dddqn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow .contrib.slim as slim\n",
    "import numpy as np\n",
    "import random\n",
    "from game_env import gameEnv\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.misc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env = gameEnv(84,3)\n",
    "env.drawGridState()\n",
    "env.actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Qnetwork():\n",
    "    def __init__(self,h_size):\n",
    "        #Receives a (P,P,3) representation of the game, pixels x pixels by rgb,\n",
    "        #specified when creating game environment\n",
    "        #The network recieves a frame from the game, flattened into an array.\n",
    "        #It then resizes it and processes it through four convolutional layers.\n",
    "        self.scalarInput =  tf.placeholder(shape=[None,21168],dtype=tf.float32)\n",
    "        self.imageIn = tf.reshape(self.scalarInput,shape=[-1,84,84,3])\n",
    "\n",
    "        self.conv1 = slim.conv2d( \\\n",
    "            inputs=self.imageIn,num_outputs=32,kernel_size=[8,8],stride=[4,4],padding='VALID', biases_initializer=None)\n",
    "        self.conv2 = slim.conv2d( \\\n",
    "            inputs=self.conv1,num_outputs=64,kernel_size=[4,4],stride=[2,2],padding='VALID', biases_initializer=None)\n",
    "        self.conv3 = slim.conv2d( \\\n",
    "            inputs=self.conv2,num_outputs=64,kernel_size=[3,3],stride=[1,1],padding='VALID', biases_initializer=None)\n",
    "        self.conv4 = slim.conv2d( \\\n",
    "            inputs=self.conv3,num_outputs=h_size,kernel_size=[7,7],stride=[1,1],padding='VALID', biases_initializer=None)\n",
    "        \n",
    "        \n",
    "        \n",
    "        #We take the output from the final convolutional layer and split it into separate advantage and value streams.\n",
    "        self.streamAC,self.streamVC = tf.split(self.conv4,2,3)\n",
    "        self.streamA = slim.flatten(self.streamAC)\n",
    "        self.streamV = slim.flatten(self.streamVC)\n",
    "        xavier_init = tf.contrib.layers.xavier_initializer()\n",
    "        self.AW = tf.Variable(xavier_init([h_size//2,env.actions]))\n",
    "        self.VW = tf.Variable(xavier_init([h_size//2,1]))\n",
    "        self.Advantage = tf.matmul(self.streamA,self.AW)\n",
    "        self.Value = tf.matmul(self.streamV,self.VW)\n",
    "        \n",
    "        #Then combine them together to get our final Q-values.\n",
    "        self.Qout = self.Value + tf.subtract(self.Advantage,tf.reduce_mean(self.Advantage,axis=1,keep_dims=True))\n",
    "        self.predict = tf.argmax(self.Qout,1)\n",
    "        \n",
    "        #Below we obtain the loss by taking the sum of squares difference between the target and prediction Q values.\n",
    "        self.targetQ = tf.placeholder(shape=[None],dtype=tf.float32)\n",
    "        self.actions = tf.placeholder(shape=[None],dtype=tf.int32)\n",
    "        self.actions_onehot = tf.one_hot(self.actions,env.actions,dtype=tf.float32)\n",
    "        \n",
    "        self.Q = tf.reduce_sum(tf.multiply(self.Qout, self.actions_onehot), axis=1)\n",
    "        \n",
    "        self.td_error = tf.square(self.targetQ - self.Q)\n",
    "        self.loss = tf.reduce_mean(self.td_error)\n",
    "        self.trainer = tf.train.AdamOptimizer(learning_rate=0.0001)\n",
    "        self.updateModel = self.trainer.minimize(self.loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class experience_buffer():\n",
    "    def __init__(self, buffer_size = 50000):\n",
    "        self.buffer = []\n",
    "        self.buffer_size = buffer_size\n",
    "    \n",
    "    def add(self,experience):\n",
    "        if len(self.buffer) + len(experience) >= self.buffer_size:\n",
    "            self.buffer[0:(len(experience)+len(self.buffer))-self.buffer_size] = []\n",
    "        self.buffer.extend(experience)\n",
    "            \n",
    "    def sample(self,size):\n",
    "        return np.reshape(np.array(random.sample(self.buffer,size)),[size,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def updateTargetGraph(tfVars,tau):\n",
    "    total_vars = len(tfVars)\n",
    "    op_holder = []\n",
    "    for idx,var in enumerate(tfVars[0:total_vars//2]):\n",
    "        op_holder.append(tfVars[idx+total_vars//2].assign((var.value()*tau) + ((1-tau)*tfVars[idx+total_vars//2].value())))\n",
    "    return op_holder\n",
    "\n",
    "def updateTarget(op_holder,sess):\n",
    "    for op in op_holder:\n",
    "        sess.run(op)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def processState(states):\n",
    "    return np.reshape(states,[21168])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32 #How many experiences to use for each training step.\n",
    "update_freq = 4 #How often to perform a training step.\n",
    "y = .99 #Discount factor on the target Q-values\n",
    "startE = 1 #Starting chance of random action\n",
    "endE = 0.1 #Final chance of random action\n",
    "annealing_steps = 10000. #How many steps of training to reduce startE to endE.\n",
    "num_episodes = 20000 #How many episodes of game environment to train network with.\n",
    "pre_train_steps = 10000 #How many steps of random actions before training begins.\n",
