657          0.41484523          0.40081367          0.38721097\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208d36ef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 3 : move 1\n",
      "ZPos : 0.5\n",
      "XPos : 3.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292036f3ac8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.57212204          0.53169394          0.5566786          0.5652963\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205ba7a58>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 4 : move 1\n",
      "ZPos : 0.5\n",
      "XPos : 4.5\n",
      "Yaw : 270.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208c2d518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.6635735          0.60023266          0.6552442          0.6149417\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29205d36780>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 5 : turn 1\n",
      "ZPos : 0.5\n",
      "XPos : 4.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208abf7b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.5683549          0.58333427          0.67769915          0.51781535\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208f9e860>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 6 : move 1\n",
      "ZPos : 1.5\n",
      "XPos : 4.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292091a9908>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.49624306          0.32485294          0.4570474          0.4186765\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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PABdccEF7m90yvOCCVyP7tVAn0PnCjuwq7VZOIJc1khPIZa1rAUsv67DJCeSy9iKnb5OXa/v27Thx4kTvWXZtsnfeD2DHVKdv8RiATa3tTQAeLbuWEGI46CSA5yYAfwvgN2a2rfXbPwL4GoCHzWwzgFcAfHJ2miiE6DedjOr/DECR6vCh/jZHCFEHAw3Z9bAbgqev+qy70VTHKotm8lRgduF4O5Cv62EblTMEe5uwXwt1Ap0v7MjndSsnkMsayQnksta1gKU/d9jkBHJZe5HT1+PlmhV3nhDijwN1fCEaiDq+EA2kVhufQ3a9bcS+Zg5t9WGJ0Wo5Zavu+uyqfB3vRwXybKo83dJnSOW0TYzPiMr24sqVK7Nyv1Z09Svt/v73v8/2dSsnt4/xcgK5rHXICeSyDpucQC5rL3L6/uDl4ne6CH3xhWgg6vhCNJBaVf2UUhYy610bZZlDvFuOZ135cEue8cSqtYdnTvlMJkDsGvKLH7DLcM2aNVnZyzaIBSz7JSeQyxrJCQxmAcuizDTA4OUEcll7kdO7CTtV77N2VT5DCPGORx1fiAaiji9EAxmakF2Gs9F62Kb29hrbVGzLefvsqquuyvZFdhTv8y4dnooZhYq+733vK2wPMDsLO/ZLTiCXNZITyGWtawFLL+uwyQnksvYip3c/erk6mWYP6IsvRCNRxxeigdSq6k9MTGQZcrxro8yd52cncbYen52EZ3pFlC3U6aMJWYViF5mH1UivKg5iAUt2f9YhJzCYBSy9rMMmJxC7dqvI6V14Xi5F7gkhClHHF6KBqOML0UBqt/F9SKMPr+VMtWzTeJuHQzGj8zrNSAJMz8rixyDYdvLHsg3NIah+Jhhn66ljYUfO+tOtnEAuayQnkMta1wKWXtZhkxPIZe1FTj+u5dsnd54QohB1fCEaiDq+EA2k9mm5s72MVtkClpGfk6f0+vKyZcuyfV4OrpNtN2+P8QpBdS3s6OlWTq4zkhPIZZWck3hZe5HTjx14uZRlVwhRiDq+EA2ko0Uz+8X8+fOTT4DoXSicuYRnbHl1i4/1rj52g7B7xat0PDuK1TJfJy9o4NvHoaEnT57Myr5N69evz/bVsbAjJzLtVk4glzWSE8hlrWsBSy/rsMkJ5LL2Iqdvk5