{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Eligibility traces" ] }, { "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: 0.29.1\n" ] } ], "source": [ "import numpy as np\n", "rng = np.random.default_rng()\n", "import matplotlib.pyplot as plt\n", "import os\n", "# os.environ[\"IMAGEIO_FFMPEG_EXE\"] = \"/opt/homebrew/bin/ffmpeg\" # if moviepy complains about not finding ffmpeg, put its path here\n", "from IPython.display import clear_output\n", "\n", "import gymnasium as gym\n", "print(\"gym version:\", gym.__version__)\n", "\n", "import pygame\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 = []\n", "\n", "def running_average(x, N):\n", " kernel = np.ones(N) / N\n", " return np.convolve(x, kernel, mode='same')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Q-learning in Gridworld\n", "\n", "### Random interaction with the environment\n", "\n", "The goal of this exercise is to solve the **Gridworld** problem using Q-learning. The code is adapted from \n", "\n", "The agent is represented by the blue circle: the **state** $s$ of the agent is its position in the 5x5 grid, i.e. a number between 0 and 24.\n", "\n", "The agent can move either to the left, right, top or bottom. When the agent tries to move outside of the environment, it stays at its current position. There are four **actions** $a$ available, which are deterministic. \n", "\n", "Its goal is to reach the green circle, while avoiding the red ones. Actions leading to the green circle receive a reward $r$ of +100, actions leading to a red square receive a reward of -100. The episode ends in those states. All other actions have a reward of -1. An episode stops after 100 steps if a goal has not been reached." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "class GridWorldEnv(gym.Env):\n", " metadata = {\"render_modes\": [\"human\", \"rgb_array\", \"rgb_array_list\"], \"render_fps\": 4}\n", "\n", " def __init__(self, render_mode=None, size=5, rewards=[100, -100, -1]):\n", " self.size = size # The size of the square grid\n", " self.window_size = 512 # The size of the PyGame window\n", " self.rewards = rewards\n", " self._step = 0\n", "\n", " # The state is the flattened (x, y) coordinate of the agent\n", " self.observation_space = gym.spaces.Discrete(size**2)\n", "\n", " # Goal location\n", " self._target_location = np.array([3, 2], dtype=int)\n", " self._distractor1_location = np.array([3, 1], dtype=int)\n", " self._distractor2_location = np.array([2, 2], dtype=int)\n", "\n", " # We have 4 actions, corresponding to \"right\", \"up\", \"left\", \"down\"\n", " self.action_space = gym.spaces.Discrete(4)\n", "\n", " self._action_to_direction = {\n", " 0: np.array([1, 0]), # right\n", " 1: np.array([0, 1]), # down\n", " 2: np.array([-1, 0]), # left\n", " 3: np.array([0, -1]), # up\n", " }\n", "\n", " assert render_mode is None or render_mode in self.metadata[\"render_modes\"]\n", " self.render_mode = render_mode\n", "\n", " if self.render_mode == \"rgb_array_list\":\n", " self._frames = []\n", " self.window = None\n", " self.clock = None\n", " self.font = pygame.font.SysFont(None, 16)\n", " self.Q = np.zeros((self.observation_space.n, self.action_space.n))\n", "\n", "\n", " def _state2coordinates(self, state):\n", " \"Returns coordinates of a state.\"\n", " return (state % self.size, int(state/self.size))\n", "\n", " def _coordinate2state(self, coord):\n", " \"Returns the state with the coordinates.