Basic_Reinforcement_Learning_Lunar_Lander.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/todnewman/coe_training/blob/master/Basic_Reinforcement_Learning_Lunar_Lander.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wiBbhnu85woU"
      },
      "source": [
        "# Basic Reinforcement Learning\n",
        "\n",
        "This example shows how a Lunar Lander can be trained to learn how to land between the flags on the ever-changing moon surface.  This example is a more \"verbose\" example, as you can see the classes for Neural Net predictors and for RL Support that I have created.  These classes have been generalized into other applications, including my Raspberry Pi Rover.\n",
        "\n",
        "**Updates**: \n",
        "* Have figured out a more robust way to install gym.  Previous approach stopped working (actually during a class!) after a CoLab update.\n",
        "* The Tensorflow 2.0 upgrade broke the ability to do inference inside a tight loop (something we do in our RL Class).  Developed some new approaches, including a custom TF function.\n",
        "\n",
        "**Author**: W. Tod Newman\n",
        "##Learning Objectives\n",
        "\n",
        "   *  Learn the concepts behind reinforcement learning\n",
        "   *  Understand how the Deep Q-Learning approach works at the highest level\n",
        "   *  See the demo on how well a trained model will work (show the art of the possible!)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Reinforcement Learning (Deep-Q Learning) \"step\" flow Image"
      ],
      "metadata": {
        "id": "NfaV69Au9Ae9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![rl_flow.png](data:image/png;base64,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      ],
      "metadata": {
        "id": "fZ2Bm4H261MY"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0jwhl_P-U86R"
      },
      "source": [
        "## Needed Libraries\n",
        "\n",
        "These are libraries that will allow this code to run.  They are not installed in CoLaboratory by default so we need to load them."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uUu2y9UllZpd"
      },
      "outputs": [],
      "source": [
        "%%time\n",
        "!pip install gym[box2d]\n",
        "!apt install ffmpeg\n",
        "!pip install pyvirtualdisplay"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "#import np_utils\n",
        "import psutil\n",
        "import os\n",
        "import csv\n",
        "import logging\n",
        "import gym\n",
        "import pandas as pd\n",
        "import matplotlib.animation as animation\n",
        "from statistics import mean, stdev\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import HTML\n",
        "from pyvirtualdisplay import Display\n",
        "from google.colab import files\n",
        "import random\n",
        "\n",
        "os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Conv2D, Flatten, Dense, Activation, Dropout, LeakyReLU\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from tensorflow.keras.callbacks import Callback, ModelCheckpoint\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras import backend as K\n",
        "from tensorflow.keras.models import Sequential, Model, load_model\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "6Pc6pQwN_QUi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W435O97VXNtg"
      },
      "source": [
        "## GenNet - Tod's RL Generic Neural Net class\n",
        "\n",
        "This class is very useful and generalizes across a number of RL applications.  It works for other Atari games as well as my Raspberry Pi-based autonomous rover.  This support two neural nets, one fully connected network with two hidden layers that uses the ADAM optimizer and one Convolutional Neural network that has 2 hidden convolutional layers and one hidden fully connected layer (plus code commented out to add a third convolutional layer if desired).  The CNN uses the RMSProp Optimizer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8P4iGj-gN0QI"
      },
      "outputs": [],
      "source": [
        "tf.test.gpu_device_name()\n",
        "\n",
        "print(tf.__version__)\n",
        "\n",
        "\n",
        "class GenNet(object):\n",
        "    \"\"\"\n",
        "    MEMBER FUNCTIONS:\n",
        "    * serve: This is a Tensorflow Function that bypasses TF overhead and returns an inference directly\n",
        "    * cnn: Defines a simple convolutional neural network for cases where images are the environment\n",
        "    * nn: A simple, fully-connected neural network.  We use this for the Lunar Lander environment\n",
        "    * predict: Conducts inference on a trained model.  Overloads TF predict method\n",
        "    * train: Trains based on most recent RL Step data.  Overloads TF train method\n",
        "    * log_results: logs loss values\n",
        "    * save_weights: Saves off model weights for reuse.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, **kwargs):\n",
