Taxi_v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/gamante91/Q-Learning-openAI-gym-Taxi-V2/blob/master/Taxi_v2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2du7zL0PGQpM"
   },
   "outputs": [],
   "source": [
    "'''\n",
    "import section\n",
    "'''\n",
    "from collections import defaultdict\n",
    "import gym\n",
    "import gym.spaces\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import sys\n",
    "from time import sleep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qJDWNICKQfMn"
   },
   "outputs": [],
   "source": [
    "'''\n",
    "constants and free functions\n",
    "'''\n",
    "epsilon_max = 0.95\n",
    "\n",
    "def select_epsilon_greedy_action(Q_s, nA, epsilon):\n",
    "    return np.argmax(Q_s) if np.random.sample() < epsilon else np.random.choice(nA)\n",
    "\n",
    "def select_greedy_action(Q_s):\n",
    "    return np.argmax(Q_s)\n",
    "\n",
    "clear = lambda : sys.stdout.flush()\n",
    "\n",
    "def render(env, info):\n",
    "    clear()\n",
    "    env.render()\n",
    "\n",
    "    if 'state' in info:\n",
    "        print(\"state:\", info['state'])\n",
    "    if 'action' in info:\n",
    "        print(\"action:\", info['action'])\n",
    "    if 'reward' in info:\n",
    "        print(\"reward:\", info['reward'])\n",
    "        if 'done' in info and info['done']:\n",
    "            print(\"game ended\")\n",
    "            print(\"you won! :)\") if info['reward'] > 0 else print(\"you lost! :(\")\n",
    "    \n",
    "    sleep(0.1)\n",
    "    \n",
    "def moving_average(x, window):\n",
    "    return np.convolve(x, np.ones((window,))/window, mode='valid')\n",
    "\n",
    "def plot_training_figures(counts, rewards, window=100):\n",
    "    \n",
    "    counts_means = moving_average(counts,window)\n",
    "    counts_rewards = moving_average(rewards,window)\n",
    "    x = range(0,len(counts_means))\n",
    "    \n",
    "    plt.rcParams[\"figure.figsize\"][0] = 15\n",
    "    plt.rcParams[\"figure.figsize\"][1] = 6\n",
    "    \n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(x,counts_means,linewidth=4)\n",
    "    plt.xlabel(\"episode\")\n",
    "    plt.ylabel(\"actions to reach a goal\")\n",
    "    plt.title(\"number of action needed to reach a goal\")\n",
    "    plt.grid(True)\n",
    "    \n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(x,counts_rewards,linewidth=4)\n",
    "    plt.xlabel(\"episode\")\n",
    "    plt.ylabel(\"reward\")\n",
    "    plt.title(\"rewards\")\n",
    "    plt.grid(True)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5echn6MoGbrr"
   },
   "outputs": [],
   "source": [
    "class Agent:\n",
    "  \n",
    "    ''' \n",
    "    agent's constructor\n",
    "    '''\n",
    "    def __init__(self, nA):\n",
    "        self.nA = nA\n",
    "        self.Q = defaultdict(lambda: np.zeros(self.nA))\n",
    "    \n",
    "    '''\n",
    "    trains the agent over a fixed number of episodes using Q-learning\n",
    "    '''\n",
    "    def train(self, env, num_episodes, alpha, gamma, epsilon):\n",
    "        counts = []\n",
    "        rewards = []\n",
    "        max_reward = -math.inf\n",
    "    \n",
    "        for i_episode in range(1, num_episodes+1):\n",
    "            state = env.reset()\n",
    "            done = False\n",
    "            count = 0\n",
    "            tot_reward = 0\n",
    "            eps = max(epsilon * i_episode, epsilon_max)\n",
    "      \n",
    "            while not done:\n",
    "                action = select_epsilon_greedy_action(self.Q[state], self.nA, eps)\n",