\"\n", " return coord[1] * self.size + coord[0]\n", "\n", " def reset(self, seed=None, options=None):\n", "\n", " self._step = 0\n", "\n", " # Initial location\n", " self._agent_location = np.array([0, 0], dtype=int)\n", "\n", " if self.render_mode == \"human\":\n", " self._render_frame()\n", " \n", " if self.render_mode == \"rgb_array_list\":\n", " self._frames = []\n", " self._render_frame()\n", "\n", " return self._coordinate2state(self._agent_location), {}\n", "\n", "\n", " def step(self, action):\n", "\n", " # Map the action (element of {0,1,2,3}) to the direction we walk in\n", " direction = self._action_to_direction[action]\n", " \n", " # We use `np.clip` to make sure we don't leave the grid\n", " self._agent_location = np.clip(\n", " self._agent_location + direction, 0, self.size - 1\n", " )\n", " \n", " # An episode is done if the agent has reached the target or the distractors\n", " if np.array_equal(self._agent_location, self._target_location):\n", " terminal = True\n", " reward = self.rewards[0]\n", " elif np.array_equal(self._agent_location, self._distractor1_location) \\\n", " or np.array_equal(self._agent_location, self._distractor2_location):\n", " terminal = True\n", " reward = self.rewards[1]\n", " else:\n", " terminal = False\n", " reward = self.rewards[2]\n", "\n", " if self.render_mode == \"human\" or self.render_mode == \"rgb_array_list\":\n", " self._render_frame()\n", "\n", " self._step += 1\n", " if self._step == 100:\n", " truncated = True\n", " else:\n", " truncated = False\n", "\n", " return self._coordinate2state(self._agent_location), reward, terminal, truncated, {}\n", "\n", " def render(self):\n", " if self.render_mode == \"rgb_array\":\n", " return self._render_frame()\n", " elif self.render_mode == \"rgb_array_list\":\n", " f = self._frames.copy()\n", " self._frames = []\n", " return f\n", "\n", " def _render_frame(self):\n", "\n", " if self.window is None and self.render_mode == \"human\":\n", " pygame.init()\n", " pygame.display.init()\n", " self.window = pygame.display.set_mode(\n", " (self.window_size, self.window_size)\n", " )\n", " if self.clock is None and self.render_mode == \"human\":\n", " self.clock = pygame.time.Clock()\n", "\n", " canvas = pygame.Surface((self.window_size, self.window_size))\n", " canvas.fill((255, 255, 255))\n", " pix_square_size = (\n", " self.window_size / self.size\n", " ) # The size of a single grid square in pixels\n", "\n", " # First we draw the target and the distractors\n", " pygame.draw.rect(\n", " canvas,\n", " (0, 255, 0),\n", " pygame.Rect(\n", " pix_square_size * self._target_location,\n", " (pix_square_size, pix_square_size),\n", " ),\n", " )\n", " pygame.draw.rect(\n", " canvas,\n", " (255, 0, 0),\n", " pygame.Rect(\n", " pix_square_size * self._distractor1_location,\n", " (pix_square_size, pix_square_size),\n", " ),\n", " )\n", " pygame.draw.rect(\n", " canvas,\n", " (255, 0, 0),\n", " pygame.Rect(\n", " pix_square_size * self._distractor2_location,\n", " (pix_square_size, pix_square_size),\n", " ),\n", " )\n", "\n", " # Now we draw the agent\n", " pygame.draw.circle(\n", " canvas,\n", " (0, 0, 255),\n", " (self._agent_location + 0.5) * pix_square_size,\n", " pix_square_size / 3,\n", " )\n", "\n", " # Add some gridlines\n", " for x in range(self.size + 1):\n", " pygame.draw.line(\n", " canvas,\n", " 0,\n", " (0, pix_square_size * x),\n", " (self.window_size, pix_square_size * x),\n", " width=3,\n", " )\n", " pygame.draw.line(\n", " canvas,\n", " 0,\n", " (pix_square_size * x, 0),\n", " (pix_square_size * x, self.window_size),\n", " width=3,\n", " )\n", "\n", " # Print Q-values\n", " for x in range(self.size):\n", " for y in range(self.size):\n", " s = self._coordinate2state((x, y))\n", " \n", " # Up\n", " val = f\"{self.Q[s, 3]:+.2f}\"\n", " text = self.font.render(val, True, (0, 0, 0))\n", " canvas.blit(text, \n", " ((x + 0.5) * pix_square_size - 6, \n", " (y) * pix_square_size + 6)\n", " )\n", " # Down\n", " val = f\"{self.Q[s, 1]:+.2f}\"\n", " text = self.font.render(val, True, (0, 0, 