DoubleDQNsInOpenAIGym.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DoubleDQNsInOpenAIGym.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "HnA-Zf8JqsL4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c187732c-31ed-4343-f17a-1fe7b4c8b0cb"
      },
      "cell_type": "code",
      "source": [
        "import gym\n",
        "import random\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from collections import deque\n",
        "print(\"Gym:\", gym.__version__)\n",
        "print(\"Tensorflow:\", tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Gym: 0.10.11\n",
            "Tensorflow: 1.9.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YHkYC1tmqsMB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "69964460-737b-4f85-ef42-d9f17b420286"
      },
      "cell_type": "code",
      "source": [
        "env_name = \"CartPole-v0\"\n",
        "env = gym.make(env_name)\n",
        "print(\"Observation space:\", env.observation_space)\n",
        "print(\"Action space:\", env.action_space)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Observation space: Box(4,)\n",
            "Action space: Discrete(2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "79GCjcYYqsMG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class QNetwork():\n",
        "    def __init__(self, state_dim, action_size, tau=0.01):\n",
        "        tf.reset_default_graph()\n",
        "        self.state_in = tf.placeholder(tf.float32, shape=[None, *state_dim])\n",
        "        self.action_in = tf.placeholder(tf.int32, shape=[None])\n",
        "        self.q_target_in = tf.placeholder(tf.float32, shape=[None])\n",
        "        action_one_hot = tf.one_hot(self.action_in, depth=action_size)\n",
        "        \n",
        "        self.q_state_local = self.build_model(action_size, \"local\")\n",
        "        self.q_state_target = self.build_model(action_size, \"target\")\n",
        "        \n",
        "        self.q_state_action = tf.reduce_sum(tf.multiply(self.q_state_local, action_one_hot), axis=1)\n",
        "        self.loss = tf.reduce_mean(tf.square(self.q_state_action - self.q_target_in))\n",
        "        self.optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(self.loss)\n",
        "        \n",
        "        self.local_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=\"local\")\n",
        "        self.target_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=\"target\")\n",
        "        self.updater = tf.group([tf.assign(t, t + tau*(l-t)) for t,l in zip(self.target_vars, self.local_vars)])\n",
        "        \n",
        "    def build_model(self, action_size, scope):\n",
        "        with tf.variable_scope(scope):\n",
        "            hidden1 = tf.layers.dense(self.state_in, 100, activation=tf.nn.relu)\n",
        "            q_state = tf.layers.dense(hidden1, action_size, activation=None)\n",
        "            return q_state\n",
        "        \n",
        "    def update_model(self, session, state, action, q_target):\n",
        "        feed = {self.state_in: state, self.action_in: action, self.q_target_in: q_target}\n",
        "        session.run([self.optimizer, self.updater], feed_dict=feed)\n",
        "        \n",
        "    def get_q_state(self, session, state, use_target=False):\n",
        "        q_state_op = self.q_state_target if use_target else self.q_state_local\n",
        "        q_state = session.run(q_state_op, feed_dict={self.state_in: state})\n",
        "        return q_state"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EYKcRk3ZqsMJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class ReplayBuffer():\n",
        "    def __init__(self, maxlen):\n",
        "        self.buffer = deque(maxlen=maxlen)\n",
        "        \n",
        "    def add(self, experience):\n",
        "        self.buffer.append(experience)\n",
        "        \n",
        "    def sample(self, batch_size):\n",
        "        sample_size = min(len(self.buffer), batch_size)\n",
        "        samples = random.choices(self.buffer, k=sample_size)\n",
        "        return map(list, zip(*samples))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XKIXlcQSqsMN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class DoubleDQNAgent():\n",
        "    def __init__(self, env):\n",
        "        self.state_dim = env.observation_space.shape\n",
        "        self.action_size = env.action_space.n\n",
        "        self.q_network = QNetwork(self.state_dim, self.action_size)\n",
        "        self.replay_buffer = ReplayBuffer(maxlen=10000)\n",
