<s> import tensorflow as tf <EOL> import numpy as np <EOL> class GameACNetwork ( object ) : <EOL> def __init__ ( self , action_size ) : <EOL> with tf . device ( "<STR_LIT>" ) : <EOL> self . _action_size = action_size <EOL> self . W_conv1 = self . _weight_variable ( [ <NUM_LIT:8> , <NUM_LIT:8> , <NUM_LIT:4> , <NUM_LIT:16> ] ) <EOL> self . b_conv1 = self . _bias_variable ( [ <NUM_LIT:16> ] ) <EOL> self . W_conv2 = self . _weight_variable ( [ <NUM_LIT:4> , <NUM_LIT:4> , <NUM_LIT:16> , <NUM_LIT:32> ] ) <EOL> self . b_conv2 = self . _bias_variable ( [ <NUM_LIT:32> ] ) <EOL> self . W_fc1 = self . _weight_variable ( [ <NUM_LIT> , <NUM_LIT> ] ) <EOL> self . b_fc1 = self . _bias_variable ( [ <NUM_LIT> ] ) <EOL> self . W_fc2 = self . _weight_variable ( [ <NUM_LIT> , action_size ] ) <EOL> self . b_fc2 = self . _bias_variable ( [ action_size ] ) <EOL> self . W_fc3 = self . _weight_variable ( [ <NUM_LIT> , <NUM_LIT:1> ] ) <EOL> self . b_fc3 = self . _bias_variable ( [ <NUM_LIT:1> ] ) <EOL> self . s = tf . placeholder ( "<STR_LIT:float>" , [ <NUM_LIT:1> , <NUM_LIT> , <NUM_LIT> , <NUM_LIT:4> ] ) <EOL> h_conv1 = tf . nn . relu ( self . _conv2d ( self . s , self . W_conv1 , <NUM_LIT:4> ) + self . b_conv1 ) <EOL> h_conv2 = tf . nn . relu ( self . _conv2d ( h_conv1 , self . W_conv2 , <NUM_LIT:2> ) + self . b_conv2 ) <EOL> h_conv2_flat = tf . reshape ( h_conv2 , [ <NUM_LIT:1> , <NUM_LIT> ] ) <EOL> h_fc1 = tf . nn . relu ( tf . matmul ( h_conv2_flat , self . W_fc1 ) + self . b_fc1 ) <EOL> self . pi = tf . nn . softmax ( tf . matmul ( h_fc1 , self . W_fc2 ) + self . b_fc2 ) <EOL> self . v = tf . matmul ( h_fc1 , self . W_fc3 ) + self . b_fc3 <EOL> def prepare_loss ( self , entropy_beta ) : <EOL> with tf . device ( "<STR_LIT>" ) : <EOL> self . a = tf . placeholder ( "<STR_LIT:float>" , [ <NUM_LIT:1> , self . _action_size ] ) <EOL> self . td = tf . placeholder ( "<STR_LIT:float>" , [ <NUM_LIT:1> ] ) <EOL> entropy = - tf . reduce_sum ( self . pi * tf . log ( self . pi ) ) <EOL> self . policy_loss = - ( tf . reduce_sum ( tf . mul ( tf . log ( self . pi ) , self . a ) ) * self . td + entropy * entropy_beta ) <EOL> self . r = tf . placeholder ( "<STR_LIT:float>" , [ <NUM_LIT:1> ] ) <EOL> self . value_loss = tf . reduce_mean ( tf . square ( self . r - self . v ) ) <EOL> def run_policy ( self , sess , s_t ) : <EOL> pi_out = sess . run ( self . pi , feed_dict = { self . s : [ s_t ] } ) <EOL> return pi_out [ <NUM_LIT:0> ] <EOL> def run_value ( self , sess , s_t ) : <EOL> v_out = sess . run ( self . v , feed_dict = { self . s : [ s_t ] } ) <EOL> return v_out [ <NUM_LIT:0> ] [ <NUM_LIT:0> ] <EOL> def get_policy_vars ( self ) : <EOL> return [ self . W_conv1 , self . b_conv1 , <EOL> self . W_conv2 , self . b_conv2 , <EOL> self . W_fc1 , self . b_fc1 , <EOL> self . W_fc2 , self . b_fc2 ] <EOL> def get_value_vars ( self ) : <EOL> return [ self . W_conv1 , self . b_conv1 , <EOL> self . W_conv2 , self . b_conv2 , <EOL> self . W_fc1 , self . b_fc1 , <EOL> self . W_fc3 , self . b_fc3 ] <EOL> def sync_from ( self , src_netowrk , name = None ) : <EOL> src_policy_vars = src_netowrk . get_policy_vars ( ) <EOL> src_value_vars = src_netowrk . get_value_vars ( ) <EOL> dst_policy_vars = self . get_policy_vars ( ) <EOL> dst_value_vars = self . get_value_vars ( ) <EOL> sync_ops = [ ] <EOL> with tf . device ( "<STR_LIT>" ) : <EOL> with tf . op_scope ( [ ] , name , "<STR_LIT>" ) as name : <EOL> for ( src_policy_var , dst_policy_var ) in zip ( src_policy_vars , dst_policy_vars ) : <EOL> sync_op = tf . assign ( dst_policy_var , src_policy_var ) <EOL> sync_ops . append ( sync_op ) <EOL> for ( src_value_var , dst_value_var ) in zip ( src_value_vars , dst_value_vars ) : <EOL> sync_op = tf . assign ( dst_value_var , src_value_var ) <EOL> sync_ops . append ( sync_op ) <EOL> return tf . group ( * sync_ops , name = name ) <EOL> def _weight_variable ( self , shape ) : <EOL> initial = tf . truncated_normal ( shape , stddev = <NUM_LIT> ) <EOL> return tf . Variable ( initial ) <EOL> def _bias_variable ( self , shape ) : <EOL> initial = tf . constant ( <NUM_LIT:0.0> , shape = shape ) <EOL> return tf . Variable ( initial ) <EOL> def _conv2d ( self , x , W , stride ) : <EOL> return tf . nn . conv2d ( x , W , strides = [ <NUM_LIT:1> , stride , stride , <NUM_LIT:1> ] , padding = "<STR_LIT>" ) <EOL> def _save_sub ( self , sess , prefix , var , name ) : <EOL> var_val = var . eval ( sess ) <EOL> var_val = np . reshape ( var_val , ( <NUM_LIT:1> , np . product ( var_val . shape ) ) ) <EOL> np . savetxt ( '<STR_LIT>' + prefix + '<STR_LIT:_>' + name + '<STR_LIT>' , var_val , delimiter = '<STR_LIT:U+002C>' ) <EOL> def save ( self , sess , prefix ) : <EOL> self . _save_sub ( sess , prefix , self . W_conv1 , "<STR_LIT>" ) <EOL> self . _save_sub ( sess , prefix , self . b_conv1 , "<STR_LIT>" ) <EOL> self . _save_sub ( sess , prefix , self . W_conv2 , "<STR_LIT>" ) <EOL> self . _save_sub ( sess , prefix , self . b_conv2 , "<STR_LIT>" ) <EOL> self . _save_sub ( sess , prefix , self . W_fc1 , "<STR_LIT>" ) <EOL> self . _save_sub ( sess , prefix , self . b_fc1 , "<STR_LIT>" ) <EOL> self . _save_sub ( sess , prefix , self . W_fc2 , "<STR_LIT>" ) <EOL> self . _save_sub ( sess , prefix , self . b_fc2 , "<STR_LIT>" ) <EOL> self . _save_sub ( sess , prefix , self . W_fc3 , "<STR_LIT>" ) <EOL> self . _save_sub ( sess , prefix , self . b_fc3 , "<STR_LIT>" ) </s>