    "                next_state, reward, done, _ = env.step(action)\n",
    "                count += 1\n",
    "                tot_reward += reward\n",
    "        \n",
    "                '''\n",
    "                updates internal Q table using Q-learning update rule\n",
    "                '''\n",
    "                self.Q[state][action] += alpha * (reward + gamma * self.Q[next_state][np.argmax(self.Q[next_state])] - self.Q[state][action])\n",
    "                state = next_state\n",
    "        \n",
    "            counts.append(count)\n",
    "            rewards.append(tot_reward)\n",
    "            max_reward = max(max_reward, tot_reward)\n",
    "   \n",
    "        return counts, rewards\n",
    "      \n",
    "    '''\n",
    "    plays with the environment\n",
    "    '''\n",
    "    def play(self, env):\n",
    "        state = env.reset()\n",
    "        done = False\n",
    "        tot_reward = 0\n",
    "    \n",
    "        while not done:\n",
    "            action = select_greedy_action(self.Q[state])\n",
    "            state, reward, done, _ = env.step(action)\n",
    "            tot_reward += reward\n",
    "      \n",
    "            info = {'state': state, 'action': action, 'reward': reward, 'done': done}\n",
    "            render(env, info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 3417
    },
    "colab_type": "code",
    "id": "dSzYgYHWGeJS",
    "outputId": "760dae37-d5c0-4f33-b4df-57d736ac844d"
   },
   "outputs": [],
   "source": [
    "env = gym.make('Taxi-v2')\n",
    "\n",
    "agent = Agent(env.action_space.n)\n",
    "train_counts, train_rewards = agent.train(env, 20000, 0.1, 0.9, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_figures(train_counts, train_rewards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1734
    },
    "colab_type": "code",
    "id": "Z_dg5ZFyfBaP",
    "outputId": "8714d5df-047e-4f4e-fcac-9821a93ad157"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+\n",
      "|R: | : :\u001b[34;1mG\u001b[0m|\n",
      "| : : : : |\n",
      "|\u001b[43m \u001b[0m: : : : |\n",
      "| | : | : |\n",
      "|\u001b[35mY\u001b[0m| : |B: |\n",
      "+---------+\n",
      "  (North)\n",
      "state: 206\n",
      "action: 1\n",
      "reward: -1\n",
      "+---------+\n",
      "|R: | : :\u001b[34;1mG\u001b[0m|\n",
      "|\u001b[43m \u001b[0m: : : : |\n",
      "| : : : : |\n",
      "| | : | : |\n",
      "|\u001b[35mY\u001b[0m| : |B: |\n",
      "+---------+\n",
      "  (North)\n",
      "state: 106\n",
      "action: 1\n",
      "reward: -1\n",
      "+---------+\n",
      "|R: | : :\u001b[34;1mG\u001b[0m|\n",
      "| :\u001b[43m \u001b[0m: : : |\n",
      "| : : : : |\n",
      "| | : | : |\n",
      "|\u001b[35mY\u001b[0m| : |B: |\n",
      "+---------+\n",
      "  (East)\n",
      "state: 126\n",
      "action: 2\n",
      "reward: -1\n",
      "+---------+\n",
      "|R: | : :\u001b[34;1mG\u001b[0m|\n",
      "| : :\u001b[43m \u001b[0m: : |\n",
      "| : : : : |\n",
      "| | : | : |\n",
      "|\u001b[35mY\u001b[0m| : |B: |\n",
      "+---------+\n",
      "  (East)\n",
      "state: 146\n",
      "action: 2\n",
      "reward: -1\n",
      "+---------+\n",
      "|R: | : :\u001b[34;1mG\u001b[0m|\n",
      "| : : :\u001b[43m \u001b[0m: |\n",
      "| : : : : |\n",
      "| | : | : |\n",
      "|\u001b[35mY\u001b[0m| : |B: |\n",
      "+---------+\n",
      "  (East)\n",
      "state: 166\n",
      "action: 2\n",
      "reward: -1\n",
      "+---------+\n",
      "|R: | : :\u001b[34;1mG\u001b[0m|\n",
      "| : : : :\u001b[43m \u001b[0m|\n",
      "| : : : : |\n",
      "| | : | : |\n",
