Taxi-v3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+\n",
      "|R: | : :G|\n",
      "| : | :\u001b[43m \u001b[0m: |\n",
      "| : : : : |\n",
      "| | : | : |\n",
      "|\u001b[35mY\u001b[0m| : |\u001b[34;1mB\u001b[0m: |\n",
      "+---------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import gym\n",
    "\n",
    "env = gym.make(\"Taxi-v3\").env\n",
    "\n",
    "env.render()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+\n",
      "|\u001b[34;1mR\u001b[0m: | : :G|\n",
      "| : | : : |\n",
      "| : : : : |\n",
      "| | : | : |\n",
      "|\u001b[35mY\u001b[0m| : |B:\u001b[43m \u001b[0m|\n",
      "+---------+\n",
      "\n",
      "Action Space Discrete(6)\n",
      "State Space Discrete(500)\n"
     ]
    }
   ],
   "source": [
    "env.reset() # reset environment to a new, random state\n",
    "env.render()\n",
    "\n",
    "print(\"Action Space {}\".format(env.action_space))\n",
    "print(\"State Space {}\".format(env.observation_space))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "State: 328\n",
      "+---------+\n",
      "|\u001b[35mR\u001b[0m: | : :G|\n",
      "| : | : : |\n",
      "| : : : : |\n",
      "| |\u001b[43m \u001b[0m: | : |\n",
      "|\u001b[34;1mY\u001b[0m| : |B: |\n",
      "+---------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "state = env.encode(3, 1, 2, 0) # (taxi row, taxi column, passenger index, destination index)\n",
    "print(\"State:\", state)\n",
    "\n",
    "env.s = state\n",
    "env.render()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: [(1.0, 428, -1, False)],\n",
       " 1: [(1.0, 228, -1, False)],\n",
       " 2: [(1.0, 348, -1, False)],\n",
       " 3: [(1.0, 328, -1, False)],\n",
       " 4: [(1.0, 328, -10, False)],\n",
       " 5: [(1.0, 328, -10, False)]}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.P[328]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Timesteps taken: 4525\n",
      "Penalties incurred: 1510\n"
     ]
    }
   ],
   "source": [
    "env.s = 328  # set environment to illustration's state\n",
    "\n",
    "epochs = 0\n",
    "penalties, reward = 0, 0\n",
    "\n",
    "frames = [] # for animation\n",
    "\n",
    "done = False\n",
    "\n",
    "while not done:\n",
    "    action = env.action_space.sample()\n",
    "    state, reward, done, info = env.step(action)\n",
    "\n",
    "    if reward == -10:\n",
    "        penalties += 1\n",
    "    \n",
    "    # Put each rendered frame into dict for animation\n",
    "    frames.append({\n",
    "        'frame': env.render(mode='ansi'),\n",
    "        'state': state,\n",
    "        'action': action,\n",
    "        'reward': reward\n",
    "        }\n",
    "    )\n",
    "\n",
    "    epochs += 1\n",
    "    \n",
    "    \n",
    "print(\"Timesteps taken: {}\".format(epochs))\n",
    "print(\"Penalties incurred: {}\".format(penalties))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "q_table = np.zeros([env.observation_space.n, env.action_space.n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode: 100000\n",
      "Training finished.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Training the agent\"\"\"\n",
    "\n",
    "import random\n",
    "from time import sleep\n",
    "from IPython.display import clear_output\n",
    "\n",
    "# Hyperparameters\n",
    "alpha = 0.1\n",
    "gamma = 0.6\n",
    "epsilon = 0.1\n",
    "\n",
    "# For plotting metrics\n",
    "all_epochs = []\n",
    "all_penalties = []\n",
    "\n",
    "for i in range(1, 100001):\n",
    "    state = env.reset()\n",
    "\n",
    "    epochs, penalties, reward, = 0, 0, 0\n",
    "    done = False\n",
    "    \n",
    "    while not done:\n",
    "        if random.uniform(0, 1) < epsilon:\n",
    "            action = env.action_space.sample() # Explore action space\n",
    "        else:\n",
    "            action = np.argmax(q_table[state]) # Exploit learned values\n",
    "\n",
    "        next_state, reward, done, info = env.step(action) \n",
    "        \n",
    "        old_value = q_table[state, action]\n",
    "        next_max = np.max(q_table[next_state])\n",
    "        \n",
    "        new_value = (1 - alpha) * old_value + alpha * (reward + gamma * next_max)\n",
    "        q_table[state, action] = new_value\n",
    "\n",
    "        if reward == -10:\n",
    "            penalties += 1\n",
    "\n",
    "        state = next_state\n",
    "        epochs += 1\n",
    "        \n",
    "    if i % 100 == 0:\n",
    "        # clear_output(wait=True)\n",
    "        print(f\"\\rEpisode: {i}\", end=\"\")\n",
    "\n",
    "print(f\"\\rEpisode: {i}\")\n",
    "print(\"Training finished.\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -2.39516231,  -2.27325184,  -2.4100308 ,  -2.35324091,\n",
       "       -10.86405863, -10.73672792])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_table[328]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results after 100 episodes:\n",
      "Average timesteps per episode: 13.41\n",
      "Average penalties per episode: 0.0\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Evaluate agent's performance after Q-learning\"\"\"\n",
    "\n",
    "total_epochs, total_penalties = 0, 0\n",
    "episodes = 100\n",
    "\n",
    "for _ in range(episodes):\n",
    "    state = env.reset()\n",
    "    epochs, penalties, reward = 0, 0, 0\n",
    "    \n",
    "    done = False\n",
    "    \n",
    "    while not done:\n",
    "        action = np.argmax(q_table[state])\n",
    "        state, reward, done, info = env.step(action)\n",
    "\n",
    "        if reward == -10:\n",
    "            penalties += 1\n",
    "\n",
    "        epochs += 1\n",
    "\n",
    "    total_penalties += penalties\n",
    "    total_epochs += epochs\n",
    "\n",
    "print(f\"Results after {episodes} episodes:\")\n",
    "print(f\"Average timesteps per episode: {total_epochs / episodes}\")\n",
    "print(f\"Average penalties per episode: {total_penalties / episodes}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# View example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+\n",
      "|R: | : :G|\n",
      "| : | : : |\n",
      "| : : : : |\n",
      "| | : | : |\n",
      "|Y| : |\u001b[35m\u001b[34;1m\u001b[43mB\u001b[0m\u001b[0m\u001b[0m: |\n",
      "+---------+\n",
      "  (Dropoff)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "total_epochs, total_penalties = 0, 0\n",
    "episodes = 1\n",
    "\n",
    "for _ in range(episodes):\n",
    "    state = env.reset()\n",
    "    epochs, penalties, reward = 0, 0, 0\n",
    "    \n",
    "    done = False\n",
    "    \n",
    "    while not done:\n",
    "        action = np.argmax(q_table[state])\n",
    "        state, reward, done, info = env.step(action)\n",
    "\n",
    "        clear_output(wait=True)\n",
    "        print(env.render(mode='ansi'))\n",
    "        sleep(.1)\n",
    "\n",
    "        if reward == -10:\n",
    "            penalties += 1\n",
    "\n",
    "        epochs += 1\n",
    "\n",
    "    total_penalties += penalties\n",
    "    total_epochs += epochs"
   ]
  }
 ],
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