Reinforce_inside_the_box.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fauskanger/public/blob/master/Reinforce_inside_the_box.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "klGNgWREsvQv"
      },
      "source": [
        "##### Copyright 2021 The TF-Agents Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "id": "nQnmcm0oI1Q-"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HNtBC6Bbb1YU"
      },
      "source": [
        "# REINFORCE agent\n",
        "\n",
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://www.tensorflow.org/agents/tutorials/6_reinforce_tutorial\">\n",
        "    <img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />\n",
        "    View on TensorFlow.org</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/agents/blob/master/docs/tutorials/6_reinforce_tutorial.ipynb\">\n",
        "    <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />\n",
        "    Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/agents/blob/master/docs/tutorials/6_reinforce_tutorial.ipynb\">\n",
        "    <img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />\n",
        "    View source on GitHub</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a href=\"https://storage.googleapis.com/tensorflow_docs/agents/docs/tutorials/6_reinforce_tutorial.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZOUOQOrFs3zn"
      },
      "source": [
        "## Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cKOCZlhUgXVK"
      },
      "source": [
        "This example shows how to train a [REINFORCE](https://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf) agent on the Cartpole environment using the TF-Agents library, similar to the [DQN tutorial](1_dqn_tutorial.ipynb).\n",
        "\n",
        "![Cartpole environment](https://github.com/tensorflow/agents/blob/master/docs/tutorials/images/cartpole.png?raw=1)\n",
        "\n",
        "We will walk you through all the components in a Reinforcement Learning (RL) pipeline for training, evaluation and data collection.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1u9QVVsShC9X"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I5PNmEzIb9t4"
      },
      "source": [
        "If you haven't installed the following dependencies, run:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "KEHR2Ui-lo8O",
        "outputId": "405cbc11-e30f-4cc4-d769-22f488f8ce2b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: tf-estimator-nightly, tf-agents, dm-reverb\n",
            "Successfully installed dm-reverb-0.7.0 tf-agents-0.12.0 tf-estimator-nightly-2.8.0.dev2021122109\n",
            "Requirement already satisfied: pyglet in /usr/local/lib/python3.7/dist-packages (1.5.0)\n",
            "Collecting xvfbwrapper\n",
            "  Downloading xvfbwrapper-0.2.9.tar.gz (5.6 kB)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from pyglet) (0.16.0)\n",
            "Building wheels for collected packages: xvfbwrapper\n",
            "  Building wheel for xvfbwrapper (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for xvfbwrapper: filename=xvfbwrapper-0.2.9-py3-none-any.whl size=5023 sha256=cb8d667bfb99f8195d5d466e4eb79b47bf0a5db25fb1126ea7b8ad101bc7b743\n",
            "  Stored in directory: /root/.cache/pip/wheels/f4/a7/4b/c559bd2437620de067a11d4ab79222445bfe3c4f2130882f76\n",
            "Successfully built xvfbwrapper\n",
            "Installing collected packages: xvfbwrapper\n",
            "Successfully installed xvfbwrapper-0.2.9\n"
          ]
        }
      ],
      "source": [
        "!sudo apt-get update\n",
        "!sudo apt-get install -y xvfb ffmpeg freeglut3-dev\n",
        "!pip install 'imageio==2.4.0'\n",
        "!pip install pyvirtualdisplay\n",
        "!pip install tf-agents[reverb]\n",
        "!pip install pyglet xvfbwrapper\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "sMitx5qSgJk1"
      },
      "outputs": [],
      "source": [
        "from __future__ import absolute_import\n",
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "\n",
        "import base64\n",
        "import imageio\n",
        "import IPython\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import PIL.Image\n",
        "import pyvirtualdisplay\n",
        "import reverb\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "from tf_agents.agents.reinforce import reinforce_agent\n",
        "from tf_agents.drivers import py_driver\n",
        "from tf_agents.environments import suite_gym\n",
        "from tf_agents.environments import tf_py_environment\n",
        "from tf_agents.networks import actor_distribution_network\n",
        "from tf_agents.policies import py_tf_eager_policy\n",
        "from tf_agents.replay_buffers import reverb_replay_buffer\n",
        "from tf_agents.replay_buffers import reverb_utils\n",
        "from tf_agents.specs import tensor_spec\n",
        "from tf_agents.trajectories import trajectory\n",