        "        self.verbose = kwargs.get('verbose', False)\n",
        "        self.nodes_1 = kwargs.get('nodes_1', 50)\n",
        "        self.nodes_2 = kwargs.get('nodes_2', 50)\n",
        "        self.nodes_3 = kwargs.get('nodes_3', 50)\n",
        "        self.num_actions = kwargs.get('num_actions', 4)\n",
        "        self.num_inputs = kwargs.get('num_inputs', 8)\n",
        "\n",
        "        self.load_weights = kwargs.get('load_weights', False)\n",
        "        self.save_weights = kwargs.get('save_weights', False)\n",
        "\n",
        "        self.log_file = kwargs.get('log_file')\n",
        "        self.weights_file = kwargs.get('weights_file', None)\n",
        "        #self.model_out = kwargs.get('model_file', None)\n",
        "        self.obs_shape = kwargs.get('obs_shape')\n",
        "        self.loss_log = []\n",
        "        self.learning_rate = .0005\n",
        "\n",
        "        self.model = self.nn()\n",
        "        #self.model = self.cnn()\n",
        "\n",
        "        if self.verbose:\n",
        "            print(\n",
        "                \"\"\"\n",
        "                    Creating neural net with options:\n",
        "                    Nodes, layer 1: %d\n",
        "                    Nodes, layer 2: %d\n",
        "                    Load weights?: %s\n",
        "                    Save weights?: %s\n",
        "                    Weights file: %s\n",
        "                    Log file: %s\n",
        "                \"\"\"\n",
        "                % (self.nodes_1,\n",
        "                   self.nodes_2,\n",
        "                   str(self.load_weights),\n",
        "                   str(self.save_weights),\n",
        "                   self.weights_file,\n",
        "                   #self.model_out,\n",
        "                   self.log_file)\n",
        "            )\n",
        "            \n",
        "    #\n",
        "    #  This is a TF function we create to return a direct inference of the model.\n",
        "    #  We do this because it's much faster when executed in a loop.\n",
        "    #\n",
        "    @tf.function\n",
        "    def serve(self, x):\n",
        "        return self.model(x, training=False)\n",
        "\n",
        "    def cnn(self):\n",
        "        input_shape = (1,84,110)\n",
        "        model = Sequential()\n",
        "        model.add(Conv2D(16, 8, strides=(4, 4),\n",
        "                              padding='valid',\n",
        "                              activation='relu',\n",
        "                              input_shape=input_shape,\n",
        "                              data_format='channels_first'\n",
        "                              ))\n",
        "\n",
        "        # Second convolutional layer\n",
        "        model.add(Conv2D(32, 4, strides=(2, 2),\n",
        "                              padding='valid',\n",
        "                              activation='relu',\n",
        "                              input_shape=input_shape,\n",
        "                              data_format='channels_first'\n",
        "                              ))\n",
        "        '''\n",
        "        # Third convolutional layer shown for reference (I don't use it...)\n",
        "        model.add(Conv2D(60, 3, strides=(1, 1),\n",
        "                              padding='valid',\n",
        "                              activation='relu',\n",
        "                              input_shape=input_shape,\n",
        "                              data_format='channels_first'\n",
        "                              ))\n",
        "        '''\n",
        "        # Flatten the convolution output\n",
        "        model.add(Flatten())\n",
        "\n",
        "        # First dense layer\n",
        "        model.add(Dense(256, activation='relu'))\n",
        "        \n",
        "        # Output layer\n",
        "        model.add(Dense(self.num_actions))\n",
        "        \n",
        "        # Optimizer - We'll pick Nadam because it works consistently and uses Momentum\n",
        "        optimizer = optimizers.Nadam(learning_rate=self.learning_rate)\n",
        "\n",
        "        # Compile the model and assign loss function and metrics functions as well \n",
        "        # as optimizer.\n",
        "        model.compile(loss='mean_squared_error',\n",
        "                           optimizer=optimizer,\n",
        "                           metrics=['accuracy'])\n",
        "\n",
        "        # Load weights if desired                   \n",
        "        if self.load_weights and self.weights_file is not None:\n",
        "            if self.verbose:\n",
        "                print(\"Loading model from %s\" % self.weights_file)\n",
        "            model.load_weights(self.weights_file)\n",
        "\n",
        "        return model\n",
        "        \n",
        "        \n",
        "    def nn(self):\n",
