rl-tetris.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tetris import Tetris\n",
    "from random import randint, shuffle\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import animation, rc, colors, image\n",
    "from IPython.display import HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize game states:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def animate(states):\n",
    "    'Creates animation from states list.'\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cmap = colors.ListedColormap(['black', 'red', 'blue'])\n",
    "    norm = colors.Normalize(0, 2)\n",
    "    ax.axis('off')\n",
    "    images = []\n",
    "    for state in states:\n",
    "        well, score, is_game_over = state\n",
    "        sc = ax.annotate(f\"score: {score}\",(1,19), color='white')\n",
    "        image = ax.imshow(well, cmap=cmap, norm=norm)\n",
    "        if is_game_over:\n",
    "            go = ax.annotate(\"GAME OVER\", (2, 10), color='white')\n",
    "            images = images + ([[image, go] * 3, [image] * 3] * 10)\n",
    "            break\n",
    "        images.append([image, sc])\n",
    "\n",
    "    return animation.ArtistAnimation(fig, images, interval=50, blit=True,\n",
    "                                    repeat_delay=1000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def play(player, max_steps = 10000):\n",
    "    'Plays tetris with player and returns list of states.'\n",
    "    tetris = Tetris()\n",
    "    states = [tetris.step(0)]\n",
    "    is_game_over = False\n",
    "    for _ in range(max_steps):\n",
    "        state = tetris.step(player(states[-1][0]))\n",
    "        states.append(state)\n",
    "        _,_,is_game_over = state\n",
    "        if is_game_over:\n",
    "            break\n",
    "    return states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def random_player(well):\n",
    "    'Returns random action.'\n",
    "    return randint(0, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "states = play(random_player)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc824ac3630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ani = animate(states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(ani.to_html5_video())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start very deep learning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/max/anaconda3/envs/tensorflow/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gather_columns(params, indices):\n",
    "    # prepare row indices\n",
    "    row_indices = tf.range(tf.shape(indices)[0])\n",
    "    # zip row indices with column indices\n",
    "    full_indices = tf.stack([row_indices, indices], axis=1)\n",
    "    # retrieve values by indices\n",
    "    return tf.gather_nd(params, full_indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q-function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def build_q_function(resolution, n_actions):\n",
    "    with tf.variable_scope(\"Q_function\"):\n",
    "        well_pl = tf.placeholder(shape=[None] + resolution + [2], dtype=tf.float32, name=\"well\")\n",
    "        reward_pl = tf.placeholder(shape=[None], dtype=tf.float32, name=\"reward\")\n",
    "        action_pl = tf.placeholder(shape=[None], dtype=tf.int32, name=\"action\")\n",
    "        \n",
    "        conv_1 = tf.layers.conv2d(well_pl, 32, 4, strides=(1,1), activation=tf.nn.relu, name=\"conv_1\", padding=\"VALID\")\n",
    "        pool_1 = tf.nn.max_pool(conv_1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=\"VALID\", name=\"pool_1\")\n",
    "        conv_2 = tf.layers.conv2d(pool_1, 32, 2, strides=(1,1), activation=tf.nn.relu, name=\"conv_2\")\n",
    "        pool_2 = tf.nn.max_pool(conv_2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=\"VALID\", name=\"pool_2\")\n",
    "        flatten = tf.contrib.layers.flatten(pool_2)\n",
    "        print(flatten.shape)\n",
    "        dense1 = tf.layers.dense(flatten, 192, activation=tf.nn.relu, name=\"dense1\")\n",
    "        #dense2 = tf.layers.dense(dense1, 3200, activation=tf.nn.relu, name=\"dense2\")\n",
    "        output = tf.layers.dense(dense1, n_actions, name=\"output\")\n",
    "        q_value = gather_columns(output, action_pl)\n",
    "        #mask = tf.one_hot(action_pl, n_actions)\n",
    "        #masked = tf.multiply(output, mask)\n",
    "        #q_value = tf.reduce_sum(masked, axis=1)\n",
    "        loss = tf.reduce_mean(tf.squared_difference(reward_pl, q_value))\n",
    "        train_op = tf.train.AdamOptimizer().minimize(loss, global_step=tf.train.get_or_create_global_step())\n",
    "        \n",
    "        summary = tf.summary.merge(\n",
    "                [tf.summary.scalar(\"loss\", loss),\n",
    "                tf.summary.histogram(\"q_value\", q_value)])\n",
    "        \n",
    "        return (well_pl, reward_pl, action_pl), (output, loss, train_op, summary)        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def build_estimator(sess, well_pl, output):\n",
    "    \n",
    "    def estimate(sess, well_pl, output, wells):\n",
    "        feed_dict = { \n",
    "            well_pl: wells\n",
    "        }\n",
