dqn.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "dqn.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "xamxypyH0f6C"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/jarrydmartinx/SML-Notebooks/blob/master/dqn.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "Yi1TMRm_uazp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# DQN"
      ]
    },
    {
      "metadata": {
        "id": "QYsLMjgzJylG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Questions\n",
        "\n",
        "\n",
        "*   How do I use Tensorboard from Colab\n",
        "*  (Suraj) Is it best to distribute a subgraph which has internal dependencies but is parallelisable, or to give it to one GPU?\n",
        "*   I've forgotten which this comma is here: e.g. (self.capacity,)\n",
        "*   Why use lambda vs. list comprehension. I read list comprehension is a complete substitute, but with lambda i can reuse it right, whereas list comp I can't reuse? In light of the supposed equivalence of the two it blows my mind that we used a lambda inside a list comprehension :-*\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "EE82I_N23pAQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "! pip install -q dm-sonnet  # Sonnet not inc by default\n",
        "\n",
        "## For OpenAI gym\n",
        "# python-opengl not installed since we are not rendering anything\n",
        "# Note: python-opengl maybe reqruired if we monitor to file - check this out: https://stackoverflow.com/questions/40195740/how-to-run-openai-gym-render-over-a-server\n",
        "# python-numpy not installed since a numpy package is already present\n",
        "# Not sure about the others, need to isolate each package to see if they are all needed.\n",
        "# cmake is definitely needed for the atari environment.\n",
        "# ! apt-get -qq install python-dev cmake zlib1g-dev libjpeg-dev xvfb libav-tools xorg-dev libboost-all-dev libsdl2-dev swig\n",
        "! pip install -U -q gym\n",
        "! pip install -U -q plotnine"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XRGFZ-BortNh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "7ec9d348-2f13-462b-b144-c727b0ce3126"
      },
      "cell_type": "code",
      "source": [
        "import gym\n",
        "import numpy as np\n",
        "import sonnet as snt\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "\n",
        "import plotnine as gg\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
            "  from pandas.core import datetools\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "XA9W4e7Ur0v4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Ingredients\n",
        "1. make a neural net to approximate Q(s, a)\n",
        "2. make a replay buffer (list basically, could use ring buffer etc.)\n",
        "3. have a simple environment to debug with"
      ]
    },
    {
      "metadata": {
        "id": "lRIoZsuTuCma",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class QNetwork(snt.AbstractModule):\n",
        "  \"\"\"A simple feedforward multi-layer-perceptron Q-function approximator.\n",
        "  \n",
        "  Q: State --> [vector of Q values, one for each action]\n",
        "  \n",
        "  Shapes:\n",
        "  [batch_size, height, width] --> [batch_size, num_actions]\n",
        "  \n",
        "\n",
        "  \"\"\"\n",
        "\n",
        "  def __init__(self, hidden_sizes, num_actions):\n",
        "    \"\"\"Instantiate our kick-ass Q-network.\n",
        "    \n",
        "    Args:\n",
        "      hidden_sizes: A list of integers, corresponding to the number of neurons\n",
        "        in each of the hidden layers of the MLP.\n",
        "      num_actions: Number of output actions.\n",
        "    \n",
        "    \"\"\"\n",
        "    super(QNetwork, self).__init__(name='q_network')\n",
        "    # Calls constructor of superclass etc. Just ensures variables are shared,\n",
        "    # once module is plugged into the graph.\n",
        "\n",
        "    self._hidden_sizes = hidden_sizes\n",
        "    self._num_actions = num_actions\n",
        "\n",
        "  def _build(self, inputs):\n",
        "    \"\"\"Hooks the module into the TensorFlow graph.\n",
        "    \n",
        "    Args:\n",
        "      inputs: A `tf.Tensor` of shape [batch_size, ...] and type tf.float32.\n",
        "      \n",