        "        model = Sequential()\n",
        "\n",
        "        # First layer.\n",
        "        model.add(Dense(\n",
        "            self.nodes_1, \n",
        "            input_shape=(self.num_inputs,)\n",
        "        ))\n",
        "        model.add(LeakyReLU(alpha=0.005))\n",
        "        # If I could figure it out, we could use a training switch \n",
        "        # to turn dropout on when training and off when predicting.\n",
        "\n",
        "        #model.add(Dropout(0.2))\n",
        "\n",
        "        # Second layer.\n",
        "        model.add(Dense(self.nodes_2))\n",
        "        model.add(LeakyReLU(alpha=0.005))\n",
        "        #model.add(Dropout(0.2))\n",
        "        \n",
        "        # Third layer.\n",
        "        model.add(Dense(self.nodes_3))\n",
        "        model.add(LeakyReLU(alpha=0.005))\n",
        "\n",
        "        # Output layer.\n",
        "        model.add(Dense(self.num_actions))\n",
        "        model.add(Activation('linear'))\n",
        "\n",
        "        # Compile the model and assign loss function and metrics functions as well \n",
        "        # as optimizer.\n",
        "        optimizer = optimizers.Nadam(lr=self.learning_rate)\n",
        "        model.compile(loss=\"mse\", optimizer=optimizer, metrics=['accuracy'])\n",
        "        \n",
        "        # Load weights if desired        \n",
        "        if self.load_weights and self.weights_file is not None:\n",
        "            if self.verbose:\n",
        "                print(\"Loading model from %s\" % self.weights_file)\n",
        "            model.load_weights(self.weights_file)\n",
        "\n",
        "        return model\n",
        "\n",
        "    def predict(self, state):\n",
        "        try:\n",
        "            # Including two other approaches for reference.  They're both super-slow.\n",
        "            #return self.model.predict(state.reshape(1, self.num_inputs)) # Slowest method as of TF2.0\n",
        "            #return self.model.predict_on_batch(state.reshape(1, self.num_inputs)) # 10x faster than Predict          \n",
        "            return self.serve(state.reshape(1, self.num_inputs)) # This is 100x faster than Predict\n",
        "            \n",
        "        except:\n",
        "            raise Exception(\"PredictionError\")\n",
        "\n",
        "    def train(self, X, y, batch_size):\n",
        "        history = LossHistory()\n",
        "        sv_mod = ModelCheckpoint(self.weights_file, \n",
        "                                           monitor='accuracy', \n",
        "                                           save_best_only=True, \n",
        "                                           save_freq=\"epoch\")\n",
        "        self.model.fit(\n",
        "            X, y, batch_size=batch_size,\n",
        "            epochs=1, verbose=0, callbacks=[history, sv_mod]\n",
        "        )\n",
        "        self.loss_log.append(history.losses)\n",
        "\n",
        "    def log_results(self):\n",
        "        # Save the results to a file so we can graph it later.\n",
        "        with open(self.log_file, 'w') as lf:\n",
        "            wr = csv.writer(lf)\n",
        "            for loss_item in self.loss_log:\n",
        "                wr.writerow(loss_item)\n",
        "        self.loss_log = []\n",
        "\n",
        "    def save_weights_file(self):\n",
        "        if self.weights_file is not None:\n",
        "            self.model.save_weights(self.weights_file, overwrite=True)\n",
        "\n",
        "\n",
        "class LossHistory(Callback):\n",
        "    def on_train_begin(self, logs={}):\n",
        "        self.losses = []\n",
        "\n",
        "    def on_batch_end(self, batch, logs={}):\n",
        "        self.losses.append(logs.get('loss'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1vYa1LAeVi3P"
      },
      "source": [
        "## GenRL- Tod's Generic RL support Library\n",
        "\n",
        "This class allows us to do basic RL stuff.  It does the interfacing with both the Environment simulation (OpenAI's GYM environment) as well as with the Neural Net action predictor.  It also handles data management and training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8TvC7sSIN_Mg"
      },
      "outputs": [],
      "source": [
        "class Gen_RL(object):\n",
        "    \"\"\"\n",
        "    MEMBER FUNCTIONS:\n",
        "    * determine_action: Determine whether to use learned or random action.\n",
        "    * step: Conduct one RL step, train if we have enough data and increment epsilon.\n",
        "    * process_minibatch: Calculates and returns x and y training data.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, model, **kwargs):\n",
        "\n",
        "        self.verbose = kwargs.get('verbose', False)\n",
        "        self.num_actions = kwargs.get('num_actions')\n",
        "\n",
