notebooks/cartpole_dqn.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/jonkrohn/DLTFpT/blob/master/notebooks/cartpole_dqn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xDKKjBC8iHM-",
        "colab_type": "text"
      },
      "source": [
        "# Cartpole DQN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cQzGnJTaiHNF",
        "colab_type": "text"
      },
      "source": [
        "Deep Q-Learning Network with Keras and OpenAI Gym, based on [Keon Kim's code](https://github.com/keon/deep-q-learning/blob/master/dqn.py)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4JCCrMfliHNK",
        "colab_type": "text"
      },
      "source": [
        "#### Import dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7C9_lB1iHNN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import random\n",
        "import gym\n",
        "import numpy as np\n",
        "from collections import deque\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.optimizers import Nadam\n",
        "import os # for creating directories"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f1_GzakuiHNV",
        "colab_type": "text"
      },
      "source": [
        "#### Set hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_2sPz36XiHNW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "env = gym.make('CartPole-v0') # initialise environment"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cK0V5UQXiHNd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8089775e-1976-42fd-eb14-5df1718f59ee"
      },
      "source": [
        "state_size = env.observation_space.shape[0]\n",
        "state_size"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T3ad_bEGiHNm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e9e3c057-aaf6-4894-96cd-b5afde13aabf"
      },
      "source": [
        "action_size = env.action_space.n\n",
        "action_size"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0JRMP1diHNr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 32"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DDZrj7iiHNw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_episodes = 1000 # n games we want agent to play "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dF34tJugiHN3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "output_dir = 'model_output/cartpole/'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PlV1yezViHN_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if not os.path.exists(output_dir):\n",
        "    os.makedirs(output_dir)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "beLpgy0SiHOF",
        "colab_type": "text"
      },
      "source": [
        "#### Define agent"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DiQrKSxiHOH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class DQNAgent:\n",
        "    def __init__(self, state_size, action_size):\n",
        "        self.state_size = state_size\n",
        "        self.action_size = action_size\n",
        "        self.memory = deque(maxlen=2000) # double-ended queue; acts like list, but elements can be added/removed from either end\n",
        "        self.gamma = 0.95 # decay or discount rate: enables agent to take into account future actions in addition to the immediate ones, but discounted at this rate\n",
        "        self.epsilon = 1.0 # exploration rate: how much to act randomly; more initially than later due to epsilon decay\n",
        "        self.epsilon_decay = 0.995 # decrease number of random explorations as the agent's performance (hopefully) improves over time\n",
        "        self.epsilon_min = 0.01 # minimum amount of random exploration permitted\n",
        "        self.learning_rate = 0.001 # rate at which NN adjusts models parameters via SGD to reduce cost \n",
        "        self.model = self._build_model() # private method \n",
        "    \n",
        "    def _build_model(self):\n",
        "        # neural net to approximate Q-value function:\n",
        "        model = Sequential()\n",
        "        model.add(Dense(32, activation='relu', \n",
        "                        input_dim=self.state_size)) # 1st hidden layer; states as input\n",
        "        model.add(Dense(32, activation='relu')) # 2nd hidden layer\n",
        "        model.add(Dense(self.action_size, activation='linear')) # 2 actions, so 2 output neurons: 0 and 1 (L/R)\n",
        "        model.compile(loss='mse',\n",
        "                      optimizer=Nadam(lr=self.learning_rate))\n",
        "        return model\n",
        "    \n",
        "    def remember(self, state, action, reward, next_state, done):\n",
        "        self.memory.append((state, action, \n",
        "                            reward, next_state, done)) # list of previous experiences, enabling re-training later\n",
        "\n",
        "    def train(self, batch_size): # method that trains NN with experiences sampled from memory\n",
        "        minibatch = random.sample(self.memory, batch_size) # sample a minibatch from memory\n",
        "        for state, action, reward, next_state, done in minibatch: # extract data for each minibatch sample\n",
        "            target = reward # if done (boolean whether game ended or not, i.e., whether final state or not), then target = reward\n",
        "            if not done: # if not done, then predict future discounted reward\n",
        "                target = (reward + \n",
        "                          self.gamma * # (target) = reward + (discount rate gamma) * \n",
        "                          np.amax(self.model.predict(next_state)[0])) # (maximum target Q based on future action a')\n",
        "            target_f = self.model.predict(state) # approximately map current state to future discounted reward\n",
        "            target_f[0][action] = target\n",
        "            self.model.fit(state, target_f, epochs=1, verbose=0) # single epoch of training with x=state, y=target_f; fit decreases loss btwn target_f and y_hat\n",
        "        if self.epsilon > self.epsilon_min:\n",
        "            self.epsilon *= self.epsilon_decay\n",
        "\n",
        "    def act(self, state):\n",
        "        if np.random.rand() <= self.epsilon: # if acting randomly, take random action\n",
        "            return random.randrange(self.action_size)\n",
        "        act_values = self.model.predict(state) # if not acting randomly, predict reward value based on current state\n",
        "        return np.argmax(act_values[0]) # pick the action that will give the highest reward (i.e., go left or right?)\n",
        "    \n",
        "    def save(self, name):\n",
        "        self.model.save_weights(name)\n",
        "\n",
        "    def load(self, name):\n",
        "        self.model.load_weights(name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8z5loBrJiHON",
        "colab_type": "text"
      },
      "source": [
        "#### Interact with environment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7r7dDq-0iHOO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "agent = DQNAgent(state_size, action_size) # initialise agent"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H2v8IrqviHOS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9ebdc192-9e47-4962-f216-6c7a6fff9cc6"
      },
      "source": [
        "for e in range(n_episodes): # iterate over episodes of gameplay\n",
        "    \n",
        "    state = env.reset() # reset state at start of each new episode of the game\n",
        "    state = np.reshape(state, [1, state_size])\n",
        "    \n",
        "    done = False\n",
        "    time = 0 # time represents a frame of the episode; goal is to keep pole upright as long as possible\n",
        "    while not done: \n",
        "#         env.render()\n",
        "        action = agent.act(state) # action is either 0 or 1 (move cart left or right); decide on one or other here\n",
        "        next_state, reward, done, _ = env.step(action) # agent interacts with env, gets feedback; 4 state data points, e.g., pole angle, cart position        \n",
        "        reward = reward if not done else -10 # reward +1 for each additional frame with pole upright        \n",
        "        next_state = np.reshape(next_state, [1, state_size])\n",
        "        agent.remember(state, action, reward, next_state, done) # remember the previous timestep's state, actions, reward, etc.        \n",
        "        state = next_state # set \"current state\" for upcoming iteration to the current next state        \n",
        "        if done: # if episode ends: \n",
        "            print(\"episode: {}/{}, score: {}, e: {:.2}\" # print the episode's score and agent's epsilon\n",
        "                  .format(e, n_episodes-1, time, agent.epsilon))\n",
        "        time += 1\n",
        "    if len(agent.memory) > batch_size:\n",
        "        agent.train(batch_size) # train the agent by replaying the experiences of the episode\n",
        "    if e % 50 == 0:\n",
        "        agent.save(output_dir + \"weights_\" \n",
        "                   + '{:04d}'.format(e) + \".hdf5\") "
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rMUA_MS7iHOX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# saved agents can be loaded with agent.load(\"./path/filename.hdf5\") "
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}