Taxi-environment.ipynb

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   "source": [
    "# Basic Q-Learning Algorithm"
   ]
  },
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   "execution_count": 1,
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   "source": [
    "import sys\n",
    "import gym\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2017-11-17 14:51:25,625] Making new env: Taxi-v2\n"
     ]
    }
   ],
   "source": [
    "env = gym.make('Taxi-v2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+\n",
      "|R: | : :\u001b[35mG\u001b[0m|\n",
      "| : : : :\u001b[43m \u001b[0m|\n",
      "| : : : : |\n",
      "| | : | : |\n",
      "|\u001b[34;1mY\u001b[0m| : |B: |\n",
      "+---------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "env.render()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
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   "outputs": [],
   "source": [
    "num_states = env.observation_space.n\n",
    "num_actions = env.action_space.n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Q = np.zeros(shape=[num_states, num_actions])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "gamma = 0.5   # discount term (µ)\n",
    "LR = 0.01     # learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episodes: 10,000\tReward: 20\tG: 10\tTotal rewards: 51,1088"
     ]
    }
   ],
   "source": [
    "total_rewards = 0\n",
    "for episode in range(1, 10000):\n",
    "    done = False\n",
    "    G, reward = 0,0\n",
    "    state = env.reset()\n",
    "    while done != True:\n",
    "        action = np.argmax(Q[state]) #1\n",
    "        state2, reward, done, info = env.step(action) #2s\n",
    "        Q[state, action] += gamma * (reward + np.max(Q[state2]) - Q[state, action]) #3\n",
    "        G += reward\n",
    "        total_rewards += reward\n",
    "        state = state2\n",
    "        sys.stdout.write('\\rEpisodes: {:,}\\tReward: {:,}\\tG: {:,}\\tTotal rewards: {:,}'.format(episode+1, reward, G, total_rewards))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+\n",
      "|R: | : :G|\n",
      "| : : : : |\n",
      "| : : : : |\n",
      "| | : | : |\n",
      "|\u001b[35m\u001b[42mY\u001b[0m\u001b[0m| : |B: |\n",
      "+---------+\n",
      "  (Dropoff)\n"
     ]
    }
   ],
   "source": [
    "env.render()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Q[state]:\n",
      "[ -5.00000000e-01  -5.00000000e-01  -5.00000000e-01  -5.00000000e-01\n",
      "  -5.00000000e+00   2.57200000e+04]\n",
      "\n",
      "Q[state, action]: 25720.0\n",
      "\n",
      "reward = 20\tinfo = {'prob': 1.0}\n"
     ]
    }
   ],
   "source": [
    "print(f'\\nQ[state]:\\n{Q[state]}')\n",
    "\n",
    "print(f'\\nQ[state, action]: {Q[state, action]}')\n",
    "\n",
    "print(f'\\nreward = {reward}\\tinfo = {info}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "env.close()"
   ]
  }
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