        "from tf_agents.utils import common\n",
        "\n",
        "# Set up a virtual display for rendering OpenAI gym environments.\n",
        "display = pyvirtualdisplay.Display(visible=0, size=(1400, 900)).start()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import abc\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "from tf_agents.environments import py_environment\n",
        "from tf_agents.environments import tf_environment\n",
        "from tf_agents.environments import tf_py_environment\n",
        "from tf_agents.environments import utils\n",
        "from tf_agents.specs import array_spec\n",
        "from tf_agents.environments import wrappers\n",
        "from tf_agents.environments import suite_gym\n",
        "from tf_agents.trajectories import time_step as ts"
      ],
      "metadata": {
        "id": "tPZcn42E4FL0"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LmC0NDhdLIKY"
      },
      "source": [
        "## Hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "HC1kNrOsLSIZ"
      },
      "outputs": [],
      "source": [
        "env_name = \"CartPole-v0\" # @param {type:\"string\"}\n",
        "collect_episodes_per_iteration = 2 # @param {type:\"integer\"}\n",
        "replay_buffer_capacity = 2000 # @param {type:\"integer\"}\n",
        "\n",
        "fc_layer_params = (100,)\n",
        "\n",
        "learning_rate = 1e-3 # @param {type:\"number\"}\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VMsJC3DEgI0x"
      },
      "source": [
        "## Environment\n",
        "\n",
        "Environments in RL represent the task or problem that we are trying to solve. Standard environments can be easily created in TF-Agents using `suites`. We have different `suites` for loading environments from sources such as the OpenAI Gym, Atari, DM Control, etc., given a string environment name.\n",
        "\n",
        "Now let us load the CartPole environment from the OpenAI Gym suite."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "t3VsAaGsUNLg"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "board_size = 5\n",
        "max_step_num = 50\n",
        "\n",
        "decoded_action = {0: (0, -1), 1: (1, 0), 2: (0, 1), 3: (-1, 0)}\n",
        "\n",
        "def get_new_state():\n",
        "  state = [np.random.randint(0, board_size) for _ in range(4)]\n",
        "  x1, y1, x2, y2 = state\n",
        "  while x1 == x2 and y1 == y2:\n",
        "    state = [np.random.randint(0, board_size) for _ in range(4)]\n",
        "    x1, y1, x2, y2 = state\n",
        "  return state\n",
        "\n",
        "# Actions: 0-3 (move 1 step up, right, down, or left)\n",
        "# State: (position_ai_x, position_ai_y, position_loot_x, position_loot_y)\n",
        "\n",
        "class InsideBoxEnv(py_environment.PyEnvironment):\n",
        "\n",
        "  def __init__(self):\n",
        "    self._action_spec = array_spec.BoundedArraySpec(\n",
        "        shape=(), dtype=np.int32, minimum=0, maximum=3, name='action')\n",
        "    self._observation_spec = array_spec.BoundedArraySpec(\n",
        "        shape=(4,), dtype=np.int32, \n",
        "        minimum=0,\n",
        "        maximum=board_size,\n",
        "        name='observation')\n",
        "    self._reset_context()\n",
        "\n",
        "  def _reset_context(self):\n",
        "    self._state = get_new_state()\n",
        "    self._episode_ended = False\n",
        "    self._step_num = 0\n",
        "    self._is_on_loot = False\n",
        "\n",
        "  def _update_context(self, dx, dy):\n",
        "    #print(\"old state: \", self._state)\n",
        "    self._is_on_loot = False\n",
        "    x, y, loot_x, loot_y = self._state\n",
        "    new_x, new_y = (x+dx) % board_size, (y+dy) % board_size\n",
        "    old_distance = np.abs(new_x - loot_x) - np.abs(new_y - loot_y)\n",
        "    new_distance = np.abs(new_x - loot_x) - np.abs(new_y - loot_y)\n",
        "    reward = board_size * 2 - new_distance if old_distance > new_distance else 0\n",
        "    if (new_x == loot_x and new_y == loot_y):\n",
        "      self._is_on_loot = True\n",
        "      reward = 2000\n",
        "      loot_x, loot_y = [np.random.randint(0, board_size) for _ in range(2)]\n",
        "    self._step_num += 1\n",
        "    self._state = new_x, new_y, loot_x, loot_y\n",
        "    #print(\"new state: \", self._state)\n",
        "    return reward\n",
        "\n",
        "  def action_spec(self):\n",
        "    return self._action_spec\n",
        "\n",
        "  def observation_spec(self):\n",
        "    return self._observation_spec\n",
        "\n",
        "  def _reset(self):\n",
        "    # print('state reset', self._state)\n",
        "    self._reset_context()\n",
        "    return ts.restart(np.array(self._state, dtype=np.int32))\n",
        "\n",