0))\n", " canvas.blit(text, \n", " ((x + 0.5) * pix_square_size - 6, \n", " (y+1) * pix_square_size - 12)\n", " )\n", " # Left\n", " val = f\"{self.Q[s, 2]:+.2f}\"\n", " text = self.font.render(val, True, (0, 0, 0))\n", " canvas.blit(text, \n", " ((x) * pix_square_size + 6, \n", " (y+ 0.5) * pix_square_size - 6)\n", " )\n", " # Right\n", " val = f\"{self.Q[s, 0]:+.2f}\"\n", " text = self.font.render(val, True, (0, 0, 0))\n", " canvas.blit(text, \n", " ((x + 1) * pix_square_size - 32, \n", " (y+ 0.5) * pix_square_size - 6)\n", " )\n", "\n", "\n", " if self.render_mode == \"human\":\n", " # The following line copies our drawings from `canvas` to the visible window\n", " self.window.blit(canvas, canvas.get_rect())\n", " pygame.event.pump()\n", " pygame.display.update()\n", "\n", " # We need to ensure that human-rendering occurs at the predefined framerate.\n", " # The following line will automatically add a delay to keep the framerate stable.\n", " self.clock.tick(self.metadata[\"render_fps\"])\n", "\n", " elif self.render_mode == \"rgb_array\":\n", " return np.transpose(\n", " np.array(pygame.surfarray.pixels3d(canvas)), axes=(1, 0, 2)\n", " )\n", " elif self.render_mode == \"rgb_array_list\":\n", " array = np.transpose(\n", " np.array(pygame.surfarray.pixels3d(canvas)), axes=(1, 0, 2)\n", " )\n", " self._frames.append(array)\n", "\n", " def close(self):\n", " if self.window is not None:\n", " pygame.display.quit()\n", " pygame.quit()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "class RandomAgent:\n", " \n", " def __init__(self, env):\n", " self.env = env\n", " self.Q = np.zeros((self.env.observation_space.n, self.env.action_space.n))\n", " \n", " def act(self, state):\n", " \"Selects an action randomly\"\n", " return self.env.action_space.sample()\n", " \n", " def train(self, nb_episodes, recorder=None):\n", " \"Runs the agent on the environment for nb_episodes.\"\n", " # Returns\n", " returns = []\n", " steps = []\n", "\n", " # Fixed number of episodes\n", " for episode in range(nb_episodes):\n", "\n", " # Reset\n", " state, info = self.env.reset()\n", " done = False\n", " nb_steps = 0\n", "\n", " # Store rewards\n", " return_episode = 0.0\n", "\n", " # Sample the episode\n", " while not done:\n", " \n", " # Select an action \n", " action = self.act(state)\n", "\n", " # Perform the action\n", " next_state, reward, terminal, truncated, info = self.env.step(action)\n", " \n", " # Append reward\n", " return_episode += reward\n", "\n", " # Go in the next state\n", " state = next_state\n", "\n", " # Increment time\n", " nb_steps += 1\n", "\n", " # Terminal state\n", " done = terminal or truncated\n", " \n", " # Pass the Q table to the GUI\n", " self.env.Q = self.Q \n", "\n", " # Store info\n", " returns.append(return_episode)\n", " steps.append(nb_steps)\n", " \n", " return returns, steps" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Create the environment\n", "env = GridWorldEnv(render_mode='human')\n", "\n", "# Create the agent\n", "agent = RandomAgent(env)\n", "\n", "# Perform random episodes\n", "returns, steps = agent.train(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q:** Adapt your Q-learning agent from last exercise to the problem. The main difference is the call to `self.env.Q = self.Q` so that the GUI displays the Q-values, the rest is similar. Train it for 100 episodes with the right hyperparameters and without rendering." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "class QLearningAgent:\n", " \"\"\"\n", " Q-learning agent.