{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Monte-Carlo control "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" import google.colab\n",
" IN_COLAB = True\n",
"except:\n",
" IN_COLAB = False\n",
"\n",
"if IN_COLAB:\n",
" !pip install -U gymnasium pygame moviepy\n",
" !pip install gymnasium[box2d]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gym version: 0.26.3\n"
]
}
],
"source": [
"import numpy as np\n",
"rng = np.random.default_rng()\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"\n",
"import gymnasium as gym\n",
"print(\"gym version:\", gym.__version__)\n",
"\n",
"from moviepy.editor import ImageSequenceClip, ipython_display\n",
"\n",
"class GymRecorder(object):\n",
" \"\"\"\n",
" Simple wrapper over moviepy to generate a .gif with the frames of a gym environment.\n",
" \n",
" The environment must have the render_mode `rgb_array_list`.\n",
" \"\"\"\n",
" def __init__(self, env):\n",
" self.env = env\n",
" self._frames = []\n",
"\n",
" def record(self, frames):\n",
" \"To be called at the end of an episode.\"\n",
" for frame in frames:\n",
" self._frames.append(np.array(frame))\n",
"\n",
" def make_video(self, filename):\n",
" \"Generates the gif video.\"\n",
" directory = os.path.dirname(os.path.abspath(filename))\n",
" if not os.path.exists(directory):\n",
" os.mkdir(directory)\n",
" self.clip = ImageSequenceClip(list(self._frames), fps=self.env.metadata[\"render_fps\"])\n",
" self.clip.write_gif(filename, fps=self.env.metadata[\"render_fps\"], loop=0)\n",
" del self._frames\n",
" self._frames = []"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The taxi environment\n",
"\n",
"In this exercise, we are going to apply **on-policy Monte-Carlo control** on the Taxi environment available in gym:\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's create the environment in ansi mode, initialize it, and render the first state:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"374\n",
"+---------+\n",
"|R: | : :G|\n",
"| : | : : |\n",
"| : : : : |\n",
"| | : |\u001b[43m \u001b[0m: |\n",
"|\u001b[35mY\u001b[0m| : |\u001b[34;1mB\u001b[0m: |\n",
"+---------+\n",
"\n",
"\n"
]
}
],
"source": [
"env = gym.make(\"Taxi-v3\", render_mode='ansi')\n",
"state, info = env.reset()\n",
"print(state)\n",
"print(env.render())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The agent is the black square. It can move up, down, left or right if there is no wall (the pipes and dashes). Its goal is to pick clients at the blue location and drop them off at the purple location. These locations are fixed (R, G, B, Y), but which one is the pick-up location and which one is the drop-off destination changes between each episode.\n",
"\n",
"**Q:** Re-run the previous cell multiple times to observe the diversity of initial states.\n",
"\n",
"The following cell prints the action space of the environment: "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Action Space: Discrete(6)\n",
"Number of actions: 6\n"
]
}
],
"source": [
"print(\"Action Space:\", env.action_space)\n",
"print(\"Number of actions:\", env.action_space.n)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 6 discrete actions: south, north, east, west, pickup, dropoff.\n",
" \n",
"Let's now look at the observation space (state space):"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"State Space: Discrete(500)\n",
"Number of states: 500\n"
]
}
],
"source": [
"print(\"State Space:\", env.observation_space)\n",
"print(\"Number of states:\", env.observation_space.n)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 500 discrete states. What are they?\n",
"\n",
"* The taxi can be anywhere in the 5x5 grid, giving 25 different locations.\n",
"* The passenger can be at any of the four locations R, G, B, Y or in the taxi: 5 values.\n",
"* The destination can be any of the four locations: 4 values.\n",
"\n",
"This gives indeed 25x5x4 = 500 different combinations.\n",
"\n",
"The internal representation of a state is a number between 0 and 499. You can use the `encode` and `decode` methods of the environment to relate it to the state variables."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"State: 224\n",
"State: [3, 1, 2, 0]\n"
]
}
],
"source": [
"state = env.encode(2, 1, 1, 0) # (taxi row, taxi column, passenger index, destination index)\n",
"print(\"State:\", state)\n",
"\n",
"state = env.decode(328) \n",
"print(\"State:\", list(state))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The reward function is simple:\n",
"\n",
"* r = 20 when delivering the client at the correct location.\n",
"* r = -10 when picking or dropping a client illegally (picking where there is no client, dropping a client somewhere else, etc)\n",
"* r = -1 for all other transitions in order to incent the agent to be as fast as possible.\n",
"\n",
"The actions pickup and dropoff are very dangerous: take them at the wrong time and your return will be very low. The navigation actions are less critical.\n",
"\n",
"Depending on the initial state, the taxi will need at least 10 steps to deliver the client, so the maximal return you can expect is around 10 (+20 for the success, -1 for all the steps). \n",
"\n",
"The task is episodic: if you have not delivered the client within 200 steps, the episode stops (no particular reward)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random agent\n",
"\n",
"Let's now define a random agent that just samples the action space."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q:** Modify the random agent of last time, so that it accepts the `GymRecorder` that generates the .gif file.\n",
"\n",
"```python\n",
"def train(self, nb_episodes, recorder=None):\n",
"```\n",
"\n",
"The environment should be started in 'rgb_array_list' mode, not 'ansi'. The game looks different but has the same rules.\n",
"\n",
"```python\n",
"env = gym.make(\"Taxi-v3\", render_mode='rgb_array_list')\n",
"recorder = GymRecorder(env)\n",
"```\n",
"\n",
"As episodes in Taxi can be quite long, only the last episode should be recorded:\n",
"\n",
"```python\n",
"if recorder is not None and episode == nb_episodes -1:\n",
" recorder.record(self.env.render())\n",
"```\n",
"\n",
"Perform 10 episodes, plot the obtained returns and vidualize the last episode."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"class RandomAgent:\n",
" \"\"\"\n",
" Random agent exploring uniformly the environment.\n",
" \"\"\"\n",
" \n",
" def __init__(self, env):\n",
" self.env = env\n",
" \n",
" def act(self, state):\n",
" \"Returns a random action by sampling the action space.\"\n",
" return self.env.action_space.sample()\n",
" \n",
" def update(self, state, action, reward, next_state):\n",
" \"Updates the agent using the transition (s, a, r, s').\"\n",
" pass\n",
" \n",
" def train(self, nb_episodes, recorder=None):\n",
" \"Runs the agent on the environment for nb_episodes. Returns the list of obtained rewards.\"\n",
" # List of returns\n",
" returns = []\n",
"\n",
" for episode in range(nb_episodes):\n",
"\n",
" # Sample the initial state\n",
" state, info = self.env.reset()\n",
"\n",
" return_episode = 0.0\n",
" done = False\n",
" while not done:\n",
"\n",
" # Select an action randomly\n",
" action = self.env.action_space.sample()\n",
" \n",
" # Sample a single transition\n",
" next_state, reward, terminal, truncated, info = self.env.step(action)\n",
" \n",
" # Go in the next state\n",
" state = next_state\n",
"\n",
" # Update return\n",
" return_episode += reward\n",
"\n",
" # End of the episode\n",
" done = terminal or truncated\n",
"\n",
" # Record at the end of the episode\n",
" if recorder is not None and episode == nb_episodes -1:\n",
" recorder.record(self.env.render())\n",
" \n",
" # Append return\n",
" returns.append(return_episode)\n",
"\n",
" return returns"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MoviePy - Building file videos/taxi.gif with imageio.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"data": {
"text/html": [
"