        "  def moves_outside(self, dx, dy):\n",
        "    x, y = self._state[:2]\n",
        "    result = 0 <= (x + dx) < board_size and 0 <= (y + dy) < board_size\n",
        "    #print(f\"moves outside?. Old: {self._state}, dxyd: ({dx},{dy}), inside: {result}\")\n",
        "    return not result\n",
        "\n",
        "  def _step(self, action):\n",
        "    if self._episode_ended:\n",
        "      # The last action ended the episode. Ignore the current action and start\n",
        "      # a new episode.\n",
        "      return self.reset()\n",
        "    \n",
        "    #print(f\"steppppp... action: \", action)\n",
        "    dx, dy = decoded_action[action.item()]\n",
        "    #print(f\"steppppp2... dxdy: ({dx},{dy})\")\n",
        "    if self.moves_outside(dx, dy):\n",
        "      self._episode_ended = True\n",
        "      return ts.termination(np.array(self._state, dtype=np.int32), 0)\n",
        "\n",
        "    reward = self._update_context(dx, dy)\n",
        "\n",
        "    # Make sure episodes don't go on forever.\n",
        "    if self._step_num < max_step_num:\n",
        "      return ts.transition(\n",
        "          np.array(self._state, dtype=np.int32), reward=reward, discount=1.0)\n",
        "    else:\n",
        "      # print('termination')\n",
        "      self._episode_ended = True\n",
        "      return ts.termination(np.array(self._state, dtype=np.int32), reward)\n",
        "\n",
        "  def render(self, mode = 'rgb_array'):\n",
        "    \n",
        "    grid_dim = board_size\n",
        "    grid = np.zeros((grid_dim, grid_dim, 3), dtype=np.uint8)\n",
        "\n",
        "    x, y, loot_x, loot_y = self._state\n",
        "\n",
        "    grid[:, 1:board_size:2, 0] = 30\n",
        "    grid[0:board_size:2, :, 2] = 120\n",
        "    if (self._is_on_loot):\n",
        "        grid[:, :, :2] = grid[:, :, :2] + 40\n",
        "    \n",
        "\n",
        "    #print('ai xy', x, y)\n",
        "    #print('loot xy', loot_x, loot_y)\n",
        "    grid[x, y, :] = 255\n",
        "    grid[loot_x, loot_y, 1] = 255\n",
        "    return grid"
      ],
      "metadata": {
        "id": "Q1rYE-UggCo-"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Validate env\n",
        "env = InsideBoxEnv()\n",
        "utils.validate_py_environment(env, episodes=5)"
      ],
      "metadata": {
        "id": "5rV-3mwX4adH"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IIHYVBkuvPNw"
      },
      "source": [
        "We can render this environment to see how it looks. A free-swinging pole is attached to a cart.  The goal is to move the cart right or left in order to keep the pole pointing up."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "RlO7WIQHu_7D",
        "outputId": "5a558848-2e10-4d4b-c4ee-0de3cb24d957",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=300x300 at 0x7F3B11FD6410>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "env.reset()\n",
        "PIL.Image.fromarray(env.render()).resize((300, 300), PIL.Image.NEAREST)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B9_lskPOey18"
      },
      "source": [
        "The `time_step = environment.step(action)` statement takes `action` in the environment.  The `TimeStep` tuple returned contains the environment's next observation and reward for that action. The `time_step_spec()` and `action_spec()` methods in the environment return the specifications (types, shapes, bounds) of the `time_step` and `action` respectively."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "exDv57iHfwQV",
        "outputId": "25b90e50-d7cc-4087-ac3a-a2461bfab203",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Observation Spec:\n",
            "BoundedArraySpec(shape=(4,), dtype=dtype('int32'), name='observation', minimum=0, maximum=5)\n",
            "Action Spec:\n",
            "BoundedArraySpec(shape=(), dtype=dtype('int32'), name='action', minimum=0, maximum=3)\n"
          ]
        }
      ],
      "source": [
        "print('Observation Spec:')\n",
        "print(env.time_step_spec().observation)\n",
        "print('Action Spec:')\n",
        "print(env.action_spec())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eJCgJnx3g0yY"
      },
      "source": [
        "So, we see that observation is an array of 4 floats: the position and velocity of the cart, and the angular position and velocity of the pole. Since only two actions are possible (move left or move right), the `action_spec` is a scalar where 0 means \"move left\" and 1 means \"move right.\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "V2UGR5t_iZX-",
        "outputId": "2510dc6d-5d29-4262-d21a-8ff26753fa07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time step:\n",
            "TimeStep(\n",
            "{'discount': array(1., dtype=float32),\n",