    "        return sess.run([output], feed_dict)\n",
    "    \n",
    "    def estimator(wells):\n",
    "        return estimate(sess, well_pl, output, wells)\n",
    "    \n",
    "    return estimator;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def build_trainer(sess, well_pl, action_pl, reward_pl, loss, train_op, summary, writer):\n",
    "    \n",
    "    def train(sess, well_pl, reward_pl, action_pl, loss, train_op, summary, wells, actions, rewards):\n",
    "        feed_dict = { \n",
    "            well_pl: wells,\n",
    "            reward_pl: rewards,\n",
    "            action_pl: actions\n",
    "        }\n",
    "        \n",
    "        return sess.run([loss, train_op, summary, tf.train.get_or_create_global_step()], feed_dict)\n",
    "    \n",
    "    def trainer(wells, actions, rewards):\n",
    "        loss_val, _, summary_val, step = train(sess, well_pl, reward_pl, action_pl, \n",
    "                        loss, train_op, summary, wells, actions, rewards)\n",
    "        writer.add_summary(summary_val, step)\n",
    "        return loss_val\n",
    "    \n",
    "    return trainer;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split well to channel with figure and channel with trash:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def preprocess(well):\n",
    "    return np.array([[[v == 1, v == 2] for v in row] for row in well], dtype=float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(?, 96)\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "(well_pl, reward_pl, action_pl), (output, loss, train_op, summary) = build_q_function([20, 10], 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/job:localhost/replica:0/task:0/device:CPU:0\n",
      "/job:localhost/replica:0/task:0/device:GPU:0\n"
     ]
    }
   ],
   "source": [
    "devices = sess.list_devices()\n",
    "for d in devices:\n",
    "    print(d.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "estimate = build_estimator(sess, well_pl, output)\n",
    "dirpath = 'tfboard'\n",
    "if tf.gfile.Exists(dirpath):\n",
    "   tf.gfile.DeleteRecursively(dirpath) \n",
    "writer = tf.summary.FileWriter(dirpath, sess.graph)\n",
    "train = build_trainer(sess, well_pl, action_pl, reward_pl, loss, train_op, summary, writer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# train_op = tf.train.AdamOptimizer().minimize(loss, global_step=tf.train.get_or_create_global_step())\n",
    "# train = build_trainer(sess, well_pl, action_pl, reward_pl, loss, train_op, summary, writer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reward calculation helpers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calc_well_stat(x):\n",
    "    'Returns tuple of filled cells count and partialy filled rows count'\n",
    "    non_empty_rows = 0\n",
    "    filled_cells = 0\n",
    "    for i in range(len(x)):\n",
    "        s = sum([f[0] for f in x[i]])\n",
    "        if s != 0:\n",
    "            non_empty_rows += 1\n",
    "            filled_cells += s\n",
    "    return (filled_cells, non_empty_rows)\n",
    "\n",
    "def is_figure_touch(x):\n",
    "    for i in range(len(x)):\n",
    "        for j in range(len(x[i])):\n",
    "            if x[i][j][1] > 0:\n",
    "                return False\n",
    "    return True\n",
    "\n",
    "def calc_lines_entropy(x):\n",
    "    n_tears = 0\n",
    "    n_lines = 0\n",
    "    for i in range(len(x)):\n",
    "        prev = x[i][0][0]\n",
    "        n_line_tears = 0\n",
    "        for j in range(1, len(x[i])):\n",
    "            if prev != x[i][j][0]:\n",
    "                n_line_tears += 1\n",
    "                prev = x[i][j][0]\n",
    "        if n_line_tears > 0:\n",
    "            n_lines += 1\n",
    "        n_tears += n_line_tears\n",
    "    return n_lines, n_tears"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Helpers test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "well = [[0] * 10] * 10 + [[1] * 10] * 10\n",
    "x = preprocess(well)\n",
    "assert calc_well_stat(x) == (100, 10)\n",
    "\n",
    "assert is_figure_touch(x)\n",
    "\n",
    "# Add figure\n",
    "well[0] = [0] * 3 + [2] * 4 + [0] * 3\n",
    "assert not is_figure_touch(preprocess(well))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We generate randomly filled rows to increase probability to drop line with random action: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generate_well(max_filled_rows = 17, max_tears = 5):\n",
    "    n_rows = randint(0, max_filled_rows)\n",
    "    well = [[0] * 10 for _ in range(20 - n_rows)]\n",
    "    for _ in range(n_rows):\n",
    "        n_tears = randint(1, max_tears)\n",
    "        tear_ind = np.random.choice(10, n_tears, replace=False)\n",
    "        row = [1] * 10\n",
    "        for i in tear_ind:\n",
    "            row[i] = 0\n",
    "        well.append(row)\n",
    "    return well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generate_action_sequence(drop_prob=0.5):\n",
    "    # move left\n",
    "    actions = [randint(1, 2)] * randint(0, 4)\n",
    "    # move right\n",
    "    actions += [3] * randint(0, 3)\n",
    "    # drop or leave\n",
    "    if np.random.rand() < drop_prob:\n",