        "        self.gamma = 0.97\n",
        "        self.eps = 1.0\n",
        "        \n",
        "        self.sess = tf.Session()\n",
        "        self.sess.run(tf.global_variables_initializer())\n",
        "        \n",
        "    def get_action(self, state):\n",
        "        q_state = self.q_network.get_q_state(self.sess, [state])\n",
        "        action_greedy = np.argmax(q_state)\n",
        "        action_random = np.random.randint(self.action_size)\n",
        "        action = action_random if random.random() < self.eps else action_greedy\n",
        "        return action\n",
        "    \n",
        "    def train(self, state, action, next_state, reward, done, use_DDQN=True):\n",
        "        self.replay_buffer.add((state, action, next_state, reward, done))\n",
        "        states, actions, next_states, rewards, dones = self.replay_buffer.sample(50)\n",
        "        \n",
        "        next_actions = np.argmax(self.q_network.get_q_state(self.sess, next_states, use_target=False), axis=1)\n",
        "        q_next_states = self.q_network.get_q_state(self.sess, next_states, use_target=use_DDQN)\n",
        "        q_next_states[dones] = np.zeros([self.action_size])\n",
        "        q_next_states_next_actions = q_next_states[np.arange(next_actions.shape[0]), next_actions]\n",
        "        q_targets = rewards + self.gamma * q_next_states_next_actions\n",
        "        self.q_network.update_model(self.sess, states, actions, q_targets)\n",
        "        \n",
        "        if done: self.eps = max(0.1, 0.99*self.eps)\n",
        "    \n",
        "    def __del__(self):\n",
        "        self.sess.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zv9Amjj4qsMQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "51824ef8-efd7-419d-8641-f6a6e9f484fb"
      },
      "cell_type": "code",
      "source": [
        "num_runs = 10\n",
        "run_rewards = []\n",
        "\n",
        "for n in range(num_runs):\n",
        "    print(\"Run {}\".format(n))\n",
        "    ep_rewards = []\n",
        "    agent = None\n",
        "    agent = DoubleDQNAgent(env)\n",
        "    num_episodes = 200\n",
        "\n",
        "    for ep in range(num_episodes):\n",
        "        state = env.reset()\n",
        "        total_reward = 0\n",
        "        done = False\n",
        "        while not done:\n",
        "            action = agent.get_action(state)\n",
        "            next_state, reward, done, info = env.step(action)\n",
        "            agent.train(state, action, next_state, reward, done, use_DDQN=(n%2==0))\n",
        "            #env.render()\n",
        "            total_reward += reward\n",
        "            state = next_state\n",
        "\n",
        "        ep_rewards.append(total_reward)\n",
        "        #print(\"Episode: {}, total_reward: {:.2f}\".format(ep, total_reward))\n",
        "        \n",
        "    run_rewards.append(ep_rewards)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Run 0\n",
            "Run 1\n",
            "Run 2\n",
            "Run 3\n",
            "Run 4\n",
            "Run 5\n",
            "Run 6\n",
            "Run 7\n",
            "Run 8\n",
            "Run 9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "JU1OmrO0uHpO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "e455da62-b5e4-4e41-9220-0a2931ea6e55"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "for n, ep_rewards in enumerate(run_rewards):\n",
        "    x = range(len(ep_rewards))\n",
        "    cumsum = np.cumsum(ep_rewards)\n",
        "    avgs = [cumsum[ep]/(ep+1) if ep<100 else (cumsum[ep]-cumsum[ep-100])/100 for ep in x]\n",
        "    col = \"r\" if (n%2==0) else \"b\"\n",
        "    plt.plot(x, avgs, color=col, label=n)\n",
        "    \n",
        "plt.title(\"DDQN vs DQN performance\")\n",
        "plt.xlabel(\"Episode\")\n",
        "plt.ylabel(\"Last 100 episode average rewards\")\n",
        "plt.legend()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fc6b18fb630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BHI7zfF/CKDFQKBRXPKaDB4jr0Brbuv+haxreKlUJlL0T5+QJmFJTCV5fksC11+F+qQGJ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Fy0dqWUMpcTcyXM5fiI4u9OZEIkfUJ+YVFhd0JDdQYM0OnXz8cbbwQRFGQw/SOdSTcfYIchCI0O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\nFAoF8P9Jao+oN9fDXQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "_UBHQMHzqsMU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "env.close()"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}