\n", " \"\"\"\n", " \n", " def __init__(self, env, gamma, exploration, decay, alpha):\n", " \"\"\"\n", " :param env: gym-like environment\n", " :param gamma: discount factor\n", " :param exploration: exploration parameter\n", " :param decay: exploration decay parameter\n", " :param alpha: learning rate\n", " \"\"\"\n", " self.env = env\n", " self.gamma = gamma\n", " self.exploration = exploration\n", " self.decay = decay\n", " self.alpha = alpha\n", " \n", " # Q_table\n", " self.Q = np.zeros([self.env.observation_space.n, self.env.action_space.n])\n", " \n", " def act(self, state):\n", " \"Returns an action using epsilon-greedy action selection.\"\n", " \n", " action = rng.choice(np.where(self.Q[state, :] == self.Q[state, :].max())[0])\n", " \n", " if rng.random() < self.exploration:\n", " action = self.env.action_space.sample() \n", " \n", " return action\n", " \n", " def update(self, state, action, reward, next_state, done):\n", " \"Updates the agent using a single transition.\"\n", " \n", " # Bellman target\n", " target = reward\n", " \n", " if not done:\n", " target += self.gamma * self.Q[next_state, :].max()\n", " \n", " # Update the Q-value\n", " self.Q[state, action] += self.alpha * (target - self.Q[state, action])\n", " \n", " # Decay exploration parameter\n", " self.exploration = self.exploration * (1 - self.decay)\n", " \n", " \n", " def train(self, nb_episodes, recorder=None):\n", " \"Runs the agent on the environment for nb_episodes.\"\n", "\n", " # Returns\n", " returns = []\n", " steps = []\n", "\n", " # Fixed number of episodes\n", " for episode in range(nb_episodes):\n", "\n", " # Reset\n", " state, info = self.env.reset()\n", " done = False\n", " nb_steps = 0\n", "\n", " # Store rewards\n", " return_episode = 0.0\n", "\n", " # Sample the episode\n", " while not done:\n", "\n", " # Select an action \n", " action = self.act(state)\n", "\n", " # Perform the action\n", " next_state, reward, terminal, truncated, info = self.env.step(action)\n", "\n", " # Terminal state\n", " done = terminal or truncated\n", " \n", " # Append reward\n", " return_episode += reward\n", "\n", " # Learn from the transition\n", " self.update(state, action, reward, next_state, done)\n", "\n", " # Go in the next state\n", " state = next_state\n", "\n", " # Increment time\n", " nb_steps += 1\n", " \n", " # Pass the Q table to the GUI\n", " self.env.Q = self.Q\n", "\n", " # Store info\n", " returns.append(return_episode)\n", " steps.append(nb_steps)\n", "\n", " if recorder is not None:\n", " recorder.record(self.env.render())\n", " \n", " return returns, steps\n", " \n", " def test(self, recorder=None):\n", " \"Performs a test episode without exploration.\"\n", " previous_epsilon = self.epsilon\n", " self.epsilon = 0.0\n", " \n", " # Reset\n", " state, info = self.env.reset()\n", " done = False\n", " nb_steps = 0\n", " return_episode= 0\n", "\n", " # Sample the episode\n", " while not done:\n", " action = self.act(state)\n", " next_state, reward, done, info = self.env.step(action)\n", " return_episode += reward\n", " state = next_state\n", " nb_steps += 1\n", " \n", " self.epsilon = previous_epsilon\n", "\n", " if recorder is not None:\n", " recorder.record(self.env.render())\n", " \n", " return return_episode, nb_steps" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Parameters\n", "gamma = 0.99\n", "epsilon = 0.1\n", "decay_epsilon = 0\n", "alpha = 0.1\n", "nb_episodes = 100\n", "\n", "# Create the environment\n", "env = GridWorldEnv(render_mode=None)\n", "\n", "# Create the agent\n", "agent = QLearningAgent(env, gamma, epsilon, decay_epsilon, alpha)\n", "\n", "# Train the agent\n", "returns, steps = agent.train(nb_episodes)\n", "\n", "plt.figure(figsize=(12, 5))\n", "plt.subplot(121)\n", "plt.plot(returns)\n", "plt.xlabel(\"Episodes\")\n", "plt.ylabel(\"Return\")\n", "plt.subplot(122)\n", "plt.plot(steps)\n", "plt.xlabel(\"Episodes\")\n", "plt.ylabel(\"Steps\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q:** Train a Q-learning agent with rendering on. Observe in particular which Q-values are updated when the agent reaches the target. Is it efficient?" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Parameters\n", "gamma = 0.99\n", "epsilon = 0.1\n", "decay_epsilon = 0\n", "alpha = 0.1\n", "nb_episodes = 10\n", "\n", "# Create the environment\n", "env = GridWorldEnv(render_mode='human')\n", "\n", "# Create the agent\n", "agent = QLearningAgent(env, gamma, epsilon, decay_epsilon, alpha)\n", "\n", "# Train the agent\n", "returns, steps = agent.train(nb_episodes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q:** Modify your agent so that it uses **softmax action selection**, with a temperature $\\tau = 1.0$ and a suitable decay. What does it change?\n", "\n", "If you have time, write a generic class for the Q-learning agent where you can select the action selection method flexibly." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "class SoftQLearningAgent(QLearningAgent):\n", " \"\"\"\n", " Q-learning agent with softmax or e-greedy AS.\n", " \"\"\"\n", " \n", " def __init__(self, env, gamma, action_selection, alpha):\n", " \"\"\"\n", " :param env: gym-like environment\n", " :param gamma: discount factor\n", " :param action selection: exploration mechanism\n", " :param alpha: learning rate\n", " \"\"\"\n", " self.action_selection = action_selection\n", " \n", " super().__init__(env, gamma, action_selection['param'], action_selection['decay'], alpha)\n", " \n", " def act(self, state):\n", " \"Returns an action using epsilon-greedy or softmax action selection.\"\n", " \n", " if self.action_selection['type'] == \"egreedy\":\n", " # epsilon-greedy\n", " if rng.uniform(0, 1, 1) < self.exploration:\n", " action = self.env.action_space.sample() \n", " else:\n", " action = rng.choice(np.where(self.Q[state, :] == self.Q[state, :].max())[0])\n", " else: \n", " # softmax\n", " logits = np.exp((self.Q[state, :] - self.Q[state, :].max())/self.exploration)\n", " probas = logits / np.sum(logits)\n", " action = rng.choice(range(4), p=probas) \n", " \n", " return action\n", " " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Parameters\n", "gamma = 0.99\n", "#action_selection = {'type': \"egreedy\", \"param\": 0.1, \"decay\": 0.0}\n", "action_selection = {'type': \"softmax\", \"param\": 1.0, \"decay\": 0.0}\n", "alpha = 0.1\n", "nb_episodes = 100\n", "\n", "# Create the environment\n", "env = GridWorldEnv(render_mode=None)\n", "\n", "# Create the agent\n", "agent = SoftQLearningAgent(env, gamma, action_selection, alpha)\n", "\n", "# Train the agent \n", "returns, steps = agent.train(nb_episodes)\n", "\n", "plt.figure(figsize=(12, 5))\n", "plt.subplot(121)\n", "plt.plot(returns)\n", "plt.xlabel(\"Episodes\")\n", "plt.ylabel(\"Return\")\n", "plt.subplot(122)\n", "plt.plot(steps)\n", "plt.xlabel(\"Episodes\")\n", "plt.ylabel(\"Steps\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**A:** The agent explores much less at the end of training, as the difference between the Q-values becomes high enough to become greedy. In particular, it quickly stops to go to the red squares. In this environment, there is no real need to decay tau." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Eligibility traces\n", "\n", "The main drawback of Q-learning is that it needs many episodes to converge (**sample complexity**).