fr1VdfxenTp3tfNFMI8cdHJ6vlLjCzX5rZr8zseTP7auv3y8zsKTPbZWYPmdn8smsJIYaDTr744wBuSyldB2ADgDvM7EYAXwdwb0rpSgCHAWyevWYKIfpJJ6vlJgBTRvW81r8E4DYAn279/gCArwD4dtn1vD3u3RBlC2F490UUzsvujOi67HrhqZDebRMtssjX4SxAvu39WqgT6HxhRw4b7VZOIJc1khPIZa1rAUvfhmGTs6zeKnL6sTkvV1/deWY218y2AdgP4AkALwI4klKaknIUwOqi84UQw0VHHT+ldDaltAHAGgA3AFg/02EznWtmd5nZVjPbWpZsQwhRD5VG9VNKRwA8CeBGAEvMbEqPXgNgb8E596WUNqaUNrLKJIQYDKV+fDNbDuCtlNIRM1sI4MeYHNjbBOCRlNKDZvYdAL9OKX0rutbChQvTunXr2mVvR7G/k33j/lj2S3s7iuVhW8mXOS0S13no0KHCfT5Mk6faMgsXLpxxG5ieqqlfCzt6vzRnaO1WTiCWlWXz5Trk5OsOm5xALmsvcvpYFi/XgQMH8Oabb5b68TuJ1V8F4AEzm4tJDeHhlNLjZvZbAA+a2b8AeA7A/R1cSwgxBHQyqv9rANfP8PtLmLT3hRDvMIZ2QY0InmHnXSis2rPq789lV8v4+Hjhseym8WoZ18Hrsg96Act+ycl1RnICg1nA0ss6bHJyvf2SU4tmCiE6Qh1fiAaiji9EA6nVxj9z5kw2VdLbJmwnszvFH8uuP+/q4CmUfF1fvvTSS6e1z+PtKM7C4u0+bivbgD6kchALWPZLTiCXNZITGMwCll7WYZOT9/cip3cLRu7sIvTFF6KBqOML0UDU8YVoILXa+HPnzi20TXj6LIdQ+lREPEXRU8WnydNpo3RfbDtFdhX7XL1dN4gVevl+dSsn74/kBHJZ61q51ss6bHLydXuR08ereLm0Wq4QohB1fCEayNCE7LLqxYsheMpUL08UtsmwueHriRZ25Ky/nGnVZ1NlV1AdCzuyGtutnEAuayQnEGe8ma0FLCPzcdByArmsvcjp29dNngt98YVoIOr4QjQQdXwhGkjti2Z6t0RZplOPn+7IGVC8C6dKei+2LXlKpbejOLOKz/RbltnXj1dw1p86Fnbsl5xALmskJ5DLWtcCll5WltPbyfye8ApH3VKWLTrCt+EXv/hFto/l9GMA/vlp0UwhRCHq+EI0kNoXzVy5cmW77F1tnDmH8eo8H+sXMWR5WKXzaiPPjuKFCr0pwtFvfoYgq++sYno5fbJRoJ6FHVk97lZOIJc1khPIZa1rAcvouhdddFF7m9+LaBbne9/7XrxT2LJlCw4dOqRFM4UQ01HHF6KBqOML0UAGGrLr3RAcBsmzkXxYYrRIBruCOq2f6+D97KbxbiO2Jdl95m3ffi3UydeKFnbk8M9u5QRyWSM5uX11LWDpZeU6I1cvy+Jt/G3bthXWMdO5ne6bDaKZqx598YVoIOr4QjQQdXwhGkitNv7ExMQ0m6wI9qt6Xy7bMT5MkUMWeTwgylbCUx+9Hcphwn5MgrPqMD4klevkENl+Lezos8b6awLdywnEsnLorZe1Djn5uixnv+AxiSp2/GzEzXRzzY6/+GY218yeM7PHW+XLzOwpM9tlZg+Z2fyyawghhoMqqv5nAfjFvr4O4N6U0pUADgPY3M+GCSFmj45UfTNbA+AvAPwrgL+3Sd3mNgCfbh3yAICvAPh22bW8m86HZvLsLQ4V9a6iaAZS2aKZHnatRW4arnPv3r2FdbDKO+gFLPslJ9cZyQkMZgHLKIsNmzGeKGS8mww3RXRqFlRR3/01+51s85sAvghg6g5cCOBISmnqiY0CWD3TiUKI4aO045vZxwDsTyk943+e4dAZ/0SZ2V1mttXMtvbzL6cQons6UfVvAvBxM/sogAUAFmFSA1hiZiOtr/4aAHtnOjmldB+A+wBgZGSkvqmAQohCKk3LNbNbAPxDSuljZvZ9AI+klB40s+8A+HVK6VvR+SMjI8m7brzdx6Gi7DLx7YyysJRNy/X7/RRhYLot56eS8lRRby+yu4zdUd79uHbt2mxftDgiT/dlt5d3c7G9HYXEdisnkMsayQnkstYhJ5DLynLydT38nkQZeTksPAoF5nNnw8b3jI6O4vTp07M6LfdLmBzo241Jm//+Hq4lhKiRSgE8KaUnATzZ2n4JwA39b5IQYrZRyK4QDaT2LLvedvL2Di8uyLYlT6H1dJpZlOssix3w9iLbXNG4Ape9fduvhTp5f7S6EKen6lZO3h/JCeSy1rWApZeV5fSwXR7Z3mX2dhV7vM5UdxH64gvRQNTxhWggtWfZ9ZlOvarNqj6rp1FYopehbJHMSy65pGKrp9dRlaVLl/alnmhfFBzFmWmrnDtbcs+GnAybh171L1P1vdnC7aliWvarf3XqBqzDnSeEeIeiji9EA1HHF6KBDDTLrqcXWyiy+3gap7fdyhY4ZPeUp8riiDw91MPhx96W43EOJrJZO60fiGWJ7Nmye1DW/inY7cb2bHSdyGUXPT9+Z6pMy43CwstCxrudqNZp/+j0OH3xhWgg6vhCNJBa3Xlz585NHME1BauNrAL7mV+sCnoXIVNFPlb3fD1VrhPNAitT9VjuiOhaHCEYEckWqfN13RNP2f2JrhWZDJEsbO50O+OunxRFUI6OjmJ8fFzuPCHEdNTxhWgg6vhCNJBa3Xlz5szJbD0/84vtaw4z9XbUunXrCuuoYneWhV5GdqnnwIEDWZlntPUrxJMz1UT4cGi2QavY1GUzDz1RFtsq9yA6tsrzjdpT5oqMskMNgl7ChmdCX3whGog6vhANRB1fiAYyUOMlCpO85pprOr4OZz31zFbIqQ+D5SnFHCLbL184TzmOzo383VEoa9l4QFRnFBrMi29GlIUYd9oefi/8sVVW2eH7Fb23vRBlN4oyD0XnFaEvvhANRB1fiAZSu6rvVZQ1a9YUHsfZUyJXlldPy7LN+NlSPLMrUsnHxsYKr8Ow6h+p1qxy+sSXUR1cT7SGPKum0YxAVo+j9jHcXn9slCy1zOTyz4nbE5kFfN1IRR+WJJhTVDGxugkZ1hdfiAaiji9EA1HHF6KB1Grjz5s3D6tXr26Xo9DRsoUTPN6WK3MbeTuP7UO2xb1d2m2WGj6X7eDoumyTsjvPt5/HK6JxBb6Ot/m5zirTaSMbmu/14sWLC4/lcYZo3IbHfqJxEd9ebmsvbtUq07f9sXz/qtjq3bjwPPriC9FAOvrim9keAMcBnAVwJqW00cyWAXgIwDoAewD8dUrpcNE1hBDDQ5Uv/q0ppQ0ppY2t8t0AtqSUrgSwpVUWQrwD6Cj1VuuLvzGldND9thPALSmlMTNbBeDJlNLV0XXOOeecVLSSDdtNka1WJXMph2JGtu/hw8UKC9vQp06d6rh9ke0bjQ9w26PQ5GPHjmXlKPtstEoRUyX7LMcveKqEvbLc0b3mtvsy19Gve82LrXo4RiKKM+C2dzo+ARTf6zfeeANnzpzpW+qtBODHZvaMmd3V+m1lSmkMAFr/r5jpRDO7y8y2mtnWXucQCyH6Q6ej+jellPaa2QoAT5jZ7zqtIKV0H4D7gMkvfhdtFEL0mY46fkppb+v//Wb2AwA3ANhnZqucql8cM/r2dQpVH86+G6la0b7ITQTk7rN9+/Zl+1jV8upUFZcS42Uum3m2aNGiwn2svh882La8Kq33zkQz06J7zepnlTDm6Drcdv9u8HNgU9Ufy/faq/4sl8/iDORuYW5P9Bz4uiy3N2m57f7dLDPBi2aOdppdqVTVN7PzzOyCqW0AHwawHcBjADa1DtsE4NGOahRCDJxOvvgrAfyg9VdvBMB/p5T+18yeBvCwmW0G8AqAT85eM4UQ/aS046eUXgJw3Qy/vw7gQ7PRKCHE7FLrSjrz589PRaveRDYgENuaXobI9QNMH0vwRDZqlZVpIpu5zMb3spQtOhnJEtn47PqLjo3cglGGG4bdtR4fxj0T/rlELlcglqXKc+lWlqgOIG9fNFW5bIymaHr0yZMncfbsWa2kI4SYjjq+EA1EHV+IBlLrtNyRkREsX768XY5WwI1sdZ56623hspDJKNMq44/l6y5durS9Hdlq3D6GZfH3oWxl2Gjcw8vG9ipfN4p9KEtl5uFn6OMbopRdZeNMvg085hCd658RU/bMvCxlU72jd5VDlaN3qttVlMrGgmZCX3whGog6vhANpHZ3nlf1PZHa2jq3vR1lreEwXCZSmaJwxyhjC6tzrPL6/byPVWA/kYn3vf7664Xti1x7ZQtfRq4jls271somXfn287FReCo/hypZgDyRXPy+8bGRbPxcfPvLFts4dOhQ4T7vCizrl0X1yJ0nhChEHV+IBqKOL0QDqdWdN2fOnMKQxrLVVCLXzJ49e9rbHPobhZzyPrbzfKYVtrkidxnb2358oizM9cILL2xvs/3KmV/8uVG4cS9yMt7mj+4tH8t1erud66ySTZjl9u9XJCe/b9EYU9m0a38uy8LTrL1rkFdn8uMKZa7cohD3Tl17+uIL0UDU8YVoILW68+bNm5eWLVvWLkcRUexG8upUtKBBFCnFx7JaxG6aaKZVtAgFuxT9sXzNqH0cEcYuJq/mRlGIvI/VyEit5HviZfWZZ6peN3oO/F74OssShfp3g+uPTBM+1tfJC5JG70lZf4oiR/07XsW96K8zMTGBlJLceUKI6ajjC9FA1PGFaCC12vjnnntuuvrqmdfcYDsqck9FoZhsA7IdFYXEsm3pXT58Hb+PZ3qxqyhyBx04cCArR7PYqiwy6u8D24dsz3oblevn8RS/n+WuMkbiQ1f5vCVLlhSex7BsflwkkpvDgPmZ+efN+zgjrx8P4GfN/cuHXZeF90b45+LlHB8fx8TEhGx8IcR01PGFaCDq+EI0kFpDds0ss9195lr2sXLYq7cJ2d6++OKL29scIsljB/5ctsfYTvY2Idttg/doAAAEiElEQVTFPlNtdB4Qxxmw/ejHGTgbLtvbK1a8vVxhNFbD03n5Ov7e871lW/jo0aOFdZaFXXt81h8eK+A2+P28z4c4A/l4Advi/jpl2Zg9/G5yViL/TPmZ8fiFf1e4fT4Mt0pIsZ/q/tprrxUe59EXX4gGoo4vRAOp1Z23cOHCdMUVV8y4j1U4zlTiXRZsBkQupihslOuM1itn15RX2dhtxKph5Nbi9noVlPexmeBlY5UymqXFM7v8sWVrtnvZ+N2JwkqjWZOsdnPZq9ZsVkXuT1aXvYnD50WqNLvdogw8TNRevo5/H7k9LEuRa/f06dNy5wkhZkYdX4gGoo4vRAOp1cY3swMAXgZwEYCDJYfXidoTM2ztAYavTcPSnktTSjOnsnbU2vHblZptTSltrL3iAtSemGFrDzB8bRq29pQhVV+IBqKOL0QDGVTHv29A9Rah9sQMW3uA4WvTsLUnZCA2vhBisEjVF6KB1NrxzewOM9tpZrvN7O4663Zt+K6Z7Tez7e63ZWb2hJntav1fvHpH/9uz1sx+YmY7zOx5M/vsINtkZgvM7Jdm9qtWe77a+v0yM3uq1Z6HzCxe8aH/7ZprZs+Z2eODbo+Z7TGz35jZNjPb2vptYO9QN9TW8c1sLoB/B/DnAK4F8Ckzu7au+h3/AeAO+u1uAFtSSlcC2NIq18UZAF9IKa0HcCOAz7Tuy6DaNA7gtpTSdQA2ALjDzG4E8HUA97bacxjA5praM8VnAexw5UG359aU0gbnwhvkO1SdlFIt/wD8CYAfufKXAXy5rvqpLesAbHflnQBWtbZXAdg5iHa16n8UwO3D0CYA5wJ4FsD7MRmcMjLTs6yhHWsw2ZluA/A4ABtwe/YAuIh+G/jzqvKvTlV/NYBXXXm09dswsDKlNAYArf9XlBw/K5jZOgDXA3hqkG1qqdXbAOwH8ASAFwEcSSlNTQ+s+9l9E8AXAUxNSbtwwO1JAH5sZs+Y2V2t34biHeqUOjPwzDRVUC6FFmZ2PoBHAHwupXQsyiQ826SUzgLYYGZLAPwAwPqZDqujLWb2MQD7U0rPmNktUz8Pqj0tbkop7TWzFQCeMLPf1Vh3X6jziz8KYK0rrwGwt8b6I/aZ2SoAaP2/v+T4vmJm8zDZ6f8rpfQ/w9AmAEgpHQHwJCbHHpaY2dSHos5ndxOAj5vZHgAPYlLd/+YA24OU0t7W//sx+YfxBgzB86pCnR3/aQBXtkZj5wO4E8BjNdYf8RiATa3tTZi0s2vBJj/t9wPYkVK6Z9BtMrPlrS89zGwhgD/D5KDaTwD8Vd3tSSl9OaW0JqW0DpPvzP+llP5mUO0xs/PM7IKpbQAfBrAdA3yHuqLOAQUAHwXwAiZtxn8axKAGgO8BGAPwFia1kM2YtBm3ANjV+n9Zje25GZNq6q8BbGv9++ig2gTgvQCea7VnO4B/bv1+OYBfAtgN4PsAzhnAs7sFwOODbE+r3l+1/j0/9R4P8h3q5p8i94RoIIrcE6KBqOML0UDU8YVoIOr4QjQQdXwhGog6vhANRB1fiAaiji9EA/l/9l3vouF1hykAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208da4f28>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 7 : move 1\n",
      "ZPos : 2.5\n",
      "XPos : 4.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292036f2ba8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.5073192          0.3493644          0.48642007          0.44018587\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208eefe48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 8 : move 1\n",
      "ZPos : 3.5\n",
      "XPos : 4.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208e450b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.5568896          0.38552457          0.5336775          0.48083997\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29204b96ac8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 9 : move 1\n",
      "ZPos : 4.5\n",
      "XPos : 4.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x292037fda58>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.63800585          0.45275056          0.6195139          0.5590163\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292036c62e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 10 : move 1\n",
      "ZPos : 5.5\n",
      "XPos : 4.5\n",
      "Yaw : 0.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208d28e48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.7552674          0.55223113          0.695083          0.6318452\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292037ecd68>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 11 : turn 1\n",
      "ZPos : 5.5\n",