        "        self.epsilon = kwargs.get('epsilon', 1)\n",
        "        self.min_epsilon = kwargs.get('min_epsilon', 0.05)\n",
        "        self.batch_size = kwargs.get('batch_size')\n",
        "        self.episodes = kwargs.get('episodes')\n",
        "        self.frames = kwargs.get('frames', 0)\n",
        "        self.replay_size = kwargs.get('replay_size')\n",
        "        self.save_steps = kwargs.get('save_steps', 5000)\n",
        "        self.gamma = kwargs.get('gamma', 0.95)\n",
        "        self.num_inputs = kwargs.get('num_inputs')\n",
        "\n",
        "        self.enable_training = kwargs.get('enable_training', True)\n",
        "\n",
        "        self.replay = []\n",
        "        self.steps = 0\n",
        "\n",
        "        self.model = model\n",
        "        self.counter = 0\n",
        "        \n",
        "        #\n",
        "        # If we send along frames, that replaces episodes as the number of\n",
        "        # steps we're going to take. So we need to reduce epsilon by frame\n",
        "        # in this case, not by \"terminal\" or episode.\n",
        "        #\n",
        "        if self.frames != 0:\n",
        "            self.has_terminal = False\n",
        "            self.epsilon_divider = self.frames\n",
        "        else:\n",
        "            self.has_terminal = True\n",
        "            self.epsilon_divider = self.episodes\n",
        "\n",
        "        if self.verbose:\n",
        "            print(\n",
        "                \"\"\"\n",
        "                    Creating learner with options:\n",
        "                    Starting epsilon: %d\n",
        "                    Minimum epsilon: %f\n",
        "                    Batch size: %d\n",
        "                    Episodes: %d\n",
        "                    Frames: %d\n",
        "                    Replay (buffer) size: %d\n",
        "                    Gamma: %f\n",
        "                    Actions: %d\n",
        "                    State inputs: %d\n",
        "                \"\"\"\n",
        "                % (self.epsilon,\n",
        "                   self.min_epsilon,\n",
        "                   self.batch_size,\n",
        "                   self.episodes,\n",
        "                   self.frames,\n",
        "                   self.replay_size,\n",
        "                   self.gamma,\n",
        "                   self.num_actions,\n",
        "                   self.num_inputs)\n",
        "            )\n",
        "\n",
        "    def determine_action(self, state):\n",
        "        '''\n",
        "        Here we decide if we should Guess an action (and then learn from the results\n",
        "        of the guess) or if we should predict a value based upon our model.\n",
        "        '''\n",
        "      \n",
        "        #\n",
        "        # Choose an action.  If our random number is lower than the current\n",
        "        # epsilon we will 'guess' an action.\n",
        "        #\n",
        "        if random.random() < self.epsilon and self.enable_training is True:\n",
        "            action = np.random.randint(0, self.num_actions)  # random\n",
        "        else:\n",
        "          \n",
        "            #\n",
        "            # We randomly landed in 'predict' mode, so we need to get Q values \n",
        "            # for each action.  We take the highest ranked action and return.\n",
        "            #\n",
        "            qval = self.model.predict(state)\n",
        "            action = (np.argmax(qval))\n",
        "\n",
        "        return action\n",
        "\n",
        "    def step(self, state, action, reward, new_state, terminal):\n",
        "        '''\n",
        "        Conduct one RL step, train if we have enough data and increment epsilon.\n",
        "        '''\n",
        "       \n",
        "        #\n",
        "        # Store our Experience replay values each step.\n",
        "        #\n",
        "        self.replay.append((state, action, reward, new_state))\n",
        "        \n",
        "        #\n",
        "        # If we've stored enough in our replay buffer, pop off the oldest.\n",
        "        #\n",
        "        if len(self.replay) > self.replay_size:\n",
        "            self.replay.pop(0)\n",
        "            \n",
        "        #\n",
        "        # If we have enough data to make a minibatch, go forth and train.\n",
        "        #\n",
        "        if len(self.replay) > self.batch_size:\n",
        "          \n",
        "            #\n",
        "            # Randomly sample a batch (batch_size) of our experience replay memory\n",
        "            #\n",
        "            minibatch = random.sample(self.replay, self.batch_size)\n",
        "            \n",
        "            #\n",
        "            # Process this minibatch to get a new set of training values.\n",
        "            #\n",
