sarsa.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1-Step SARSA \n",
    "\n",
    "This notebook shows the tabular reinforcement learning algorithm, 1-step SARSA applied to the Taxi problem (it also works on CliffWalking and FrozenLake) from Open AI's gym. 1-step SARSA is an on-policy Temporal Difference control algorithm. The algorithm is defined by: \n",
    "$$Q(S_t, A_t) \\leftarrow Q(S_t, A_t) + \\alpha [R_{t + 1} + \\gamma Q(S_{t + 1}, A_{t + 1}) - Q(S_t, A_t)]$$\n",
    ", where $Q$ is the learned action-value function, $R$ is the reward, $A$ is the action, $\\alpha$ is the step size, $\\gamma$ is the discount rate and $t$ is the time step.\n",
    "\n",
    "For more information on the SARSA algorithm see section 6.4 of [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf). For alternative implementations of 1-step SARSA see: [SARSA Example by Denny Britz](https://github.com/dennybritz/reinforcement-learning/blob/master/TD/SARSA%20Solution.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import gymnasium as gym\n",
    "import numpy as np\n",
    "from IPython.display import Video\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Type of environment, options include:\n",
    "# Taxi-v3, CliffWalking-v0, FrozenLake-v1\n",
    "ENV_TYPE = \"Taxi-v3\"\n",
    "\n",
    "# Save videos from training\n",
    "SAVE_VIDEOS = False\n",
    "VIDEOS_ROOT_DIR = './videos/sarsa/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the environment\n",
    "env = gym.make(ENV_TYPE, render_mode=\"rgb_array\")\n",
    "\n",
    "# Number of possible states and actions\n",
    "num_states = env.observation_space.n \n",
    "num_actions = env.action_space.n\n",
    "\n",
    "# Action-value function, \n",
    "# initialized to 0 for all states and actions\n",
    "Q = np.zeros([num_states, num_actions])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create An $\\epsilon$-Greedy Policy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def policy_fn(state, Q, epsilon, num_actions):\n",
    "    # Create a distribution of actions and divide the epsilon probability between all actions\n",
    "    action_dist = np.ones(num_actions, dtype=float) * epsilon / num_actions\n",
    "    # Find the best action\n",
    "    best_action = np.argmax(Q[state, :])\n",
    "    # Set probability for the best action to (1 - epsilon)\n",
    "    action_dist[best_action] += (1.0 - epsilon)\n",
    "    return action_dist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learn The Optimal Action-Value Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 5 Average Returns: -233.80\n",
      "Episode 10 Average Returns: -461.80\n",
      "Episode 15 Average Returns: -227.80\n",
      "Episode 20 Average Returns: -197.40\n",
      "Episode 25 Average Returns: -207.80\n",
      "Episode 30 Average Returns: -182.40\n",
      "Episode 35 Average Returns: -120.40\n",
      "Episode 40 Average Returns: -240.80\n",
      "Episode 45 Average Returns: -107.40\n",
      "Episode 50 Average Returns: -271.80\n",
      "Episode 55 Average Returns: -260.80\n",
      "Episode 60 Average Returns: -7.40\n",
      "Episode 65 Average Returns: -229.80\n",
      "Episode 70 Average Returns: -66.60\n",
      "Episode 75 Average Returns: -231.80\n",
      "Episode 80 Average Returns: -73.20\n",
      "Episode 85 Average Returns: -44.00\n",
      "Episode 90 Average Returns: -67.80\n",
      "Episode 95 Average Returns: -32.20\n",
      "Episode 100 Average Returns: -51.00\n",
      "Episode 105 Average Returns: -17.40\n",
      "Episode 110 Average Returns: -65.80\n",
      "Episode 115 Average Returns: -37.40\n",
      "Episode 120 Average Returns: -15.20\n",
      "Episode 125 Average Returns: -45.00\n",
      "Episode 130 Average Returns: -62.80\n",
      "Episode 135 Average Returns: -19.20\n",
      "Episode 140 Average Returns: -20.40\n",
      "Episode 145 Average Returns: -25.80\n",
      "Episode 150 Average Returns: -44.40\n",