      "XPos : 4.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208d96160>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.6041821          0.565276          0.828968          0.63236845\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x292090e5400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 12 : move 1\n",
      "ZPos : 5.5\n",
      "XPos : 3.5\n",
      "Yaw : 90.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2920878ef28>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.9090142          0.6604532          0.71487254          0.7989253\n",
      "Obtained reward : -0.01\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208f114e0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Move Array 13 : move 1\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29208ddef98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0.99882317          0.791755          0.7612052          0.8399509\n",
      "Obtained reward : 0.9900000095367432\n",
      "State error : State did not changed though the action was move -1\n",
      "Max steps reached\n",
      "Max steps reached\n",
      "Max steps reached\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move 1\n",
      "State error : State did not changed though the action was move -1\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "with tf.Session() as sess:\n",
    "    \n",
    "    if NetType == ChooseNetwork.LSTM:\n",
    "        cell = tf.contrib.rnn.BasicLSTMCell(num_units = num_nodes, state_is_tuple = True)\n",
    "        mainQN = NetType.value(cell, 'main')\n",
    "    elif NetType == ChooseNetwork.StackedConvolutional:\n",
    "        mainQN = NetType.value(trace_length)\n",
    "    else:\n",
    "        mainQN = NetType.value()\n",
    "    \n",
    "    saver = tf.train.Saver()\n",
    "    \n",
    "    init = tf.global_variables_initializer()\n",
    "    sess.run(init)\n",
    "    \n",
    "    print('Loading Model...')\n",
    "    saver.restore(sess, path_to_saved_model)\n",
    "      \n",
    "    tests_results = []\n",
    "    for test in range(nb_tests):\n",
    "        episode_nb_win = 0\n",
    "        episode_j_by_win = []\n",
    "        for episode in range(nb_episodes_by_test):\n",
    "            if NetType == ChooseNetwork.LSTM:\n",
    "                lstm_state = (np.zeros([1, num_nodes]), np.zeros([1, num_nodes])) #Reset the recurrent layer's hidden state\n",
    "\n",
    "            s = env.reset()\n",
    "            s = processState(s)\n",
    "            d = False\n",
    "            j = 0\n",
    "            episode_frames = []\n",
    "            episode_frames.append(s)\n",
    "            episode_qvalues = []\n",
    "            episode_rewards = []\n",
    "\n",
    "            ZPos = []\n",
    "            XPos = []\n",
    "            Yaw = []\n",
    "            moves = []\n",
    "\n",