    "        actions.append(4)\n",
    "    else:\n",
    "        actions += [0] * 20\n",
    "    return actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generate_sample_sequence():\n",
    "    while True:\n",
    "        well = generate_well(10)\n",
    "        t = Tetris(well = well)\n",
    "        actions = generate_action_sequence()\n",
    "        samples = []\n",
    "        prev_state = preprocess(well)\n",
    "        for action in actions:\n",
    "            well, score, is_game_over = t.step(action)\n",
    "            state = preprocess(well)\n",
    "            sample = prev_state, action, score / 100, state, is_game_over\n",
    "            samples.append(sample)\n",
    "            prev_state = state\n",
    "            if score > 0:\n",
    "                break\n",
    "            if is_game_over:\n",
    "                break \n",
    "        if len(samples) > 0:\n",
    "            yield samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class ReplayBuffer:\n",
    "    def __init__(self, capacity):\n",
    "        self.__capacity = capacity\n",
    "        self.__buffer = []\n",
    "        self.__index = 0\n",
    "        \n",
    "    def add(self, entry):\n",
    "        if len(self.__buffer) < self.__capacity:\n",
    "            self.__buffer.append(entry)\n",
    "        else:\n",
    "            self.__buffer[self.__index] = entry\n",
    "            self.__index += 1\n",
    "            if self.__index >= self.__capacity:\n",
    "                self.__index = 0\n",
    "                \n",
    "    def prepare_batch(self, batch_size, gamma):\n",
    "        if batch_size < 1:\n",
    "            return [], [], []\n",
    "        indices = np.random.choice(len(self.__buffer), min(batch_size, len(self.__buffer)), replace=False)\n",
    "        X = [self.__buffer[i][0] for i in indices]\n",
    "        actions = [self.__buffer[i][1] for i in indices]\n",
    "        rewards = np.array([self.__buffer[i][2] for i in indices])\n",
    "        X_next = [self.__buffer[i][3] for i in indices]\n",
    "        terminal_flags = np.array([not self.__buffer[i][4] for i in indices], dtype = float)\n",
    "        q_values = np.squeeze(np.max(estimate(X_next), axis=-1))\n",
    "        y_train = rewards + q_values * terminal_flags * gamma\n",
    "        return X, actions, y_train   \n",
    "    \n",
    "    def save(self, file_name):\n",
    "        with open(file_name, 'wb') as file:\n",
    "            pickle.dump(self.__buffer, file)\n",
    "            \n",
    "    def load(self, file_name):\n",
    "        with open(file_name, 'rb') as file:\n",
    "            self.__buffer = pickle.load(file)\n",
    "        self.__index = 0\n",
    "        \n",
    "    def __len__(self):\n",
    "         return len(self.__buffer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fill_reply_buffers(positvie_buffer, other_buffer, samples_n = 10000):\n",
    "    positive_added_n = 0\n",
    "    for seq in generate_sample_sequence():\n",
    "        if seq[-1][2] > 0:\n",
    "            positvie_buffer.add(seq[-1])\n",
    "            positive_added_n += 1\n",
    "            if (positive_added_n >= samples_n):\n",
    "                break\n",
    "            print(f\"Positive added: {positive_added_n}\", end='\\r')\n",
    "        else:\n",
    "            for sample in seq: other_buffer.add(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "positive_buffer = ReplayBuffer(100000)\n",
    "other_buffer = ReplayBuffer(100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "fill_reply_buffers(positive_buffer, other_buffer, samples_n=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#positive_buffer.load(\"pb.bin\")\n",
    "#other_buffer.load(\"ob.bin\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learning session:\n",
    "    eps - random action probability\n",
    "    gamma - discount factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def learn(positive_buffer, \n",
    "          other_buffer,\n",
    "          estimate, \n",
    "          train, \n",
    "          eps=0.9, \n",
    "          gamma=0.99, \n",
    "          replay_capacity=100000, \n",
    "          batch_size=32, \n",
    "          episode_n=100, \n",
    "          steps_n=1000):\n",
    "        \n",
    "    max_score = 0\n",
    "    for i in range(episode_n):\n",
    "        tetris = Tetris(well=generate_well(15))\n",
    "        well, score, _ = tetris.step(0)\n",
    "        x = preprocess(well)\n",
    "        actions = []\n",
    "        for j in range(steps_n):\n",
    "            if len(actions) == 0:\n",
    "                if np.random.rand() < eps:\n",
    "                    actions = [randint(0, 4)] # * randint(1, 4)\n",
    "                else:\n",
    "                    actions = [np.argmax(estimate([x]))]\n",
    "            a = actions.pop()\n",
    "            well, new_score, is_game_over = tetris.step(a)\n",
    "            new_x = preprocess(well)\n",
    "            r = float(new_score - score)\n",
    "            score = new_score\n",
    "            replay_entry = (x, a, r, new_x, is_game_over)\n",
    "            x = new_x\n",