            " 'observation': array([2, 1, 3, 0], dtype=int32),\n",
            " 'reward': array(0., dtype=float32),\n",
            " 'step_type': array(0, dtype=int32)})\n",
            "Next time step:\n",
            "TimeStep(\n",
            "{'discount': array(1., dtype=float32),\n",
            " 'observation': array([1, 1, 3, 0], dtype=int32),\n",
            " 'reward': array(0., dtype=float32),\n",
            " 'step_type': array(1, dtype=int32)})\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=300x300 at 0x7F3B11F4EF50>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "time_step = env.reset()\n",
        "print('Time step:')\n",
        "print(time_step)\n",
        "\n",
        "action = np.array([3], dtype=np.uint8)\n",
        "\n",
        "next_time_step = env.step(action)\n",
        "print('Next time step:')\n",
        "print(next_time_step)\n",
        "\n",
        "\n",
        "PIL.Image.fromarray(env.render()).resize((300, 300), PIL.Image.NEAREST)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zuUqXAVmecTU"
      },
      "source": [
        "Usually we create two environments: one for training and one for evaluation. Most environments are written in pure python, but they can be easily converted to TensorFlow using the `TFPyEnvironment` wrapper. The original environment's API uses numpy arrays, the `TFPyEnvironment` converts these to/from `Tensors` for you to more easily interact with TensorFlow policies and agents.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "Xp-Y4mD6eDhF"
      },
      "outputs": [],
      "source": [
        "train_py_env = InsideBoxEnv()  # suite_gym.load(env_name)\n",
        "eval_py_env = InsideBoxEnv() # suite_gym.load(env_name)\n",
        "\n",
        "train_env = tf_py_environment.TFPyEnvironment(train_py_env)\n",
        "eval_env = tf_py_environment.TFPyEnvironment(eval_py_env)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E9lW_OZYFR8A"
      },
      "source": [
        "## Agent\n",
        "\n",
        "The algorithm that we use to solve an RL problem is represented as an `Agent`. In addition to the REINFORCE agent, TF-Agents provides standard implementations of a variety of `Agents` such as [DQN](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf), [DDPG](https://arxiv.org/pdf/1509.02971.pdf), [TD3](https://arxiv.org/pdf/1802.09477.pdf), [PPO](https://arxiv.org/abs/1707.06347) and [SAC](https://arxiv.org/abs/1801.01290).\n",
        "\n",
        "To create a REINFORCE Agent, we first need an `Actor Network` that can learn to predict the action given an observation from the environment.\n",
        "\n",
        "We can easily create an `Actor Network` using the specs of the observations and actions. We can specify the layers in the network which, in this example, is the `fc_layer_params` argument set to a tuple of `ints` representing the sizes of each hidden layer (see the Hyperparameters section above).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "TgkdEPg_muzV"
      },
      "outputs": [],
      "source": [
        "actor_net = actor_distribution_network.ActorDistributionNetwork(\n",
        "    train_env.observation_spec(),\n",
        "    train_env.action_spec(),\n",
        "    fc_layer_params=fc_layer_params)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z62u55hSmviJ"
      },
      "source": [
        "We also need an `optimizer` to train the network we just created, and a `train_step_counter` variable to keep track of how many times the network was updated.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "jbY4yrjTEyc9"
      },
      "outputs": [],
      "source": [
        "optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)\n",
        "\n",
        "train_step_counter = tf.Variable(0)\n",
        "\n",
        "tf_agent = reinforce_agent.ReinforceAgent(\n",
        "    train_env.time_step_spec(),\n",
        "    train_env.action_spec(),\n",
        "    actor_network=actor_net,\n",
        "    optimizer=optimizer,\n",
        "    normalize_returns=True,\n",
        "    train_step_counter=train_step_counter)\n",
        "tf_agent.initialize()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I0KLrEPwkn5x"
      },
      "source": [
        "## Policies\n",
        "\n",
        "In TF-Agents, policies represent the standard notion of policies in RL: given a `time_step` produce an action or a distribution over actions. The main method is `policy_step = policy.action(time_step)` where `policy_step` is a named tuple `PolicyStep(action, state, info)`.  The `policy_step.action` is the `action` to be applied to the environment, `state` represents the state for stateful (RNN) policies and `info` may contain auxiliary information such as log probabilities of the actions.\n",
        "\n",
        "Agents contain two policies: the main policy that is used for evaluation/deployment (agent.policy) and another policy that is used for data collection (agent.collect_policy)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "BwY7StuMkuV4"