        "            X_train, y_train = self.process_minibatch(minibatch, terminal)\n",
        "            \n",
        "            #\n",
        "            # Train the model on X,y from this minibatch.  This training is\n",
        "            # cumulative, as we load the weights first.\n",
        "            #\n",
        "            self.model.train(X_train, y_train, self.batch_size)\n",
        "            \n",
        "        #\n",
        "        # Decrement epsilon over time. This will determine how often we 'guess'\n",
        "        # vs. how often we 'predict'\n",
        "        #\n",
        "        if self.epsilon > self.min_epsilon:\n",
        "            if (self.has_terminal and terminal) or self.has_terminal is False:\n",
        "                self.epsilon -= (1.0 / (self.epsilon_divider))\n",
        "        \n",
        "                self.epsilon = max (0.05, self.epsilon)\n",
        "          \n",
        "        #   \n",
        "        # Save weights to our model.  NOT USED DUE TO MODELCHECKPOINT callback\n",
        "        #\n",
        "        #if self.model.save_weights and self.model.weights_file is not None:\n",
        "        #    if self.steps % self.save_steps == 0 and self.steps > 0:\n",
        "        #        print(\"Saving weights.\")\n",
        "        #        self.model.save_weights_file()\n",
        "\n",
        "        self.steps += 1\n",
        "        \n",
        "\n",
        "    def process_minibatch(self, minibatch, terminal=False):\n",
        "        ''' \n",
        "        Basic Q-Learning math.  We use our model to predict the current and previous\n",
        "        Q-values for each record in our replay memory batch.\n",
        "        Eventually returns the X and y training arrays.\n",
        "        '''\n",
        "      \n",
        "        X_train = []\n",
        "        y_train = []\n",
        "        \n",
        "        #\n",
        "        # Loop through our batch and create arrays for X and y\n",
        "        # so that we can fit our model at every step.\n",
        "        #\n",
        "        for memory in minibatch:\n",
        "          \n",
        "            #\n",
        "            # Get stored values for each record in the minibatch.\n",
        "            #\n",
        "            old_state_m, action_m, reward_m, new_state_m = memory\n",
        "            \n",
        "            #\n",
        "            # Use trained model to get action prediction on old state.\n",
        "            #\n",
        "            old_qval = self.model.predict(old_state_m)\n",
        "            \n",
        "            #\n",
        "            # Get action prediction on new state.\n",
        "            #\n",
        "            newQ = self.model.predict(new_state_m)\n",
        "            \n",
        "            #\n",
        "            # Estimate the optimal FUTURE value using Numpy's max.\n",
        "            #\n",
        "            maxQ = np.max(newQ)\n",
        "            \n",
        "            #\n",
        "            # Define our label - The previous set of actions\n",
        "            #\n",
        "            y = np.zeros((1, self.num_actions))\n",
        "            y[:] = old_qval[:]\n",
        "            \n",
        "            #\n",
        "            # Check for terminal state (Signifies Episode is complete).\n",
        "            #\n",
        "            if not terminal: # Episode is not complete\n",
        "                #\n",
        "                # Here's where we perform a simple value iteration update, \n",
        "                # using the weighted average of the old action value and the new information\n",
        "                # multiplied by our discount rate (gamma).\n",
        "                #\n",
        "                update = (reward_m + (self.gamma * maxQ))\n",
        "                \n",
        "            else:  # Episode is complete.  No use in applying the future parameters.\n",
        "                update = reward_m\n",
        "                \n",
        "            #\n",
        "            # Assign the new Q-value to y for the action we took.  This is how we\n",
        "            # associate actions with rewards!\n",
        "            #\n",
        "            y[0][action_m] = update\n",
        "            \n",
        "            #\n",
        "            # Build training dataset by appending our previous state space\n",
        "            # and our new y labels for previous and future actions.\n",
        "            #\n",
        "            X_train.append(old_state_m.reshape(self.num_inputs,))\n",
        "            y_train.append(y.reshape(self.num_actions,))\n",
        "\n",
        "        X_train = np.array(X_train)\n",
        "        y_train = np.array(y_train)\n",
        "\n",
        "        return X_train, y_train\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zrv8q8QCqfhT"