    "            while not d:\n",
    "                j += 1\n",
    "\n",
    "                if NetType == ChooseNetwork.LSTM:\n",
    "                    prediction, qvalues, lstm_state1 = sess.run([mainQN.prediction, mainQN.Qout, mainQN.rnn_state],\\\n",
    "                        feed_dict={mainQN.x:[s], mainQN.train_length:1, \\\n",
    "                                   mainQN.lstm_state_in:lstm_state, mainQN.batch_size:1})\n",
    "                elif NetType == ChooseNetwork.StackedConvolutional:\n",
    "                    stacked_s = get_stacked_states(episode_frames, trace_length)\n",
    "                    prediction, qvalues = sess.run([mainQN.prediction, mainQN.Qout],\\\n",
    "                        feed_dict={mainQN.x:[stacked_s]})\n",
    "                else:\n",
    "                    prediction, qvalues = sess.run([mainQN.prediction, mainQN.Qout], \\\n",
    "                                                      feed_dict = {mainQN.x:[s]})\n",
    "                index_action_predicted = prediction[0]\n",
    "                episode_qvalues.append(qvalues[0])\n",
    "\n",
    "                #Get new state and reward from environment\n",
    "                s1_raw, r, d, info = env.step(index_action_predicted)\n",
    "\n",
    "                if info[\"observation\"]:\n",
    "                    ZPos.append(info['observation']['ZPos'])\n",
    "                    XPos.append(info['observation']['XPos'])\n",
    "                    Yaw.append(info['observation']['Yaw'])\n",
    "                s1 = processState(s1_raw)\n",
    "                moves.append(index_action_predicted)\n",
    "                episode_frames.append(s1)\n",
    "                episode_rewards.append(r)\n",
    "                if (s == s1).all():\n",
    "                    print(\"State error : State did not changed though the action was \" + env.action_names[0][index_action_predicted])\n",
    "\n",
    "                s = s1\n",
    "                if NetType == ChooseNetwork.LSTM:\n",
    "                    lstm_state = lstm_state1\n",
    "\n",
    "                if d == True:\n",
    "                    if r == 0 or r == 10 or r == -10:\n",
    "                        print(\"Unrecognized reward Error : \" + str(r))\n",
    "                    elif r > 0:\n",
    "                        episode_nb_win+= 1\n",
    "                        episode_j_by_win.append(j)\n",
    "                    elif r < 0:\n",
    "                        print(\"Max steps reached\")                    \n",
    "                    break\n",
    "            \n",
    "            if episode % (test_print_freq) == 0:\n",
    "                print(\"#### EPISODE \" + str(episode) + \" (TEST \" + str(test) + \") MOVES ####\")\n",