      },
      "outputs": [],
      "source": [
        "eval_policy = tf_agent.policy\n",
        "collect_policy = tf_agent.collect_policy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "94rCXQtbUbXv"
      },
      "source": [
        "## Metrics and Evaluation\n",
        "\n",
        "The most common metric used to evaluate a policy is the average return. The return is the sum of rewards obtained while running a policy in an environment for an episode, and we usually average this over a few episodes. We can compute the average return metric as follows.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "bitzHo5_UbXy"
      },
      "outputs": [],
      "source": [
        "def compute_avg_return(environment, policy, num_episodes=10):\n",
        "\n",
        "  total_return = 0.0\n",
        "  for _ in range(num_episodes):\n",
        "\n",
        "    time_step = environment.reset()\n",
        "    episode_return = 0.0\n",
        "\n",
        "    while not time_step.is_last():\n",
        "      action_step = policy.action(time_step)\n",
        "      time_step = environment.step(action_step.action)\n",
        "      episode_return += time_step.reward\n",
        "    total_return += episode_return\n",
        "\n",
        "  avg_return = total_return / num_episodes\n",
        "  return avg_return.numpy()[0]\n",
        "\n",
        "\n",
        "# Please also see the metrics module for standard implementations of different\n",
        "# metrics."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NLva6g2jdWgr"
      },
      "source": [
        "## Replay Buffer\n",
        "\n",
        "In order to keep track of the data collected from the environment, we will use [Reverb](https://deepmind.com/research/open-source/Reverb), an efficient, extensible, and easy-to-use replay system by Deepmind. It stores experience data when we collect trajectories and is consumed during training.\n",
        "\n",
        "This replay buffer is constructed using specs describing the tensors that are to be stored, which can be obtained from the agent using `tf_agent.collect_data_spec`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "vX2zGUWJGWAl"
      },
      "outputs": [],
      "source": [
        "table_name = 'uniform_table'\n",
        "replay_buffer_signature = tensor_spec.from_spec(\n",
        "      tf_agent.collect_data_spec)\n",
        "replay_buffer_signature = tensor_spec.add_outer_dim(\n",
        "      replay_buffer_signature)\n",
        "table = reverb.Table(\n",
        "    table_name,\n",
        "    max_size=replay_buffer_capacity,\n",
        "    sampler=reverb.selectors.Uniform(),\n",
        "    remover=reverb.selectors.Fifo(),\n",
        "    rate_limiter=reverb.rate_limiters.MinSize(1),\n",
        "    signature=replay_buffer_signature)\n",
        "\n",
        "reverb_server = reverb.Server([table])\n",
        "\n",
        "replay_buffer = reverb_replay_buffer.ReverbReplayBuffer(\n",
        "    tf_agent.collect_data_spec,\n",
        "    table_name=table_name,\n",
        "    sequence_length=None,\n",
        "    local_server=reverb_server)\n",
        "\n",
        "rb_observer = reverb_utils.ReverbAddEpisodeObserver(\n",
        "    replay_buffer.py_client,\n",
        "    table_name,\n",
        "    replay_buffer_capacity\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZGNTDJpZs4NN"
      },
      "source": [
        "For most agents, the `collect_data_spec` is a `Trajectory` named tuple containing the observation, action, reward etc."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rVD5nQ9ZGo8_"
      },
      "source": [
        "## Data Collection\n",
        "\n",
        "As REINFORCE learns from whole episodes, we define a function to collect an episode using the given data collection policy and save the data (observations, actions, rewards etc.) as trajectories in the replay buffer. Here we are using 'PyDriver' to run the experience collecting loop. You can learn more about TF Agents driver in our [drivers tutorial](https://www.tensorflow.org/agents/tutorials/4_drivers_tutorial)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "wr1KSAEGG4h9"
      },
      "outputs": [],
      "source": [
        "def collect_episode(environment, policy, num_episodes):\n",
        "\n",
        "  driver = py_driver.PyDriver(\n",
        "    environment,\n",
        "    py_tf_eager_policy.PyTFEagerPolicy(\n",
        "      policy, use_tf_function=True),\n",
        "    [rb_observer],\n",
        "    max_episodes=num_episodes)\n",
        "  initial_time_step = environment.reset()\n",
        "  driver.run(initial_time_step)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hBc9lj9VWWtZ"
      },
      "source": [
