Recurrent_net.py

import tensorflow as tf
import cv2  # read in pixel data
import pong  # our class
import numpy as np  # math
import random  # random
from collections import deque  # queue data structure. fast appends. and pops. replay memory

#DQN. CNN reads in pixel data. 
#reinforcement learning. trial and error.
#maximize action based on reward
#agent environment loop
#this is called Q Learning
#based on just game state. mapping of state to action is policy
#experience replay. learns from past policies

#hyper parameters
ACTIONS = 3  # up,down, stay
#define our learning rate
GAMMA = 0.99
#for updating our gradient or training over time
INITIAL_EPSILON = 1.0
FINAL_EPSILON = 0.05
#how many frames to anneal epsilon
EXPLORE = 500000
OBSERVE = 50000
#store our experiences, the size of it
REPLAY_MEMORY = 500000
#batch size to train on
BATCH = 100

#create tensorflow graph


def createGraph():

    #first convolutional layer. bias vector
    #creates an empty tensor with all elements set to zero with a shape
    W_conv1 = tf.Variable(tf.zeros([8, 8, 4, 32]))
    b_conv1 = tf.Variable(tf.zeros([32]))

    W_conv2 = tf.Variable(tf.zeros([4, 4, 32, 64]))
    b_conv2 = tf.Variable(tf.zeros([64]))

    W_conv3 = tf.Variable(tf.zeros([3, 3, 64, 64]))
    b_conv3 = tf.Variable(tf.zeros([64]))

    W_fc4 = tf.Variable(tf.zeros([3136, 784]))
    b_fc4 = tf.Variable(tf.zeros([784]))

    W_fc5 = tf.Variable(tf.zeros([784, ACTIONS]))
    b_fc5 = tf.Variable(tf.zeros([ACTIONS]))

    #input for pixel data
    s = tf.placeholder("float", [None, 84, 84, 4])

    #Computes rectified linear unit activation function on  a 2-D convolution given 4-D input and filter tensors
    conv1 = tf.nn.relu(tf.nn.conv2d(s, W_conv1, strides=[1, 4, 4, 1], padding="VALID") + b_conv1)

    conv2 = tf.nn.relu(tf.nn.conv2d(conv1, W_conv2, strides=[1, 2, 2, 1], padding="VALID") + b_conv2)

    conv3 = tf.nn.relu(tf.nn.conv2d(conv2, W_conv3, strides=[1, 1, 1, 1], padding="VALID") + b_conv3)

    conv3_flat = tf.reshape(conv3, [-1, 3136])

    fc4 = tf.nn.relu(tf.matmul(conv3_flat, W_fc4) + b_fc4)

    fc5 = tf.matmul(fc4, W_fc5) + b_fc5

    return s, fc5


#deep q network. feed in pixel data to graph session
def trainGraph(inp, out, sess):

    #to calculate the argmax, we multiply the predicted output with a vector with one value 1 and rest as 0
    argmax = tf.placeholder("float", [None, ACTIONS])
    gt = tf.placeholder("float", [None])  # ground truth

    #action
    action = tf.reduce_sum(tf.multiply(out, argmax), reduction_indices=1)
    #cost function we will reduce through backpropagation
    cost = tf.reduce_mean(tf.square(action - gt))
    #optimisation function to reduce our minimize our cost function
    train_step = tf.train.AdamOptimizer(1e-6).minimize(cost)

    #initialize our game
    game = pong.PongGame()

    #create a queue for experience replay to store policies
    D = deque()

    #initial frame
    frame = game.getPresentFrame()
    #convert rgb to gray scale for processing
    frame = cv2.cvtColor(cv2.resize(frame, (84, 84)), cv2.COLOR_BGR2GRAY)
    #binary colors, black or white
    ret, frame = cv2.threshold(frame, 1, 255, cv2.THRESH_BINARY)
    #stack frames, that is our input tensor
    inp_t = np.stack((frame, frame, frame, frame), axis=2)

    #saver
    saver = tf.train.Saver()

    sess.run(tf.initialize_all_variables())

    t = 0
    epsilon = INITIAL_EPSILON

    #training day
    while(1):
        #output tensor
        out_t = out.eval(feed_dict={inp: [inp_t]})[0]
        #argmax function
        argmax_t = np.zeros([ACTIONS])

        #
        if(random.random() <= epsilon):
            maxIndex = random.randrange(ACTIONS)
        else:
            maxIndex = np.argmax(out_t)
        argmax_t[maxIndex] = 1

        if epsilon > FINAL_EPSILON:
            epsilon -= (INITIAL_EPSILON - FINAL_EPSILON) / EXPLORE

        #reward tensor if score is positive
        reward_t, frame = game.getNextFrame(argmax_t)
        #get frame pixel data
        frame = cv2.cvtColor(cv2.resize(frame, (84, 84)), cv2.COLOR_BGR2GRAY)
        ret, frame = cv2.threshold(frame, 1, 255, cv2.THRESH_BINARY)
        frame = np.reshape(frame, (84, 84, 1))
        #new input tensor
        inp_t1 = np.append(frame, inp_t[:, :, 0:3], axis=2)

        #add our input tensor, argmax tensor, reward and updated input tensor to tack of experiences
        D.append((inp_t, argmax_t, reward_t, inp_t1))

        #if we run out of replay memory, make room
        if len(D) > REPLAY_MEMORY:
            D.popleft()

        #training iteration
        if t > OBSERVE and t % 10 == 0: #t % 10 == 0 speeds up the calculations on slow machines

            #get values from our replay memory
            minibatch = random.sample(D, BATCH)

            inp_batch = [d[0] for d in minibatch]
            argmax_batch = [d[1] for d in minibatch]
            reward_batch = [d[2] for d in minibatch]
            inp_t1_batch = [d[3] for d in minibatch]

            gt_batch = []
            out_batch = out.eval(feed_dict={inp: inp_t1_batch})

            #add values to our batch
            for i in range(0, len(minibatch)):
                gt_batch.append(reward_batch[i] + GAMMA * np.max(out_batch[i]))

            #train on that
            train_step.run(feed_dict={
                           gt: gt_batch,
                           argmax: argmax_batch,
                           inp: inp_batch
                           })

        #update our input tensor the the next frame
        inp_t = inp_t1
        t = t + 1

        #print our where we are after saving where we are
        if t % 10000 == 0:
            saver.save(sess, './' + 'pong' + '-dqn', global_step=t)

        print("TIMESTEP", t, "/ EPSILON", epsilon, "/ ACTION", maxIndex, "/ REWARD", reward_t, "/ Q_MAX %e" % np.max(out_t))


def main():
    #create session
    sess = tf.InteractiveSession()
    #input layer and output layer by creating graph
    inp, out = createGraph()
    #train our graph on input and output with session variables
    trainGraph(inp, out, sess)

if __name__ == "__main__":
    main()