      },
      "source": [
        "## Pick the Weights File for \"Show\" Mode\n",
        "\n",
        "Right now I have the trained lunarlander file saved in my downloads directory"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "98crw1xCUaJy"
      },
      "outputs": [],
      "source": [
        "!wget -O /content/lunarlander-mod 'https://raw.githubusercontent.com/todnewman/models/master/lunarlander_50_50_50.hdf5'\n",
        "%cd /content\n",
        "%ls -al"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WOTf3HRB6QnI"
      },
      "source": [
        "## Main Function\n",
        "\n",
        "This section will set up the Neural Net to predict what actions the Lunar Lander should take.  It also governs the initialization of the environment simulation and allows the Lunar Lander to receive environmental state values from the environment simulation and then receive rewards based on the appropriateness of the action.\n",
        "\n",
        "This will train to be able to perform this activity after about 5,000 episodes.\n",
        "\n",
        "To Train the Lander, uncomment the line \"train_or_show = train\" and ensure that \"show\" is commented out.  Once a trained model has been saved, switch to \"show\" mode to load the model into the empty lunar lander and start it predicting actions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jVGaOA068r3s"
      },
      "source": [
        "files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "No9eymbLNsCb"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "Implements Gen_RL in attempt to solve Gym's Lunar Lander environment.\n",
        "This is the main function that calls the classes in later workbook blocks.\n",
        "Better to execute this block last...\n",
        "\"\"\"\n",
        "\n",
        "import time\n",
        "\n",
        "frames = []\n",
        "\n",
        "#\n",
        "# Change the number of episodes.  About 5K episodes are needed to train the \n",
        "# model effectively.\n",
        "#\n",
        "episodes =  20\n",
        "\n",
        "#\n",
        "# The below won't change for LunarLander\n",
        "#\n",
        "inputs = 8\n",
        "actions = 4\n",
        "\n",
        "#\n",
        "# Just change these to switch from training mode to inference (show) mode.\n",
        "# When in 'show' mode you need to make sure you have a weights file uploaded\n",
        "# (see previous block)\n",
        "#\n",
        "\n",
        "train_or_show = 'train'            # TRAINING the agent\n",
        "#train_or_show = 'show'            # LOAD Trained Model in an Empty Agent\n",
        "\n",
        "\n",
        "log_file = 'loss_log.txt'\n",
        "\n",
        "#\n",
        "# Define parameters for training vs. inference\n",
        "#\n",
        "if train_or_show == 'train':\n",
        "    weights_file = 'lunarlander-train'\n",
        "    enable_training = True\n",
        "    load_weights = False\n",
        "    save_weights = True\n",
        "else:\n",
        "    weights_file = 'lunarlander-mod'\n",
        "    enable_training = False\n",
        "    load_weights = True\n",
        "    save_weights = False\n",
        "#\n",
        "# Create the environment. You can change this to other Gym environments\n",
        "# to experiment.  Here we'll print out the action and observation spaces.\n",
        "#\n",
        "env = gym.make('LunarLander-v2')\n",
        "\n",
        "obs_shape = env.observation_space.shape\n",
        "\n",
        "#\n",
        "# Set up the network by creating an instance of the GenNet class.\n",
        "#\n",
        "network = GenNet(nodes_1=50, nodes_2=50, nodes_3=50, num_actions=actions, \n",
        "                   num_inputs=inputs, verbose=True,\n",
        "                   load_weights=load_weights, weights_file=weights_file, \n",
        "                   obs_shape=obs_shape, save_weights=save_weights, \n",
        "                   log_file=log_file)\n",
        "                  \n",
        "                 \n",
        "#\n",
        "# Setup Gen RL's deep RL model by creating an instance of the Gen_RL class.\n",
        "#\n",
        "rl = Gen_RL(network, episodes=episodes, num_actions=actions,\n",
        "                  batch_size=32, min_epsilon=0.05, num_inputs=inputs,\n",
        "                  replay_size=10000, gamma=0.95, verbose=True,\n",
        "                  enable_training=enable_training)\n",
        "\n",
        "def show_RAM_usage():\n",
        "    py = psutil.Process(os.getpid())\n",
        "    print('RAM usage: {} GB'.format(py.memory_info()[0]/2. ** 30))\n",
        "\n",
        "rewards = []\n",
        "results = []\n",
        "max_steps = 600\n",
        "repeat_action = 3\n",
        "\n",
        "\n",
        "# Run.\n",
        "for i in range(episodes):\n",
        "    K.clear_session() # for the TF2.0 Memory Leak?\n",
        "    start = time.time()\n",
        "\n",
        "    # Get initial state.\n",