\n", "\n", "One way to speed up learning is to use eligibility traces, one per state-action pair:\n", "\n", "```python\n", "traces = np.zeros((nb_states, nb_actions))\n", "```\n", "\n", "After each transition $(s_t, a_t)$, Q($\\lambda$) updates a **trace** $e(s_t, a_t)$ and modifies all Q-values as:\n", "\n", "1. The trace of the last transition is incremented from 1:\n", " \n", "$$e(s_t, a_t) = e(s_t, a_t) +1$$\n", " \n", "2. Q($\\lambda$)-learning is applied on **ALL** Q-values, using the TD error at time $t$:\n", " \n", "$$Q(s, a) = Q(s, a) + \\alpha \\, (r_{t+1} + \\gamma \\, \\max_{a'} Q(s_{t+1}, a') - Q(s_t, a_t)) \\, e(s, a)$$\n", " \n", "3. All traces are exponentially decreased using the trace parameter $\\lambda$ (e.g. 0.7):\n", "\n", "$$\n", "e(s, a) = \\lambda \\, \\gamma \\, e(s, a)\n", "$$\n", "\n", "All traces are reset to 0 at the beginning of an episode.\n", "\n", "**Q:** Implement eligibility traces in your Q($\\lambda$)-learning agent and see if it improves convergence. Train it with rendering on and observe how all Q-values are updated." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "class QLambdaLearningAgent(SoftQLearningAgent):\n", " \"\"\"\n", " Q(lambda)-learning agent with softmax or e-greedy AS and eligibility traces.\n", " \"\"\"\n", " \n", " def __init__(self, env, gamma, lbda, action_selection, alpha):\n", " \"\"\"\n", " :param env: gym-like environment\n", " :param gamma: discount factor\n", " :param lbda: trace\n", " :param action selection: exploration mechanism\n", " :param alpha: learning rate\n", " \"\"\"\n", " self.lbda = lbda\n", " \n", " # Traces\n", " self.traces = np.zeros([env.observation_space.n, env.action_space.n])\n", " \n", " super().__init__(env, gamma, action_selection, alpha)\n", " \n", "\n", " \n", " def update(self, state, action, reward, next_state, done):\n", " \n", " # Bellman target\n", " target = reward\n", " if not done:\n", " target += self.gamma * self.Q[next_state, :].max()\n", " \n", " # Update ALL Q-values\n", " self.Q += self.alpha * (target - self.Q[state, action]) * self.traces\n", " \n", " # Decay exploration parameter\n", " self.exploration = self.exploration * (1 - self.decay)\n", " \n", " \n", " def train(self, nb_episodes, recorder=None):\n", " \"Runs the agent on the environment for nb_episodes.\"\n", "\n", " # Returns\n", " returns = []\n", " steps = []\n", "\n", " # Fixed number of episodes\n", " for episode in range(nb_episodes):\n", "\n", " # Reset\n", " state, info = self.env.reset()\n", " done = False\n", " nb_steps = 0\n", " \n", " # Reset traces\n", " self.traces *= 0.0\n", "\n", " # Store rewards\n", " return_episode = 0.0\n", "\n", " # Sample the episode\n", " while not done:\n", "\n", " # Select an action \n", " action = self.act(state)\n", "\n", " # Perform the action\n", " next_state, reward, terminal, truncated, info = self.env.step(action)\n", " \n", " # Terminal state\n", " done = terminal or truncated\n", " \n", " # Update return \n", " return_episode += reward\n", " \n", " # Increment trace\n", " self.traces[state, action] += 1\n", "\n", " # Learn from the transition\n", " self.update(state, action, reward, next_state, done)\n", " \n", " # Update all traces\n", " self.traces *= self.gamma * self.lbda\n", "\n", " # Go in the next state\n", " state = next_state\n", "\n", " # Increment time\n", " nb_steps += 1\n", " \n", " \n", " # Pass the Q table to the GUI\n", " self.env.Q = self.Q\n", "\n", " # Store info\n", " returns.append(return_episode)\n", " steps.append(nb_steps)\n", "\n", " if recorder is not None:\n", " recorder.record(self.env.render())\n", "\n", " return returns, steps" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Parameters\n", "gamma = 0.99\n", "lbda = 0.7\n", "#action_selection = {'type': \"egreedy\", \"param\": 0.1, \"decay\": 0.0}\n", "action_selection = {'type': \"softmax\", \"param\": 1.0, \"decay\": 0.0}\n", "alpha = 0.1\n", "nb_episodes = 100\n", "\n", "# Create the environment\n", "env = GridWorldEnv()\n", "\n", "# Create the agent\n", "agent = QLambdaLearningAgent(env, gamma, lbda, action_selection, alpha)\n", "\n", "# Train the