        "    Returns:\n",
        "      A `tf.Tensor` of shape [num_actions] and type tf.float32 corresponding to\n",
        "      [Q(s, a) for a in A]\n",
        "    \n",
        "    \"\"\"\n",
        "    # MLP expects a batch of vectors\n",
        "    inputs = snt.BatchFlatten()(inputs)  # Flatten the input image into a vector\n",
        "    \n",
        "    # Inputs should be of size [batch_size, x]\n",
        "    assert len(inputs.get_shape().as_list()) == 2  \n",
        "   \n",
        "    mlp = snt.nets.MLP(output_sizes=self._hidden_sizes + [self._num_actions])\n",
        "    \n",
        "    return mlp(inputs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xamxypyH0f6C",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Testing area"
      ]
    },
    {
      "metadata": {
        "id": "pLzw0uox2QSY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Can make MLP as an exercise\n",
        "class MyMLP():\n",
        "  pass\n",
        "\n",
        "  \n",
        "  # layer_n = snt.Linear(output_size=blah)\n",
        "  # tf.relu\n",
        "  # etc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "g8oblCwgx8VO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "outputId": "6c786801-e2b5-4d3b-b749-624929d008b7"
      },
      "cell_type": "code",
      "source": [
        "tf.reset_default_graph()\n",
        "\n",
        "# Build our model\n",
        "mlp = QNetwork(hidden_sizes=[30, 30], num_actions=5)  \n",
        "\n",
        "# Create a tensor that generates random noise images\n",
        "dummy_observation = tf.random_uniform(shape=(1, 32, 32, 3), dtype=tf.float32)\n",
        "\n",
        "# Plug our model into this image and get 'q-values' out\n",
        "q_values = mlp(dummy_observation)\n",
        "\n",
        "with tf.train.MonitoredSession() as sess:\n",
        "  q_val, dummy_val = sess.run([q_values, dummy_observation])\n",
        "  plt.imshow(np.squeeze(dummy_val))\n",
        "  print(q_val)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "[[-0.24581951 -0.24164805  0.04359115 -0.42192426 -0.30897924]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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cNxd4mZ9YdNLuXK8/U8lL5pr7q2LOqg/lFckBIKaivMr7Z+srmMbvOOayjekz+WZ6AFi2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\nSIHFkohIgcWSiEjh/wDZ8w1dmw6EvAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f9614eae898>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "HXuIb3pc1Xpk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "13cca476-9b13-4a92-b810-770ac0e39d96"
      },
      "cell_type": "code",
      "source": [
        "mlp.get_variables()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Variable 'q_network/mlp/linear_0/w:0' shape=(3072, 30) dtype=float32_ref>,\n",
              " <tf.Variable 'q_network/mlp/linear_0/b:0' shape=(30,) dtype=float32_ref>,\n",
              " <tf.Variable 'q_network/mlp/linear_1/w:0' shape=(30, 30) dtype=float32_ref>,\n",
              " <tf.Variable 'q_network/mlp/linear_1/b:0' shape=(30,) dtype=float32_ref>,\n",
              " <tf.Variable 'q_network/mlp/linear_2/w:0' shape=(30, 5) dtype=float32_ref>,\n",
              " <tf.Variable 'q_network/mlp/linear_2/b:0' shape=(5,) dtype=float32_ref>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "sOApUlW31Yrq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "0d494d18-3794-44ef-908d-08db8533c53f"
      },
      "cell_type": "code",
      "source": [
        "tf.trainable_variables()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tf.Variable 'q_network/mlp/linear_0/w:0' shape=(3072, 30) dtype=float32_ref>,\n",
              " <tf.Variable 'q_network/mlp/linear_0/b:0' shape=(30,) dtype=float32_ref>,\n",
              " <tf.Variable 'q_network/mlp/linear_1/w:0' shape=(30, 30) dtype=float32_ref>,\n",
              " <tf.Variable 'q_network/mlp/linear_1/b:0' shape=(30,) dtype=float32_ref>,\n",
              " <tf.Variable 'q_network/mlp/linear_2/w:0' shape=(30, 5) dtype=float32_ref>,\n",
              " <tf.Variable 'q_network/mlp/linear_2/b:0' shape=(5,) dtype=float32_ref>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "KE-W8bdmFoaQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Replay Buffer"
      ]
    },
    {
      "metadata": {
        "id": "_-79uD_RNri_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "d4c8d774-9ca7-449c-af5e-f7004ba78627"