        "    state = env.reset()\n",
        "\n",
        "    terminal = False\n",
        "    e_rewards = 0\n",
        "    steps = 0\n",
        "\n",
        "    while not terminal:\n",
        "        steps += 1\n",
        "        \n",
        "        #\n",
        "        # Send the state to Gen_RL, get our predicted action.\n",
        "        #\n",
        "        action = rl.determine_action(state)\n",
        "        #\n",
        "        # Best results come from performing the same action multiple times.  This\n",
        "        # comes from the original DeepMind DQN paper.  This improves training time\n",
        "        # but doesn't really sacrifice performance.  \n",
        "        # Each time, the environment (OpenAI Gym) gives us the new state, a reward,\n",
        "        # and whether the Episode is terminal or not.\n",
        "        #\n",
        "        for x in range(repeat_action):\n",
        "            new_state, reward, terminal, _ = env.step(action)   \n",
        "        #\n",
        "        # Create an array of frames from the environment's 'render' feature.\n",
        "        # This is primarily done in CoLaboratory due to the difficulty of\n",
        "        # rendering to a display.  Outside of CoLab I'd just call env.render().\n",
        "        # We switch this out when training large datasets because it will use all your\n",
        "        # memory.\n",
        "        #\n",
        "        if train_or_show == 'show' or (train_or_show == 'train' and episodes < 60):\n",
        "            frames.append(env.render(mode = 'rgb_array'))\n",
        "        #\n",
        "        # Add the info to our experience replay for training.  Experience Replay\n",
        "        # is the breakthrough where data acquired during the online learning\n",
        "        # process are stored and presented repeatedly to the underlying\n",
        "        # RL algorithm. This increases data efficiency, while exploiting\n",
        "        # the computational efficiency of the underlying algorithm.  There is a\n",
        "        # lot of research into ER these days to improve it and mitigate its liabilities.\n",
        "        # - https://arxiv.org/abs/1712.01275\n",
        "        # - http://www.busoniu.net/files/papers/smcc11.pdf\n",
        "        #\n",
        "        if enable_training:\n",
        "            rl.step(state, action, reward, new_state, terminal)   \n",
        "        #\n",
        "        # Accumulate rewards.\n",
        "        #\n",
        "        e_rewards += reward\n",
        "        \n",
        "        #\n",
        "        # If the Episode is complete.\n",
        "        #\n",
        "        if terminal:\n",
        "            \n",
        "            #\n",
        "            # The Episode is complete.  If we aren't training, be more verbose \n",
        "            # about the mission.\n",
        "            #\n",
        "            if not enable_training:\n",
        "                if reward == -100:\n",
        "                    result = 'Crashed'\n",
        "                else:\n",
        "                    result = 'Landed'\n",
        "                print(\"%s! Score: %d\" % (result, e_rewards))\n",
        "\n",
        "            # Create Rewards Array.\n",
        "            rewards.append(e_rewards)\n",
        "            if len(rewards) > 10:\n",
        "                rewards.pop(0)\n",
        "            e_rewards = 0\n",
        "            \n",
        "        #\n",
        "        # Update state for the next iteration in this episode. Our next action\n",
        "        # will be based off of this state.\n",
        "        #\n",
        "        state = new_state\n",
        "        \n",
        "        #\n",
        "        # This keeps us from getting stuck.\n",
        "        #\n",
        "        if steps > max_steps:\n",
        "            print(\"Too many steps.\")\n",
        "            break\n",
        "        #end = time.time()\n",
        "        #print(end - start)\n",
        "    #\n",
        "    # Every 10th episode, print out parametrics.  This will help us determine\n",
        "    # how well the training is progressing.\n",
        "    #\n",
        "    if i % 10 == 0 and i > 0:\n",
        "        end = time.time()\n",
        "        show_RAM_usage()\n",
        "        print(\"-\"*80)\n",
        "        print(\"Epsilon: %.5f\" % rl.epsilon)\n",
        "        print(\"Episode: %d\" % i)\n",
        "        print(\"Mean: %.2f\\tMax: %d\\tStdev: %.2f\" %\n",
        "              (mean(rewards), max(rewards), stdev(rewards)))\n",
        "        print(f\"Time for 10 Episodes: {(end-start)*10}\")\n",
        "        results.append(mean(rewards))\n",
        "\n",
        "# Just print out all of our mean rewards so we can plot them or\n",
        "# do other fun things.\n",
        "\n",
        "#plt.figure(figsize=(frames[0].shape[1] / 72.0, frames[0].shape[0] / 72.0), dpi = 72)\n",