    "max_epLength = 50 #The max allowed length of our episode.\n",
    "load_model = False #Whether to load a saved model.\n",
    "path = \"./dqn\" #The path to save our model to.\n",
    "h_size = 512 #The size of the final convolutional layer before splitting it into Advantage and Value streams.\n",
    "tau = 0.001 #Rate to update target network toward primary network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      "WARNING:tensorflow:From <ipython-input-3-8331eaf1692c>:28: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
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      "185170 -0.45999999999999985 0.09999999999985551\n",
      "185234 -0.27999999999999997 0.09999999999985551\n",
      "185310 -0.5199999999999999 0.09999999999985551\n",
      "185370 0.0 0.09999999999985551\n",
      "Percent of succesful episodes: -0.7977599999999269%\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "mainQN = Qnetwork(h_size)\n",
    "targetQN = Qnetwork(h_size)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "trainables = tf.trainable_variables()\n",
    "\n",
    "targetOps = updateTargetGraph(trainables,tau)\n",
    "\n",
    "myBuffer = experience_buffer()\n",
    "\n",
    "e = startE\n",
    "stepDrop = (startE - endE)/annealing_steps\n",
    "\n",
    "#create lists to contain total rewards and steps per episode\n",
    "jList = []\n",
    "rList = []\n",
    "total_steps = 0\n",
    "\n",
    "#Make a path for our model to be saved in.\n",
    "if not os.path.exists(path):\n",
    "    os.makedirs(path)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    if load_model == True:\n",
    "        print('Loading Model...')\n",
    "        ckpt = tf.train.get_checkpoint_state(path)\n",
    "        saver.restore(sess,ckpt.model_checkpoint_path)\n",
    "    for i in range(num_episodes):\n",
    "        episodeBuffer = experience_buffer()\n",
    "        #Resetting env and getting first observation\n",
    "        env.reset()\n",
    "        s = env.reset()\n",
    "        s = processState(s)\n",
    "\n",
    "        d = env.game_over\n",
    "        rAll = 0\n",
    "        j = 0\n",
    "        #The Q-Network\n",
    "        #If agent takes longer than max episodes, end trial\n",
    "        while j < max_epLength:\n",
    "            j += 1\n",
    "            #Choose action greedily(with e chance of random action) from Q-network\n",
    "            if np.random.rand(1) < e or total_steps < pre_train_steps:\n",
    "                a = np.random.randint(0,env.actions)\n",
    "            else:\n",
    "                a = sess.run(mainQN.predict,feed_dict={mainQN.scalarInput:[s]})[0]\n",
    "            s1,r,d = env.step(a)\n",
    "            s1 = processState(s1)\n",
    "            total_steps += 1\n",
    "            episodeBuffer.add(np.reshape(np.array([s,a,r,s1,d]),[1,5])) \n",
    "            #Save the experience to our episode buffer.\n",
    "            \n",
    "\n",
    "            if total_steps > pre_train_steps:\n",
    "                if e > endE:\n",
    "                    e -= stepDrop\n",
    "                \n",
    "                if total_steps % (update_freq) == 0:\n",
    "                    trainBatch = myBuffer.sample(batch_size) #Get a random batch of experiences.\n",
    "                    #Below we perform the Double-DQN update to the target Q-values\n",
    "                    Q1 = sess.run(mainQN.predict,feed_dict={mainQN.scalarInput:np.vstack(trainBatch[:,3])})\n",
    "                    Q2 = sess.run(targetQN.Qout,feed_dict={targetQN.scalarInput:np.vstack(trainBatch[:,3])})\n",
    "                    end_multiplier = -(trainBatch[:,4] - 1)\n",
    "                    doubleQ = Q2[range(batch_size),Q1]\n",
    "                    targetQ = trainBatch[:,2] + (y*doubleQ * end_multiplier)\n",
    "                    #Update the network with our target values.\n",
    "                    _ = sess.run(mainQN.updateModel, \\\n",
    "                        feed_dict={mainQN.scalarInput:np.vstack(trainBatch[:,0]),mainQN.targetQ:targetQ, mainQN.actions:trainBatch[:,1]})\n",
    "                    \n",
    "                    updateTarget(targetOps,sess) #Update the target network toward the primary network.\n",
    "            rAll += r\n",
    "            s = s1\n",
    "            \n",
    "            if d == True:\n",
    "\n",
    "                break\n",
    "        \n",
    "        myBuffer.add(episodeBuffer.buffer)\n",
    "        jList.append(j)\n",
    "        rList.append(rAll)\n",
    "        #Periodically save the model. \n",
    "        if i % 1000 == 0:\n",
    "            saver.save(sess,path+'/model-'+str(i)+'.ckpt')\n",
    "            print(\"Saved Model\")\n",
    "        if len(rList) % 10 == 0:\n",
    "            print(total_steps,np.mean(rList[-10:]), e)\n",
    "    saver.save(sess,path+'/model-'+str(i)+'.ckpt')\n",
    "print(\"Percent of succesful episodes: \" + str(sum(rList)/num_episodes) + \"%\")\n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d5cd66e2b0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rMat = np.resize(np.array(rList),[len(rList)//100,100])\n",
    "rMean = np.average(rMat,1)\n",
    "plt.plot(rMean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}