TD/SARSA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import gym\n",
    "import itertools\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys\n",
    "\n",
    "if \"../\" not in sys.path:\n",
    "  sys.path.append(\"../\") \n",
    "\n",
    "from collections import defaultdict\n",
    "from lib.envs.windy_gridworld import WindyGridworldEnv\n",
    "from lib import plotting\n",
    "\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = WindyGridworldEnv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_epsilon_greedy_policy(Q, epsilon, nA):\n",
    "    \"\"\"\n",
    "    Creates an epsilon-greedy policy based on a given Q-function and epsilon.\n",
    "    \n",
    "    Args:\n",
    "        Q: A dictionary that maps from state -> action-values.\n",
    "            Each value is a numpy array of length nA (see below)\n",
    "        epsilon: The probability to select a random action . float between 0 and 1.\n",
    "        nA: Number of actions in the environment.\n",
    "    \n",
    "    Returns:\n",
    "        A function that takes the observation as an argument and returns\n",
    "        the probabilities for each action in the form of a numpy array of length nA.\n",
    "    \n",
    "    \"\"\"\n",
    "    def policy_fn(observation):\n",
    "        A = np.ones(nA, dtype=float) * epsilon / nA\n",
    "        best_action = np.argmax(Q[observation])\n",
    "        A[best_action] += (1.0 - epsilon)\n",
    "        return A\n",
    "    return policy_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sarsa(env, num_episodes, discount_factor=1.0, alpha=0.5, epsilon=0.1):\n",
    "    \"\"\"\n",
    "    SARSA algorithm: On-policy TD control. Finds the optimal epsilon-greedy policy.\n",
    "    \n",
    "    Args:\n",
    "        env: OpenAI environment.\n",
    "        num_episodes: Number of episodes to run for.\n",
    "        discount_factor: Gamma discount factor.\n",
    "        alpha: TD learning rate.\n",
    "        epsilon: Chance the sample a random action. Float betwen 0 and 1.\n",
    "    \n",
    "    Returns:\n",
    "        A tuple (Q, stats).\n",
    "        Q is the optimal action-value function, a dictionary mapping state -> action values.\n",
    "        stats is an EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\n",
    "    \"\"\"\n",
    "    \n",
    "    # The final action-value function.\n",
    "    # A nested dictionary that maps state -> (action -> action-value).\n",
    "    Q = defaultdict(lambda: np.zeros(env.action_space.n))\n",
    "    \n",
    "    # Keeps track of useful statistics\n",
    "    stats = plotting.EpisodeStats(\n",
    "        episode_lengths=np.zeros(num_episodes),\n",
    "        episode_rewards=np.zeros(num_episodes))\n",
    "\n",
    "    # The policy we're following\n",
    "    policy = make_epsilon_greedy_policy(Q, epsilon, env.action_space.n)\n",
    "    \n",
    "\n",
    "    for i_episode in range(num_episodes):\n",
    "        # Print out which episode we're on, useful for debugging.\n",
    "        if (i_episode + 1) % 100 == 0:\n",
    "            print(\"\\rEpisode {}/{}.\".format(i_episode + 1, num_episodes), end=\"\")\n",
    "            sys.stdout.flush()\n",
    "        \n",
    "        # Implement this!\n",
    "    \n",
    "    return Q, stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 200/200."
     ]
    }
   ],
   "source": [
    "Q, stats = sarsa(env, 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Xq6SkRFFRUdq4caNmzJjh0yY5OVkbNmxQjx49tHnzZm+w69evn9atW6eqqioFBQVp586d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\nrMmTJ/t9mhgAAMA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x106a76a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TC5O7du2Ky5cvVzgdVe7NN9/E8OHDERUVhUmTJuGvv/5SpmVlZcHT0xP333+/Ms7Ozg4P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\n+sXxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106b08390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BAABYjkAHAABgOQIdAACA5Qh0AAAAliPQAQAAWI5ABwAAYDkCHQAAgOUIdAAAAJYj0AEA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\nAADLEegAAAAs938BJCULTMwc5aUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106c886a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting.plot_episode_stats(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}