    "            if r > 0:\n",
    "                positive_buffer.add(replay_entry)\n",
    "            else:\n",
    "                other_buffer.add(replay_entry)\n",
    "            n_positive_samples = min(len(positive_buffer), batch_size // 4)\n",
    "            batch_pos = positive_buffer.prepare_batch(n_positive_samples , gamma) \n",
    "            batch_other = other_buffer.prepare_batch(batch_size - n_positive_samples , gamma)\n",
    "            batch_zipped = list(zip(*batch_pos)) + list(zip(*batch_other))\n",
    "            shuffle(batch_zipped)\n",
    "            state_batch, actions_batch, rewards_batch = list(zip(*batch_zipped))\n",
    "            loss = train(state_batch, actions_batch, rewards_batch)\n",
    "            print(f\"Episode: {i}, step: {j}, reward: {r:.2}, score: {score}, max score: {max_score}, loss: {loss:.3}     \", end='\\r')\n",
    "            if is_game_over:\n",
    "                break\n",
    "        max_score = max(max_score, score)\n",
    "    return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learn(positive_buffer, other_buffer, estimate, train, eps=0.5, episode_n=100, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def neuro_player(well):\n",
    "    'Returns model predicted action.'\n",
    "    return np.argmax(estimate([preprocess(well)]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start session:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eps: 0.2, steps: 137, score: 0                                              7          \n",
      "Eps: 0.18, steps: 121, score: 0                                                        \n",
      "Eps: 0.16, steps: 109, score: 0                                              8         \n",
      "Episode: 40, step: 24, reward: 0.0, score: 0, max score: 0, loss: 8.58e+07      \r"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-53-13656cebcf87>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0meps\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mlearn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpositive_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepisode_n\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mstates\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mneuro_player\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'replay.bin'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-24-eddc901cc2ce>\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(positive_buffer, other_buffer, estimate, train, eps, gamma, replay_capacity, batch_size, episode_n, steps_n)\u001b[0m\n\u001b[1;32m     34\u001b[0m                 \u001b[0mother_buffer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreplay_entry\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m             \u001b[0mn_positive_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpositive_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m             \u001b[0mbatch_pos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpositive_buffer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_positive_samples\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mgamma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m             \u001b[0mbatch_other\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mother_buffer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mn_positive_samples\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mgamma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m             \u001b[0mbatch_zipped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mbatch_pos\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mbatch_other\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-21-90e4220467a0>\u001b[0m in \u001b[0;36mprepare_batch\u001b[0;34m(self, batch_size, gamma)\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m         \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0mactions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for eps in np.linspace(0.2, 0.0, 11):\n",
    "    learn(positive_buffer, other_buffer, estimate, train, eps=eps, episode_n=1000, batch_size=256)\n",
    "    states = play(neuro_player)\n",
    "    with open('replay.bin', 'wb') as file:\n",
    "        pickle.dump(states, file)\n",
    "    print(f\"Eps: {eps:.2}, steps: {len(states)}, score: {states[-1][1]}                                              \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check how model plays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_score = 0\n",
    "for _ in range(1000):\n",
    "    states = play(neuro_player)\n",
    "    if states[-1][1] > max_score:\n",
    "        max_score = states[-1][1]\n",
    "        best_game = states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "states = best_game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7420bcd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ani = animate(states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(ani.to_html5_video())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ani.save(\"tetris.mp4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (Tensorflow)",
   "language": "python",
   "name": "tf"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}