      },
      "cell_type": "code",
      "source": [
        "class ReplayBuffer2(object):\n",
        "  \n",
        "  def __init__(self, capacity=1000):\n",
        "    \n",
        "    self._capacity = capacity\n",
        "    self._contents = None\n",
        "    self._num_inserted = 0\n",
        "\n",
        "  def add(self, transition):\n",
        "    if not self._contents:\n",
        "      # Takes a vector x to the expression/s after colon\n",
        "      zeros_maker = lambda x: np.zeros(shape=(self._capacity,) + x.shape, dtype=x.dtype)\n",
        "      # Calls lambda expression \n",
        "      self._contents = [zeros_maker(x) for x in transition]\n",
        "\n",
        "#      contents = [states_tm1, actions, rewards, dones, states_t]\n",
        "#      where:\n",
        "#           states_tm1 is shape (C, D)\n",
        "#           actions is shape(C)\n",
        "#           ...\n",
        "\n",
        "    # Get a 'safe' index (i.e. ring buffer shit)\n",
        "    slot = self._num_inserted % self._capacity\n",
        "\n",
        "#     for i in range(len(transition)):\n",
        "#       x = transition[i]\n",
        "#       # ...\n",
        "\n",
        "    for idx, x in enumerate(transition):\n",
        "\n",
        "      self._contents[idx][slot] = x\n",
        "\n",
        "      self._num_inserted += 1\n",
        "\n",
        "  def sample(self, batch_size):\n",
        "    if batch_size > self.size():\n",
        "      raise ValueError('Dude you dumbass, the replay doesnt have enough shit yet.')\n",
        "    \n",
        "    indices = np.random.randint(low=0, high=self.size(), size=batch_size)\n",
        "    # e.g. [3, 99, 64, 326, 480, ...] of length batch_size\n",
        "\n",
        "    return [x[indices] for x in self._contents]\n",
        "  \n",
        "  def ready_to_sample(self, batch_size):\n",
        "    return batch_size < self.size()\n",
        "  \n",
        "  def size(self):\n",
        "    return min(self._num_inserted, self._capacity)"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1VaehXiOFpZ_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class ReplayBuffer(object):\n",
        "  \"\"\"A really simple replay (ring) buffer.\"\"\"\n",
        "  \n",
        "  def __init__(self, obs_shape, obs_dtype=np.float32, capacity=10000):\n",
        "    \"\"\"Replay Buffer Constructor.\"\"\"\n",
        "    \n",
        "    self._capacity = capacity\n",
        "    \n",
        "    # Containers for all our shit\n",
        "    self._states_tm1 = np.zeros(shape=(capacity,) + obs_shape,\n",
        "                                dtype=obs_dtype)  # [C, D]\n",
        "    self._states_t = np.zeros(shape=(capacity,) + obs_shape,\n",
        "                              dtype=obs_dtype)  # [C, D]\n",
        "    self._actions = np.zeros(shape=(capacity,), dtype=np.int32)  # [C]\n",
        "    self._rewards = np.zeros(shape=(capacity,), dtype=np.float32)  # [C]\n",
        "    self._dones = np.zeros(shape=(capacity,), dtype=np.float32)  # [C]\n",
        "                  \n",
        "    # Keep track of how much shit we've put in\n",
        "    self._num_inserted = 0\n",
        "    \n",
        "  def add(self, s_tm1, a_t, r_t, s_t, done_t):\n",
        "    \"\"\"Adds one transition to the replay buffer.\"\"\"\n",
        "\n",
        "    # Get a 'safe' index (i.e. ring buffer shit)\n",
        "    idx = self._num_inserted % self._capacity\n",
        "    \n",
        "    # Chuck a transition in\n",
        "    self._states_tm1[idx] = s_tm1\n",
        "    self._states_t[idx] = s_t\n",
        "    self._actions[idx] = a_t\n",
        "    self._rewards[idx] = r_t\n",
        "    self._dones[idx] = d_t\n",
        "    \n",
        "    # Dick dacks\n",
        "    self._num_inserted += 1\n",
        "    \n",
        "  def sample(self, batch_size):\n",
        "    \"\"\"Sample `batch_size` transitions at random from the buffer.\"\"\"\n",
        "    indices = np.random.randint(low=0, high=self.size(), size=batch_size)\n",
        "    \n",
        "    return (self._states_tm1[indices],\n",
        "            self._states_t[indices],\n",
        "            self._actions[indices],\n",
        "            self._rewards[indices],\n",