        "## Training the agent\n",
        "\n",
        "The training loop involves both collecting data from the environment and optimizing the agent's networks. Along the way, we will occasionally evaluate the agent's policy to see how we are doing.\n",
        "\n",
        "The following will take ~3 minutes to run."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "0pTbJ3PeyF-u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2393b427-0923-42da-df8a-db74fba53a5e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 7 µs, sys: 1 µs, total: 8 µs\n",
            "Wall time: 16.7 µs\n",
            "step = 25: loss = 0.0\n",
            "step = 50: loss = 0.0\n",
            "step = 50: Average Return = 0.0\n",
            "step = 75: loss = 0.0\n",
            "step = 100: loss = 0.0\n",
            "step = 100: Average Return = 200.0\n",
            "step = 125: loss = 0.0\n",
            "step = 150: loss = -0.9700860977172852\n",
            "step = 150: Average Return = 200.0\n",
            "step = 175: loss = 0.0\n",
            "step = 200: loss = 2.1831867694854736\n",
            "step = 200: Average Return = 200.0\n",
            "step = 225: loss = 0.0\n",
            "step = 250: loss = 0.0\n",
            "step = 250: Average Return = 600.0\n",
            "step = 275: loss = 0.0\n",
            "step = 300: loss = 0.0\n",
            "step = 300: Average Return = 200.0\n",
            "step = 325: loss = 1.1487846374511719\n",
            "step = 350: loss = 4.944547653198242\n",
            "step = 350: Average Return = 600.0\n",
            "step = 375: loss = -0.038188934326171875\n",
            "step = 400: loss = 0.0\n",
            "step = 400: Average Return = 3800.0\n",
            "step = 425: loss = 0.0\n",
            "step = 450: loss = 1.4439163208007812\n",
            "step = 450: Average Return = 0.0\n",
            "step = 475: loss = -0.0004119873046875\n",
            "step = 500: loss = 0.0\n",
            "step = 500: Average Return = 600.0\n",
            "step = 525: loss = -2.9885196685791016\n",
            "step = 550: loss = 4.079931259155273\n",
            "step = 550: Average Return = 200.0\n",
            "step = 575: loss = -0.09344100952148438\n",
            "step = 600: loss = 7.823944091796875\n",
            "step = 600: Average Return = 1000.0\n",
            "step = 625: loss = 0.0\n",
            "step = 650: loss = 6.591678619384766\n",
            "step = 650: Average Return = 200.0\n",
            "step = 675: loss = 4.0686354637146\n",
            "step = 700: loss = 0.0\n",
            "step = 700: Average Return = 200.0\n",
            "step = 725: loss = 3.1507911682128906\n",
            "step = 750: loss = 0.0\n",
            "step = 750: Average Return = 5400.0\n",
            "step = 775: loss = 6.805858612060547\n",
            "step = 800: loss = 0.0\n",
            "step = 800: Average Return = 4600.0\n",
            "step = 825: loss = -1.777318000793457\n",
            "step = 850: loss = -0.20945477485656738\n",
            "step = 850: Average Return = 7000.0\n",
            "step = 875: loss = -0.9947388172149658\n",
            "step = 900: loss = -3.092440605163574\n",
            "step = 900: Average Return = 14200.0\n",
            "step = 925: loss = 1.1446971893310547\n",
            "step = 950: loss = -1.0345182418823242\n",
            "step = 950: Average Return = 1800.0\n",
            "step = 975: loss = 4.832486152648926\n",
            "step = 1000: loss = -0.8098970651626587\n",
            "step = 1000: Average Return = 4600.0\n",
            "step = 1025: loss = 11.395318984985352\n",
            "step = 1050: loss = 6.430061340332031\n",
            "step = 1050: Average Return = 1600.0\n",
            "step = 1075: loss = 1.3655891418457031\n",
            "step = 1100: loss = 3.1598453521728516\n",
            "step = 1100: Average Return = 6200.0\n",
            "step = 1125: loss = 1.7689342498779297\n",
            "step = 1150: loss = 0.0\n",
            "step = 1150: Average Return = 11600.0\n",
            "step = 1175: loss = -0.7996759414672852\n",
            "step = 1200: loss = -0.5655450820922852\n",
            "step = 1200: Average Return = 9200.0\n",
            "step = 1225: loss = -5.803144454956055\n",
            "step = 1250: loss = -0.27216148376464844\n",
            "step = 1250: Average Return = 10800.0\n",
            "step = 1275: loss = -4.892138481140137\n",
            "step = 1300: loss = 8.033834457397461\n",
            "step = 1300: Average Return = 8400.0\n",
            "step = 1325: loss = -3.284038543701172\n",
            "step = 1350: loss = -0.5077791213989258\n",
            "step = 1350: Average Return = 6800.0\n",
            "step = 1375: loss = 6.094365119934082\n",
            "step = 1400: loss = -5.671710968017578\n",
            "step = 1400: Average Return = 9800.0\n",
