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critic_network.py
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critic_network.py
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import tensorflow as tf
import numpy as np
import math
LAYER1_SIZE = 400
LAYER2_SIZE = 300
LEARNING_RATE = 1e-3
TAU = 0.001
L2 = 0.01
class CriticNetwork:
"""docstring for CriticNetwork"""
def __init__(self,sess,state_dim,action_dim):
self.time_step = 0
self.sess = sess
# create q network
self.state_input,\
self.action_input,\
self.q_value_output,\
self.net = self.create_q_network(state_dim,action_dim)
# create target q network (the same structure with q network)
self.target_state_input,\
self.target_action_input,\
self.target_q_value_output,\
self.target_update = self.create_target_q_network(state_dim,action_dim,self.net)
self.create_training_method()
# initialization
self.sess.run(tf.initialize_all_variables())
self.update_target()
def create_training_method(self):
# Define training optimizer
self.y_input = tf.placeholder("float",[None,1])
weight_decay = tf.add_n([L2 * tf.nn.l2_loss(var) for var in self.net])
self.cost = tf.reduce_mean(tf.square(self.y_input - self.q_value_output)) + weight_decay
self.optimizer = tf.train.AdamOptimizer(LEARNING_RATE).minimize(self.cost)
self.action_gradients = tf.gradients(self.q_value_output,self.action_input)
def create_q_network(self,state_dim,action_dim):
# the layer size could be changed
layer1_size = LAYER1_SIZE
layer2_size = LAYER2_SIZE
state_input = tf.placeholder("float",[None,state_dim])
action_input = tf.placeholder("float",[None,action_dim])
W1 = self.variable([state_dim,layer1_size],state_dim)
b1 = self.variable([layer1_size],state_dim)
W2 = self.variable([layer1_size,layer2_size],layer1_size+action_dim)
W2_action = self.variable([action_dim,layer2_size],layer1_size+action_dim)
b2 = self.variable([layer2_size],layer1_size+action_dim)
W3 = tf.Variable(tf.random_uniform([layer2_size,1],-3e-3,3e-3))
b3 = tf.Variable(tf.random_uniform([1],-3e-3,3e-3))
layer1 = tf.nn.relu(tf.matmul(state_input,W1) + b1)
layer2 = tf.nn.relu(tf.matmul(layer1,W2) + tf.matmul(action_input,W2_action) + b2)
q_value_output = tf.identity(tf.matmul(layer2,W3) + b3)
return state_input,action_input,q_value_output,[W1,b1,W2,W2_action,b2,W3,b3]
def create_target_q_network(self,state_dim,action_dim,net):
state_input = tf.placeholder("float",[None,state_dim])
action_input = tf.placeholder("float",[None,action_dim])
ema = tf.train.ExponentialMovingAverage(decay=1-TAU)
target_update = ema.apply(net)
target_net = [ema.average(x) for x in net]
layer1 = tf.nn.relu(tf.matmul(state_input,target_net[0]) + target_net[1])
layer2 = tf.nn.relu(tf.matmul(layer1,target_net[2]) + tf.matmul(action_input,target_net[3]) + target_net[4])
q_value_output = tf.identity(tf.matmul(layer2,target_net[5]) + target_net[6])
return state_input,action_input,q_value_output,target_update
def update_target(self):
self.sess.run(self.target_update)
def train(self,y_batch,state_batch,action_batch):
self.time_step += 1
self.sess.run(self.optimizer,feed_dict={
self.y_input:y_batch,
self.state_input:state_batch,
self.action_input:action_batch
})
def gradients(self,state_batch,action_batch):
return self.sess.run(self.action_gradients,feed_dict={
self.state_input:state_batch,
self.action_input:action_batch
})[0]
def target_q(self,state_batch,action_batch):
return self.sess.run(self.target_q_value_output,feed_dict={
self.target_state_input:state_batch,
self.target_action_input:action_batch
})
def q_value(self,state_batch,action_batch):
return self.sess.run(self.q_value_output,feed_dict={
self.state_input:state_batch,
self.action_input:action_batch})
# f fan-in size
def variable(self,shape,f):
return tf.Variable(tf.random_uniform(shape,-1/math.sqrt(f),1/math.sqrt(f)))
'''
def load_network(self):
self.saver = tf.train.Saver()
checkpoint = tf.train.get_checkpoint_state("saved_critic_networks")
if checkpoint and checkpoint.model_checkpoint_path:
self.saver.restore(self.sess, checkpoint.model_checkpoint_path)
print "Successfully loaded:", checkpoint.model_checkpoint_path
else:
print "Could not find old network weights"
def save_network(self,time_step):
print 'save critic-network...',time_step
self.saver.save(self.sess, 'saved_critic_networks/' + 'critic-network', global_step = time_step)
'''