<s> import tensorflow as tf <EOL> import numpy as np <EOL> import random <EOL> from game_state import GameState <EOL> from game_ac_network import GameACNetwork <EOL> from a3c_training_thread import A3CTrainingThread <EOL> from rmsprop_applier import RMSPropApplier <EOL> from constants import ACTION_SIZE <EOL> from constants import PARALLEL_SIZE <EOL> from constants import CHECKPOINT_DIR <EOL> from constants import RMSP_EPSILON <EOL> def choose_action ( pi_values ) : <EOL> values = [ ] <EOL> sum = <NUM_LIT:0.0> <EOL> for rate in pi_values : <EOL> sum = sum + rate <EOL> value = sum <EOL> values . append ( value ) <EOL> r = random . random ( ) * sum <EOL> for i in range ( len ( values ) ) : <EOL> if values [ i ] >= r : <EOL> return i ; <EOL> return len ( values ) - <NUM_LIT:1> <EOL> global_network = GameACNetwork ( ACTION_SIZE ) <EOL> learning_rate_input = tf . placeholder ( "<STR_LIT:float>" ) <EOL> policy_applier = RMSPropApplier ( learning_rate = learning_rate_input , <EOL> decay = <NUM_LIT> , <EOL> momentum = <NUM_LIT:0.0> , <EOL> epsilon = RMSP_EPSILON ) <EOL> value_applier = RMSPropApplier ( learning_rate = learning_rate_input , <EOL> decay = <NUM_LIT> , <EOL> momentum = <NUM_LIT:0.0> , <EOL> epsilon = RMSP_EPSILON ) <EOL> training_threads = [ ] <EOL> for i in range ( PARALLEL_SIZE ) : <EOL> training_thread = A3CTrainingThread ( i , global_network , <NUM_LIT:1.0> , <EOL> learning_rate_input , <EOL> policy_applier , value_applier , <EOL> <NUM_LIT> ) <EOL> training_threads . append ( training_thread ) <EOL> sess = tf . Session ( ) <EOL> init = tf . initialize_all_variables ( ) <EOL> sess . run ( init ) <EOL> saver = tf . train . Saver ( ) <EOL> checkpoint = tf . train . get_checkpoint_state ( CHECKPOINT_DIR ) <EOL> if checkpoint and checkpoint . model_checkpoint_path : <EOL> saver . restore ( sess , checkpoint . model_checkpoint_path ) <EOL> print "<STR_LIT>" , checkpoint . model_checkpoint_path <EOL> else : <EOL> print "<STR_LIT>" <EOL> game_state = GameState ( <NUM_LIT:0> , display = True ) <EOL> while True : <EOL> pi_values = global_network . run_policy ( sess , game_state . s_t ) <EOL> action = choose_action ( pi_values ) <EOL> game_state . process ( action ) <EOL> game_state . update ( ) </s>