            "step = 1425: loss = -2.714231014251709\n",
            "step = 1450: loss = 0.0\n",
            "step = 1450: Average Return = 5000.0\n",
            "step = 1475: loss = -2.383452892303467\n",
            "step = 1500: loss = 2.05908203125\n",
            "step = 1500: Average Return = 5200.0\n",
            "step = 1525: loss = 0.7647056579589844\n",
            "step = 1550: loss = -0.41246509552001953\n",
            "step = 1550: Average Return = 11000.0\n",
            "step = 1575: loss = 3.085176467895508\n",
            "step = 1600: loss = 4.883401870727539\n",
            "step = 1600: Average Return = 7200.0\n",
            "step = 1625: loss = -2.617751121520996\n",
            "step = 1650: loss = -7.547527313232422\n",
            "step = 1650: Average Return = 6000.0\n",
            "step = 1675: loss = -3.7062644958496094\n",
            "step = 1700: loss = -2.7923402786254883\n",
            "step = 1700: Average Return = 10400.0\n",
            "step = 1725: loss = -1.4627571105957031\n",
            "step = 1750: loss = 0.5844974517822266\n",
            "step = 1750: Average Return = 15800.0\n",
            "step = 1775: loss = 5.132513046264648\n",
            "step = 1800: loss = 4.801398277282715\n",
            "step = 1800: Average Return = 23200.0\n",
            "step = 1825: loss = -2.824784278869629\n",
            "step = 1850: loss = 4.119278907775879\n",
            "step = 1850: Average Return = 9800.0\n",
            "step = 1875: loss = 0.11080360412597656\n",
            "step = 1900: loss = -0.8456850051879883\n",
            "step = 1900: Average Return = 5800.0\n",
            "step = 1925: loss = 2.7565460205078125\n",
            "step = 1950: loss = 2.829313278198242\n",
            "step = 1950: Average Return = 11600.0\n",
            "step = 1975: loss = 4.899655342102051\n",
            "step = 2000: loss = 0.3248629570007324\n",
            "step = 2000: Average Return = 3200.0\n",
            "step = 2025: loss = -6.143377304077148\n",
            "step = 2050: loss = -5.177906036376953\n",
            "step = 2050: Average Return = 31400.0\n",
            "step = 2075: loss = 7.1222686767578125\n",
            "step = 2100: loss = -1.75396728515625\n",
            "step = 2100: Average Return = 24600.0\n",
            "step = 2125: loss = -5.283578872680664\n",
            "step = 2150: loss = 4.054875373840332\n",
            "step = 2150: Average Return = 28600.0\n",
            "step = 2175: loss = 1.0503604412078857\n",
            "step = 2200: loss = 2.1421432495117188\n",
            "step = 2200: Average Return = 25200.0\n",
            "step = 2225: loss = 11.55757999420166\n",
            "step = 2250: loss = 0.0\n",
            "step = 2250: Average Return = 12600.0\n",
            "step = 2275: loss = 0.36244773864746094\n",
            "step = 2300: loss = -6.642606735229492\n",
            "step = 2300: Average Return = 30200.0\n",
            "step = 2325: loss = 6.337614059448242\n",
            "step = 2350: loss = 5.062723159790039\n",
            "step = 2350: Average Return = 8800.0\n",
            "step = 2375: loss = 8.756579399108887\n",
            "step = 2400: loss = -5.754019260406494\n",
            "step = 2400: Average Return = 16200.0\n",
            "step = 2425: loss = -6.352694511413574\n",
            "step = 2450: loss = 2.4027700424194336\n",
            "step = 2450: Average Return = 28800.0\n",
            "step = 2475: loss = 5.90463399887085\n",
            "step = 2500: loss = -1.4410314559936523\n",
            "step = 2500: Average Return = 26600.0\n"
          ]
        }
      ],
      "source": [
        "num_iterations = 2500 # @param {type:\"integer\"}\n",
        "log_interval = 25 # @param {type:\"integer\"}\n",
        "num_eval_episodes = 10 # @param {type:\"integer\"}\n",
        "eval_interval = 50 # @param {type:\"integer\"}\n",
        "\n",
        "try:\n",
        "  %%time\n",
        "except:\n",
        "  pass\n",
        "\n",
        "# (Optional) Optimize by wrapping some of the code in a graph using TF function.\n",
        "tf_agent.train = common.function(tf_agent.train)\n",
        "\n",
        "# Reset the train step\n",
        "tf_agent.train_step_counter.assign(0)\n",
        "\n",
        "# Evaluate the agent's policy once before training.\n",
        "avg_return = compute_avg_return(eval_env, tf_agent.policy, num_eval_episodes)\n",
        "returns = [avg_return]\n",
        "\n",
        "for _ in range(num_iterations):\n",
        "\n",
        "  # Collect a few episodes using collect_policy and save to the replay buffer.\n",
        "  collect_episode(\n",
        "      train_py_env, tf_agent.collect_policy, collect_episodes_per_iteration)\n",
        "\n",
        "  # Use data from the buffer and update the agent's network.\n",
        "  iterator = iter(replay_buffer.as_dataset(sample_batch_size=1))\n",