        "            self._dones[indices])\n",
        "    \n",
        "  def size(self):\n",
        "    \"\"\"Gets the current size of the replay buffer lol.\"\"\"\n",
        "    return min(self._num_inserted, self._capacity)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "z1TVDTEX6TW-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Run loop"
      ]
    },
    {
      "metadata": {
        "id": "YyMPhXDe9A5f",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Hyperparameters etc\n",
        "\n",
        "NUM_EPISODES = 2000\n",
        "HIDDEN_SIZES = [30]\n",
        "GAMMA = 0.99\n",
        "LEARNING_RATE = 1e-3\n",
        "EPSILON = 0.05\n",
        "BATCH_SIZE = 32"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Xr9hJ24zS8Rm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "5b9df349-32b4-4a3b-9691-f7071e5eef62"
      },
      "cell_type": "code",
      "source": [
        "# Environment shit\n",
        "env = gym.make('Pendulum-v0') # Use dir(env) to see all completions\n",
        "\n",
        "# Get observation/action specifications\n",
        "obs_shape = env.observation_space.shape  # [D]\n",
        "num_actions = int((env.action_space.high - env.action_space.low + 1)[0])  # []"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
            "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YxQ0HFCtbyac",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Building the TensorFlow graph\n"
      ]
    },
    {
      "metadata": {
        "id": "a-r0YoFd6Qwd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Building the TensorFlow graph\n",
        "\n",
        "# Shapes:\n",
        "#\n",
        "# D: Dimensions of observation space (i.e. \\mathbb{R}^D)\n",
        "# B: Batch dimension (B=1 at inference time)\n",
        "# A: Number of actions\n",
        "# \n",
        "\n",
        "tf.reset_default_graph()\n",
        "\n",
        "# Build Q-network model\n",
        "q_network = QNetwork(hidden_sizes=HIDDEN_SIZES, num_actions=num_actions) \n",
        "\n",
        "# Make placeholders for obs and reward (to put into TF graph)\n",
        "online_o_tm1 = tf.placeholder(shape=(1,) + obs_shape, dtype=tf.float32)  # [1, D]\n",
        "\n",
        "o_tm1 = tf.placeholder(shape=(BATCH_SIZE,) + obs_shape, dtype=tf.float32)  # [B, D]\n",
        "o_t = tf.placeholder(shape=(BATCH_SIZE,) + obs_shape, dtype=tf.float32)  # [B, D]\n",
        "a_t = tf.placeholder(shape=(BATCH_SIZE,), dtype=tf.int32)  # [B, 1]\n",
        "r_t = tf.placeholder(shape=(BATCH_SIZE,), dtype=tf.float32)  # [B, 1]\n",
        "done_t = tf.placeholder(shape=(BATCH_SIZE,), dtype=tf.float32)  # [B, 1]\n",
        "\n",
        "# Forward pass of the network for acting/inference/policy computation\n",
        "q_values = tf.squeeze(q_network(online_o_tm1))  # [A]\n",
        "\n",
        "# Forward (batched) pass of the network for training from replay transitions\n",
        "batch_q_values = q_network(o_tm1)  # [B, A]\n",
        "\n",
        "# Greedy policy\n",
        "a_ = tf.argmax(q_values)  # []\n",
        "\n",
        "# # Epsilon-greedy shit\n",
        "# random_var = tf.random_uniform(shape=(), dtype=tf.float32, minval=0., maxval=1.)\n",
        "# explore_action = lambda: tf.random.uniform(shape=(), dtype=tf.int32, minval=0, maxval=num_actions)\n",
        "# do_explore = tf.less(random_var, EPSILON)  # tf.bool, shape [] (scalar)\n",
        "# a_tm1 = tf.cond(do_explore, random_action, greedy_action)\n",
        "\n",
        "# Make a target Q-network\n",
        "target_q_network = QNetwork(hidden_sizes=HIDDEN_SIZES, num_actions=num_actions)\n",
        "\n",
        "# Training loss\n",
        "target_q_values = tf.squeeze(target_q_network(o_t))  # [A]\n",
        "\n",
        "# One-step Bootstrap target\n",
        "target = r_t + GAMMA * (1. - done_t) * tf.reduce_max(target_q_values, axis=1)\n",
        "\n",
        "q = tf.gather(batch_q_values, a_t, axis=1)\n",
        "\n",
        "# Square loss\n",
        "loss = tf.square(target - q)\n",
        "\n",
        "# Optimizer - standard learning rate is 1E-3\n",
        "optimizer = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE)\n",
        "\n",
        "# SGD step op\n",
        "step = optimizer.minimize(loss)\n",
        "\n",
        "online_variables = q_network.get_variables()\n",