      "|\u001b[35mY\u001b[0m| : |B: |\n",
      "+---------+\n",
      "  (East)\n",
      "state: 186\n",
      "action: 2\n",
      "reward: -1\n",
      "+---------+\n",
      "|R: | : :\u001b[34;1m\u001b[43mG\u001b[0m\u001b[0m|\n",
      "| : : : : |\n",
      "| : : : : |\n",
      "| | : | : |\n",
      "|\u001b[35mY\u001b[0m| : |B: |\n",
      "+---------+\n",
      "  (North)\n",
      "state: 86\n",
      "action: 1\n",
      "reward: -1\n",
      "+---------+\n",
      "|R: | : :\u001b[42mG\u001b[0m|\n",
      "| : : : : |\n",
      "| : : : : |\n",
      "| | : | : |\n",
      "|\u001b[35mY\u001b[0m| : |B: |\n",
      "+---------+\n",
      "  (Pickup)\n",
      "state: 98\n",
      "action: 4\n",
      "reward: -1\n",
      "+---------+\n",
      "|R: | : :G|\n",
      "| : : : :\u001b[42m_\u001b[0m|\n",
      "| : : : : |\n",
      "| | : | : |\n",
      "|\u001b[35mY\u001b[0m| : |B: |\n",
      "+---------+\n",
      "  (South)\n",
      "state: 198\n",
      "action: 0\n",
      "reward: -1\n",
      "+---------+\n",
      "|R: | : :G|\n",
      "| : : :\u001b[42m_\u001b[0m: |\n",
      "| : : : : |\n",
      "| | : | : |\n",
      "|\u001b[35mY\u001b[0m| : |B: |\n",
      "+---------+\n",
      "  (West)\n",
      "state: 178\n",
      "action: 3\n",
      "reward: -1\n",
      "+---------+\n",
      "|R: | : :G|\n",
      "| : : : : |\n",
      "| : : :\u001b[42m_\u001b[0m: |\n",
      "| | : | : |\n",
      "|\u001b[35mY\u001b[0m| : |B: |\n",
      "+---------+\n",
      "  (South)\n",
      "state: 278\n",
      "action: 0\n",
      "reward: -1\n",
      "+---------+\n",
      "|R: | : :G|\n",
      "| : : : : |\n",
      "| : :\u001b[42m_\u001b[0m: : |\n",
      "| | : | : |\n",
      "|\u001b[35mY\u001b[0m| : |B: |\n",
      "+---------+\n",
      "  (West)\n",
      "state: 258\n",
      "action: 3\n",
      "reward: -1\n",
      "+---------+\n",
      "|R: | : :G|\n",
      "| : : : : |\n",
      "| :\u001b[42m_\u001b[0m: : : |\n",
      "| | : | : |\n",
      "|\u001b[35mY\u001b[0m| : |B: |\n",
      "+---------+\n",
      "  (West)\n",
      "state: 238\n",
      "action: 3\n",
      "reward: -1\n",
      "+---------+\n",
      "|R: | : :G|\n",
      "| : : : : |\n",
      "|\u001b[42m_\u001b[0m: : : : |\n",
      "| | : | : |\n",
      "|\u001b[35mY\u001b[0m| : |B: |\n",
      "+---------+\n",
      "  (West)\n",
      "state: 218\n",
      "action: 3\n",
      "reward: -1\n",
      "+---------+\n",
      "|R: | : :G|\n",
      "| : : : : |\n",
      "| : : : : |\n",
      "|\u001b[42m_\u001b[0m| : | : |\n",
      "|\u001b[35mY\u001b[0m| : |B: |\n",
      "+---------+\n",
      "  (South)\n",
      "state: 318\n",
      "action: 0\n",
      "reward: -1\n",
      "+---------+\n",
      "|R: | : :G|\n",
      "| : : : : |\n",
      "| : : : : |\n",
      "| | : | : |\n",
      "|\u001b[35m\u001b[42mY\u001b[0m\u001b[0m| : |B: |\n",
      "+---------+\n",
      "  (South)\n",
      "state: 418\n",
      "action: 0\n",
      "reward: -1\n",
      "+---------+\n",
      "|R: | : :G|\n",
      "| : : : : |\n",
      "| : : : : |\n",
      "| | : | : |\n",
      "|\u001b[35m\u001b[42mY\u001b[0m\u001b[0m| : |B: |\n",
      "+---------+\n",
      "  (Dropoff)\n",
      "state: 418\n",
      "action: 5\n",
      "reward: 20\n",
      "game ended\n",
      "you won! :)\n"
     ]
    }
   ],
   "source": [
    "frames = agent.play(env)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "openAI-gym-taxi-v2.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.7.0"
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 },
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