        "  trajectories, _ = next(iterator)\n",
        "  train_loss = tf_agent.train(experience=trajectories)  \n",
        "\n",
        "  replay_buffer.clear()\n",
        "\n",
        "  step = tf_agent.train_step_counter.numpy()\n",
        "\n",
        "  if step % log_interval == 0:\n",
        "    print('step = {0}: loss = {1}'.format(step, train_loss.loss))\n",
        "\n",
        "  if step % eval_interval == 0:\n",
        "    avg_return = compute_avg_return(eval_env, tf_agent.policy, num_eval_episodes)\n",
        "    print('step = {0}: Average Return = {1}'.format(step, avg_return))\n",
        "    returns.append(avg_return)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "68jNcA_TiJDq"
      },
      "source": [
        "## Visualization\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aO-LWCdbbOIC"
      },
      "source": [
        "### Plots\n",
        "\n",
        "We can plot return vs global steps to see the performance of our agent. In `Cartpole-v0`, the environment gives a reward of +1 for every time step the pole stays up, and since the maximum number of steps is 200, the maximum possible return is also 200."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "NxtL1mbOYCVO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "6cbe9895-5a3e-4adf-e7e2-073ceef96b2d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-1570.0, 36110.0)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "steps = range(0, num_iterations + 1, eval_interval)\n",
        "plt.plot(steps, returns)\n",
        "plt.ylabel('Average Return')\n",
        "plt.xlabel('Step')\n",
        "plt.ylim(top=max(returns) * 1.15)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M7-XpPP99Cy7"
      },
      "source": [
        "### Videos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9pGfGxSH32gn"
      },
      "source": [
        "It is helpful to visualize the performance of an agent by rendering the environment at each step. Before we do that, let us first create a function to embed videos in this colab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "ULaGr8pvOKbl"
      },
      "outputs": [],
      "source": [
        "def embed_mp4(filename):\n",
        "  \"\"\"Embeds an mp4 file in the notebook.\"\"\"\n",
        "  video = open(filename,'rb').read()\n",
        "  b64 = base64.b64encode(video)\n",
        "  tag = '''\n",
        "  <video width=\"640\" height=\"480\" controls>\n",
        "    <source src=\"data:video/mp4;base64,{0}\" type=\"video/mp4\">\n",
        "  Your browser does not support the video tag.\n",
        "  </video>'''.format(b64.decode())\n",
        "\n",
        "  return IPython.display.HTML(tag)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9c_PH-pX4Pr5"
      },
      "source": [
        "The following code visualizes the agent's policy for a few episodes:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "owOVWB158NlF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 985
        },
        "outputId": "621ab0b0-3a71-456b-948b-4778f74e719b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <video width=\"640\" height=\"480\" controls>\n",
              "    <source src=\"data:video/mp4;base64,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type=\"video/mp4\">\n",
              "  Your browser does not support the video tag.\n",
              "  </video>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <video width=\"640\" height=\"480\" controls>\n",
              "    <source 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type=\"video/mp4\">\n",
              "  Your browser does not support the video tag.\n",
              "  </video>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "def get_resized_frame(data):\n",
        "  frame_resized_image = PIL.Image.fromarray(data).resize((320, 320), PIL.Image.NEAREST)\n",
        "  return np.array(frame_resized_image)\n",
        "                      \n",
        "\n",
        "num_episodes = 10\n",
        "video_filename = 'imageio.mp4'\n",
        "kargs = { 'macro_block_size': None }\n",
        "with imageio.get_writer(video_filename, fps=4) as video:\n",
        "  for _ in range(num_episodes):\n",
        "    time_step = eval_env.reset()\n",
        "    video.append_data(get_resized_frame(eval_py_env.render()))\n",
        "    while not time_step.is_last():\n",
        "      action_step = tf_agent.policy.action(time_step)\n",
        "      time_step = eval_env.step(action_step.action)\n",
        "      video.append_data(get_resized_frame(eval_py_env.render()))\n",
        "\n",
        "embed_mp4(video_filename)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "2w5ecyoBPSK-"
      },
      "execution_count": 21,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "Reinforce inside the box.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}