      "Episode 155 Average Returns: 0.80\n",
      "Episode 160 Average Returns: -19.80\n",
      "Episode 165 Average Returns: -27.80\n",
      "Episode 170 Average Returns: -14.00\n",
      "Episode 175 Average Returns: -5.60\n",
      "Episode 180 Average Returns: -95.00\n",
      "Episode 185 Average Returns: -8.00\n",
      "Episode 190 Average Returns: -80.80\n",
      "Episode 195 Average Returns: -1.00\n",
      "Episode 200 Average Returns: 0.40\n",
      "Episode 205 Average Returns: 8.60\n",
      "Episode 210 Average Returns: -28.60\n",
      "Episode 215 Average Returns: -6.40\n",
      "Episode 220 Average Returns: 2.40\n",
      "Episode 225 Average Returns: -36.80\n",
      "Episode 230 Average Returns: 7.80\n",
      "Episode 235 Average Returns: 2.60\n",
      "Episode 240 Average Returns: -6.80\n",
      "Episode 245 Average Returns: -13.00\n",
      "Episode 250 Average Returns: -4.80\n",
      "Episode 255 Average Returns: 7.20\n",
      "Episode 260 Average Returns: 9.00\n",
      "Episode 265 Average Returns: -26.60\n",
      "Episode 270 Average Returns: -12.40\n",
      "Episode 275 Average Returns: -11.60\n",
      "Episode 280 Average Returns: -6.60\n",
      "Episode 285 Average Returns: -17.20\n",
      "Episode 290 Average Returns: 8.20\n",
      "Episode 295 Average Returns: -10.60\n",
      "Episode 300 Average Returns: -9.20\n",
      "Episode 305 Average Returns: 7.60\n",
      "Episode 310 Average Returns: 1.60\n",
      "Episode 315 Average Returns: 5.00\n",
      "Episode 320 Average Returns: -10.80\n",
      "Episode 325 Average Returns: 1.60\n",
      "Episode 330 Average Returns: 9.20\n",
      "Episode 335 Average Returns: -67.20\n",
      "Episode 340 Average Returns: 8.20\n",
      "Episode 345 Average Returns: -0.20\n",
      "Episode 350 Average Returns: 8.80\n",
      "Episode 355 Average Returns: -4.40\n",
      "Episode 360 Average Returns: 8.00\n",
      "Episode 365 Average Returns: 3.60\n",
      "Episode 370 Average Returns: 9.00\n",
      "Episode 375 Average Returns: 3.00\n",
      "Episode 380 Average Returns: 4.00\n",
      "Episode 385 Average Returns: -11.00\n",
      "Episode 390 Average Returns: 4.60\n",
      "Episode 395 Average Returns: 3.60\n",
      "Episode 400 Average Returns: 6.60\n"
     ]
    }
   ],
   "source": [
    "# Number of episodes to train on\n",
    "episodes = 401\n",
    "# Sliding window for episode evaluation (needs to be less than episodes)\n",
    "episode_window = 5\n",
    "# Discount ratio\n",
    "gamma = 0.99\n",
    "# Step size\n",
    "alpha = 0.618\n",
    "# Probability of taking a non-greedy action\n",
    "epsilon = 0.001\n",
    "\n",
    "env = gym.make(ENV_TYPE, render_mode=\"rgb_array\")\n",
    "env = gym.wrappers.RecordEpisodeStatistics(env, buffer_length=episodes)\n",
    "\n",
    "if SAVE_VIDEOS:\n",
    "    os.makedirs(VIDEOS_ROOT_DIR, exist_ok=True)\n",
    "    env = gym.wrappers.RecordVideo(\n",
    "        env=env, \n",
    "        video_folder=VIDEOS_ROOT_DIR,\n",
    "        episode_trigger=lambda episode: episode % 100 == 0,\n",
    "        video_length=0,\n",
    "        name_prefix=ENV_TYPE\n",
    "        )\n",
    "\n",
    "\n",
    "for episode in range(1, episodes+1):\n",
    "    terminated, truncated = False, False\n",
    "    reward = 0\n",
    "    state, info = env.reset()\n",
    "    action_dist = policy_fn(state, Q, epsilon, num_actions)\n",
    "    action = np.random.choice(np.arange(num_actions), p=action_dist)\n",
    "\n",
    "    while not terminated:\n",
    "        # Take the action and observe reward and next state\n",
    "        next_state, reward, terminated, truncated, info = env.step(action)\n",
    "        # Select the next action following the epsilon-greedy policy\n",
    "        next_action_dist = policy_fn(next_state, Q, epsilon, num_actions)\n",
    "        next_action = np.random.choice(np.arange(num_actions), p=next_action_dist)\n",