agent\n", "returns, steps = agent.train(nb_episodes)\n", "\n", "plt.figure(figsize=(12, 5))\n", "plt.subplot(121)\n", "plt.plot(returns)\n", "plt.xlabel(\"Episodes\")\n", "plt.ylabel(\"Return\")\n", "plt.subplot(122)\n", "plt.plot(steps)\n", "plt.xlabel(\"Episodes\")\n", "plt.ylabel(\"Steps\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Parameters\n", "gamma = 0.99\n", "lbda = 0.7\n", "#action_selection = {'type': \"egreedy\", \"param\": 0.1, \"decay\": 0.0}\n", "action_selection = {'type': \"softmax\", \"param\": 1.0, \"decay\": 0.0}\n", "alpha = 0.1\n", "nb_episodes = 10\n", "\n", "# Create the environment\n", "env = GridWorldEnv(render_mode='human')\n", "\n", "# Create the agent\n", "agent = QLambdaLearningAgent(env, gamma, lbda, action_selection, alpha)\n", "\n", "# Train the agent\n", "returns = agent.train(nb_episodes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q:** Vary the trace parameter $\\lambda$ and discuss its influence." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Parameters\n", "gamma = 0.99\n", "#action_selection = {'type': \"egreedy\", \"param\": 0.1, \"decay\": 0.0}\n", "action_selection = {'type': \"softmax\", \"param\": 1.0, \"decay\": 0.0}\n", "alpha = 0.1\n", "nb_episodes = 100\n", "\n", "list_returns = []\n", "\n", "for lbda in np.linspace(0.1, 1.0, 10):\n", "\n", " # Create the environment\n", " env = GridWorldEnv()\n", "\n", " # Create the agent\n", " agent = QLambdaLearningAgent(env, gamma, lbda, action_selection, alpha)\n", "\n", " # Train the agent\n", " returns, steps = agent.train(nb_episodes)\n", " \n", " list_returns.append(returns)\n", " \n", "plt.figure(figsize=(12, 6))\n", "for idx, lbda in enumerate(np.linspace(0.1, 1.0, 10)):\n", " plt.plot(list_returns[idx], label=str(lbda))\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**A:** $\\lambda$ should not be too high nor too low in order to speed up learning. It controls the bias/variance trade-off." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q:** Increase the size of Gridworld to 100x100 and observe how long it takes to learn the optimal strategy using eligibility traces or not.\n", "\n", "```python\n", "env = GridWorldEnv(size=100)\n", "```\n", "\n", "Comment on the **curse of dimensionality** and the interest of tabular RL for complex tasks with large state spaces and sparse rewards (e.g. robotics)." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Parameters\n", "gamma = 0.99\n", "lbda = 0.7\n", "#action_selection = {'type': \"egreedy\", \"param\": 0.1, \"decay\": 0.0}\n", "action_selection = {'type': \"softmax\", \"param\": 1.0, \"decay\": 0.0}\n", "alpha = 0.1\n", "nb_episodes = 100\n", "\n", "# Create the environment\n", "env = GridWorldEnv(size=100)\n", "\n", "# Create the agent\n", "agent = SoftQLearningAgent(env, gamma, action_selection, alpha)\n", "#agent = QLambdaLearningAgent(env, gamma, lbda, action_selection, alpha)\n", "\n", "# Train the agent\n", "returns, steps = agent.train(nb_episodes)\n", "\n", "plt.figure(figsize=(12, 5))\n", "plt.subplot(121)\n", "plt.plot(returns)\n", "plt.xlabel(\"Episodes\")\n", "plt.ylabel(\"Return\")\n", "plt.subplot(122)\n", "plt.plot(steps)\n", "plt.xlabel(\"Episodes\")\n", "plt.ylabel(\"Steps\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**A:** When the Gridworld is too big, the likelihood to hit the target per chance when exploring is very low. There are a lot of unsuccessful trials before learning starts to occur. But it happens after a while." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.9.13 ('deeprl')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.6" }, "vscode": { "interpreter": { "hash": "932956c8e5d2f79d68ff59e849758b6e4ddbf01f7f22c7d8bb3532c38341d908" } } }, "nbformat": 4, "nbformat_minor": 4 }