        "\n",
        "\n",
        "if train_or_show == 'show':\n",
        "    patch = plt.imshow(frames[0])\n",
        "    plt.axis('off')\n",
        "    animate = lambda i: patch.set_data(frames[i])\n",
        "    ani = animation.FuncAnimation(plt.gcf(), animate, frames=len(frames), interval = 50)\n",
        "else:\n",
        "    patch = plt.imshow(frames[0])\n",
        "    animate = lambda i: patch.set_data(frames[i])\n",
        "    network.log_results()\n",
        "    df3 = pd.read_csv(log_file, header=0)\n",
        "    df3.columns = ['Loss']\n",
        "    df3.plot(y='Loss')\n",
        "    for r in results:\n",
        "        print(r)\n",
        "\n",
        "env.close()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Videos\n",
        "(note the differences)\n",
        "1. [Crazy Agent Undergoing Training!](https://github.com/todnewman/coe_training/blob/master/videos/RL_training_process.mp4)\n",
        "2. [Confident Agent with Downloaded Brains](https://github.com/todnewman/coe_training/blob/master/videos/RL_inference.mp4)\n"
      ],
      "metadata": {
        "id": "HRWXDGZsB_eA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import HTML\n",
        "from base64 import b64encode\n",
        "# Grab the Videos\n",
        "!wget -O /content/training_model.mp4 'https://raw.githubusercontent.com/todnewman/coe_training/master/videos/RL_training_process.mp4'\n",
        "!wget -O /content/inference_model.mp4 'https://raw.githubusercontent.com/todnewman/coe_training/master/videos/RL_inference.mp4'\n",
        "\n",
        "mp4 = open('/content/training_model.mp4','rb').read()\n",
        "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
        "HTML(\"\"\"\n",
        "<video width=1200 controls>\n",
        "      <source src=\"%s\" type=\"video/mp4\">\n",
        "</video>\n",
        "\"\"\" % data_url)"
      ],
      "metadata": {
        "id": "zt1KfrKQDFd5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mp4 = open('/content/inference_model.mp4','rb').read()\n",
        "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
        "HTML(\"\"\"\n",
        "<video width=1200 controls>\n",
        "      <source src=\"%s\" type=\"video/mp4\">\n",
        "</video>\n",
        "\"\"\" % data_url)"
      ],
      "metadata": {
        "id": "kPwsqVuqGI_3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# How to CREATE the Video Files below\n",
        "\n",
        "I'm showing a couple of different methods\n",
        "* HTML5 MP4 \n",
        "* Javascript HTML"
      ],
      "metadata": {
        "id": "o4FjixcIMKlG"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hNmdk7vmKo7O"
      },
      "source": [
        "## Create HTML5 (MP4) video\n",
        "\n",
        "Here we can create a MP4 movie that can be sent as an attachment, etc.\n",
        "* This takes about 2.5 minutes for a 20 episode video."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wUjPkuUWAOrk"
      },
      "outputs": [],
      "source": [
        "#\n",
        "# Install some libraries to support the .mp4 video\n",
        "#\n",
        "!apt-get -qq -y install xvfb freeglut3-dev ffmpeg> /dev/null\n",
        "!apt install ffmpeg\n",
        "\n",
        "patch = plt.imshow(frames[0])\n",
        "animate = lambda i: patch.set_data(frames[i])        \n",
        "ani = animation.FuncAnimation(plt.gcf(), animate, frames=len(frames), interval = 50)\n",
        "%time HTML(ani.to_html5_video())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OK2ZtjedKw21"
      },
      "source": [
        "## Create JSHTML video\n",
        "\n",
        "Rendering Javascript HTML so you can see the performance of the lander.  Be patient, this takes just over a minute."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gU6gcPAql1jr"
      },
      "outputs": [],
      "source": [
        "%time HTML(ani.to_jshtml())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PcO4XHY5KYRJ"
      },
      "source": [
        "## Save Video to Downloads folder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "orKKsKTrvpkh"
      },
      "outputs": [],
      "source": [
        "\n",
        "!apt-get -qq -y install xvfb freeglut3-dev ffmpeg> /dev/null\n",
        "\n",
        "\n",
        "Writer = animation.writers['ffmpeg']\n",
        "writer = Writer(fps=15, metadata=dict(artist='TodN'), bitrate=1800)\n",
        "ani.save('working_inference.mp4', writer=writer)\n",
        "files.download('working_inference.mp4')  # from colab to browser download\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tjbbEBrNwg7u"
      },
      "outputs": [],
      "source": [
        "from google.colab import files\n",
        "files.download('loss_log.txt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "89ZxlSOEyKdG"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "machine_shape": "hm",
      "name": "Basic Reinforcement Learning - Lunar Lander.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}