awjuliani

class AC_Network():
    def __init__(self,s_size,a_size,scope,trainer):
        ....
        ....
        ....
        if scope != 'global':
            self.actions = tf.placeholder(shape=[None],dtype=tf.int32)
            self.actions_onehot = tf.one_hot(self.actions,a_size,dtype=tf.float32)
            self.target_v = tf.placeholder(shape=[None],dtype=tf.float32)
            self.advantages = tf.placeholder(shape=[None],dtype=tf.float32)

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class Worker():
      ....
      ....
      ....
      def work(self,max_episode_length,gamma,global_AC,sess,coord):
        episode_count = 0
        total_step_count = 0
        print "Starting worker " + str(self.number)
        with sess.as_default(), sess.graph.as_default():                 
            while not coord.should_stop():

awjuliani

# Copies one set of variables to another.
# Used to set worker network parameters to those of global network.
def update_target_graph(from_scope,to_scope):
    from_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, from_scope)
    to_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, to_scope)

    op_holder = []
    for from_var,to_var in zip(from_vars,to_vars):
        op_holder.append(to_var.assign(from_var))
    return op_holder

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with tf.device("/cpu:0"): 
    master_network = AC_Network(s_size,a_size,'global',None) # Generate global network
    num_workers = multiprocessing.cpu_count() # Set workers ot number of available CPU threads
    workers = []
    # Create worker classes
    for i in range(num_workers):
        workers.append(Worker(DoomGame(),i,s_size,a_size,trainer,saver,model_path))

with tf.Session() as sess:
    coord = tf.train.Coordinator()

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class AC_Network():
    def __init__(self,s_size,a_size,scope,trainer):
        with tf.variable_scope(scope):
            #Input and visual encoding layers
            self.inputs = tf.placeholder(shape=[None,s_size],dtype=tf.float32)
            self.imageIn = tf.reshape(self.inputs,shape=[-1,84,84,1])
            self.conv1 = slim.conv2d(activation_fn=tf.nn.elu,
                inputs=self.imageIn,num_outputs=16,
                kernel_size=[8,8],stride=[4,4],padding='VALID')
            self.conv2 = slim.conv2d(activation_fn=tf.nn.elu,

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awjuliani

import tensorflow as tf
import numpy as np
import tensorflow.contrib.slim as slim

total_layers = 25 #Specify how deep we want our network
units_between_stride = total_layers / 5

def denseBlock(input_layer,i,j):
    with tf.variable_scope("dense_unit"+str(i)):
        nodes = []

awjuliani

import tensorflow as tf
import numpy as np
import tensorflow.contrib.slim as slim

total_layers = 25 #Specify how deep we want our network
units_between_stride = total_layers / 5

def highwayUnit(input_layer,i):
    with tf.variable_scope("highway_unit"+str(i)):
        H = slim.conv2d(input_layer,64,[3,3])

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import tensorflow as tf
import numpy as np
import tensorflow.contrib.slim as slim

total_layers = 25 #Specify how deep we want our network
units_between_stride = total_layers / 5

def resUnit(input_layer,i):
    with tf.variable_scope("res_unit"+str(i)):
        part1 = slim.batch_norm(input_layer,activation_fn=None)