    "        # Update the expected return for the action-value function\n",
    "        Q[state, action] += alpha * (reward + gamma * Q[next_state, next_action] - Q[state, action]) \n",
    "        state = next_state\n",
    "        action = next_action\n",
    "    \n",
    "    if episode % episode_window == 0:\n",
    "        print(f'Episode {episode} Average Returns: {np.mean(np.array(env.return_queue).flatten()[-episode_window:]) :.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot The Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code used from: https://gymnasium.farama.org/tutorials/blackjack_tutorial/#visualizing-the-training\n",
    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "axs[0].set_title(\"Episode rewards\")\n",
    "reward_moving_average = (\n",
    "    np.convolve(\n",
    "        np.array(env.return_queue).flatten(), np.ones(episode_window), mode=\"valid\"\n",
    "    )\n",
    "    / episode_window\n",
    ")\n",
    "axs[0].plot(range(len(reward_moving_average)), reward_moving_average)\n",
    "axs[1].set_title(\"Episode lengths\")\n",
    "length_moving_average = (\n",
    "    np.convolve(\n",
    "        np.array(env.length_queue).flatten(), np.ones(episode_window), mode=\"valid\"\n",
    "    )\n",
    "    / episode_window\n",
    ")\n",
    "axs[1].plot(range(len(length_moving_average)), length_moving_average)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Episode Videos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Episode 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"./videos/sarsa//Taxi-v3-episode-0.mp4\" controls  >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Video(f\"{VIDEOS_ROOT_DIR}/{ENV_TYPE}-episode-0.mp4\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Episode 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"./videos/sarsa//Taxi-v3-episode-100.mp4\" controls  >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Video(f\"{VIDEOS_ROOT_DIR}/{ENV_TYPE}-episode-100.mp4\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Episode 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"./videos/sarsa//Taxi-v3-episode-200.mp4\" controls  >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Video(f\"{VIDEOS_ROOT_DIR}/{ENV_TYPE}-episode-200.mp4\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Episode 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"./videos/sarsa//Taxi-v3-episode-300.mp4\" controls  >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Video(f\"{VIDEOS_ROOT_DIR}/{ENV_TYPE}-episode-300.mp4\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Episode 400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"./videos/sarsa//Taxi-v3-episode-400.mp4\" controls  >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Video(f\"{VIDEOS_ROOT_DIR}/{ENV_TYPE}-episode-400.mp4\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Render Final Model On An Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Reward: 9, Steps Taken: 12\n"
     ]
    }
   ],
   "source": [
    "# Run the algorithm in inferencing mode and \n",
    "# observe its performance\n",
    "env = gym.make(ENV_TYPE, render_mode=\"human\")\n",
    "state, info = env.reset(seed=60)\n",
    "G = 0\n",
    "num_steps = 0\n",
    "terminated = False\n",
    "truncated = False\n",
    "\n",
    "while not terminated and not truncated:\n",
    "    action = np.argmax(Q[state, :]) \n",
    "    state, reward, terminated, truncated, info = env.step(action)\n",
    "    G += reward\n",
    "    num_steps += 1\n",
    "\n",
    "print(f'Total Reward: {G}, Steps Taken: {num_steps}')\n",
    "\n",
    "env.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "flax",
   "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": "3.10.12"
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  "orig_nbformat": 4,
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   "interpreter": {
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