        "target_variables = target_q_network.get_variables()\n",
        "\n",
        "# TensorFlow op to copy weights from the online network to the target network\n",
        "update_op = [tf.assign(v, w) \n",
        "             for v, w in zip(online_variables, target_variables)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "P4gcalgK6dRb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "fa43c9bc-aa3e-4901-8389-5ae3bcaec619"
      },
      "cell_type": "code",
      "source": [
        "replay = ReplayBuffer2()\n",
        "\n",
        "steps = 0\n",
        "results = []\n",
        "with tf.train.MonitoredSession() as sess:\n",
        "  for episode in range(NUM_EPISODES):\n",
        "    # Reset the environment to get the first initial state.\n",
        "    state = env.reset()\n",
        "    end_of_episode = False\n",
        "    total_return = 0\n",
        "    sess.run(update_op)\n",
        "\n",
        "    while not end_of_episode:\n",
        "      # \n",
        "      previous_state = state\n",
        "\n",
        "      # Epsilon-greedy policy\n",
        "      if np.random.uniform() < EPSILON:\n",
        "        action = np.random.randint(num_actions)\n",
        "      else:\n",
        "        action = sess.run(a_, feed_dict={online_o_tm1: np.expand_dims(state, 0)})\n",
        "\n",
        "      # Step the environment\n",
        "      state, reward, end_of_episode, _ = env.step([action])\n",
        "      steps += 1\n",
        "\n",
        "      # Add to replay\n",
        "      transition = [previous_state, action, reward, \n",
        "                    np.array(np.bool(end_of_episode)), state]\n",
        "      replay.add(transition)\n",
        "\n",
        "      # Match up the observations/rewards/etc with corresponding placeholders\n",
        "      if replay.ready_to_sample(BATCH_SIZE):\n",
        "        sample = replay.sample(BATCH_SIZE)\n",
        "        previous_states, actions, rewards, end_of_episodes, states = sample\n",
        "        feed = {a_t: actions,\n",
        "                done_t: np.float32(end_of_episodes), \n",
        "                o_tm1: previous_states, \n",
        "                o_t: states,\n",
        "                r_t: rewards}\n",
        "\n",
        "        # Run the learning/SGD step\n",
        "        sess.run(step, feed_dict=feed)\n",
        "      total_return += reward\n",
        "\n",
        "    # Logging\n",
        "    result = {'episode': episode, 'return': total_return, 'steps': steps}\n",
        "    results.append(result)\n",
        "    if episode % (NUM_EPISODES // 10) == 0:\n",
        "      print('Episode {episode}. Return: {return}. Total steps: {steps}'.format(**result))\n",
        "    \n",
        "    \n",
        "results = pd.DataFrame(results)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "Episode 0. Return: -1487.2132553395836. Total steps: 200\n",
            "Episode 200. Return: -1384.230525725266. Total steps: 40200\n",
            "Episode 400. Return: -1500.5849759433852. Total steps: 80200\n",
            "Episode 600. Return: -1353.9417136180348. Total steps: 120200\n",
            "Episode 800. Return: -1375.4650976209036. Total steps: 160200\n",
            "Episode 1000. Return: -1322.4011948872328. Total steps: 200200\n",
            "Episode 1200. Return: -1303.3621742879307. Total steps: 240200\n",
            "Episode 1400. Return: -1135.8518312490262. Total steps: 280200\n",
            "Episode 1600. Return: -1513.2327037654265. Total steps: 320200\n",
            "Episode 1800. Return: -1355.13352338096. Total steps: 360200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "II1l3MUjBCw1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "a1408761-1381-4695-9877-f41ccb1741e2"
      },
      "cell_type": "code",
      "source": [
        "p = (gg.ggplot(results)\n",
        "     + gg.aes('steps', 'return')\n",
        "     + gg.geom_point()\n",
        "    )\n",
        "p"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mc3NzlR4ahmFoZwUtlZseJqKdYcOGITw8nK+8m9+1WVgvWmZmJvz9/fm6VePG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\nQgghhBAN/h+ORhi/FMB/+wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f960e6c1080>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363269200777016)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    }
  ]
}