    "                for z in range(j):  \n",
    "                    print(\"-----------------------\")\n",
    "                    plt.imshow(reverse_processState(episode_frames[z]), cmap=plt.get_cmap(\"gray\"))\n",
    "                    plt.show()\n",
    "\n",
    "                    print(\"- Move Array \" + str(z) + \" : \" + env.action_names[0][moves[z]])\n",
    "                    if z != j-1:\n",
    "                        print(\"ZPos : \"+ str(ZPos[z]))\n",
    "                        print(\"XPos : \"+ str(XPos[z]))\n",
    "                        print(\"Yaw : \"+ str(Yaw[z]))\n",
    "                    figure = plt.figure()\n",
    "                    axes = figure.add_subplot(2, 1, 1)\n",
    "                    axes.matshow([episode_qvalues[z]])\n",
    "                    axes.set_xticks(range(len(env.action_names[0])))\n",
    "                    actions_names = [\"Straight\", \"Back\", \"Right\", \"Left\"]\n",
    "                    axes.set_xticklabels(actions_names)\n",
    "                    plt.show()\n",
    "\n",
    "                    print(\"         \" + \"          \".join(str(qval) for qval in episode_qvalues[z]))\n",
    "                    print(\"Obtained reward : \" + str(episode_rewards[z]))\n",
    "\n",
    "        tests_results.append({\n",
    "            \"nb_win\": episode_nb_win,\n",
    "            \"mean_j_by_win\": np.mean(episode_j_by_win)\n",
    "        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'nb_win': 92, 'mean_j_by_win': 11.945652173913043},\n",
       " {'nb_win': 87, 'mean_j_by_win': 13.045977011494253},\n",
       " {'nb_win': 91, 'mean_j_by_win': 12.428571428571429}]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tests_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Human-level control through deep reinforcement learning](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf)\n",
    "- [Deep Recurrent Q-Learning for Partially Observable MDPs - Matthew Hausknecht and Peter Stone](https://arxiv.org/pdf/1507.06527.pdf)\n",
    "- [Deep Reinforcement Learning with Double Q-learning - Hado van Hasselt and Arthur Guez and David Silver](https://arxiv.org/pdf/1509.06461.pdf)\n",
    "- [Teacher-Student Curriculum Learning - Tambet Matiisen and Avital Oliver and Taco Cohen and John Schulman](https://arxiv.org/pdf/1707.00183.pdf)\n",
    "- [Deep Learning for Video Game Playing - Niels Justesen and Philip Bontrager and Julian Togelius and Sebastian Risi](https://arxiv.org/pdf/1708.07902.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thanks for reading ;)"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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