<s> """<STR_LIT>""" <EOL> import os <EOL> import cPickle <EOL> import time <EOL> import logging <EOL> import numpy as np <EOL> import ale_data_set <EOL> import sys <EOL> sys . setrecursionlimit ( <NUM_LIT> ) <EOL> class NeuralAgent ( object ) : <EOL> def __init__ ( self , q_network , epsilon_start , epsilon_min , <EOL> epsilon_decay , replay_memory_size , exp_pref , <EOL> replay_start_size , update_frequency , rng , <EOL> s3_utility ) : <EOL> self . network = q_network <EOL> self . epsilon_start = epsilon_start <EOL> self . epsilon_min = epsilon_min <EOL> self . epsilon_decay = epsilon_decay <EOL> self . replay_memory_size = replay_memory_size <EOL> self . exp_pref = exp_pref <EOL> self . replay_start_size = replay_start_size <EOL> self . update_frequency = update_frequency <EOL> self . rng = rng <EOL> self . s3_utility = s3_utility <EOL> self . phi_length = self . network . num_frames <EOL> self . image_width = self . network . input_width <EOL> self . image_height = self . network . input_height <EOL> time_str = time . strftime ( "<STR_LIT>" , time . gmtime ( ) ) <EOL> self . exp_dir = self . exp_pref + time_str + "<STR_LIT:{}>" . format ( self . network . lr ) . replace ( "<STR_LIT:.>" , "<STR_LIT:p>" ) + "<STR_LIT:_>" + "<STR_LIT:{}>" . format ( self . network . discount ) . replace ( "<STR_LIT:.>" , "<STR_LIT:p>" ) <EOL> self . results_filepath = self . exp_dir + '<STR_LIT>' <EOL> self . learning_filepath = self . exp_dir + '<STR_LIT>' <EOL> try : <EOL> os . stat ( self . exp_dir ) <EOL> except OSError : <EOL> os . makedirs ( self . exp_dir ) <EOL> self . num_actions = self . network . num_actions <EOL> self . data_set = ale_data_set . DataSet ( width = self . image_width , <EOL> height = self . image_height , <EOL> rng = rng , <EOL> max_steps = self . replay_memory_size , <EOL> phi_length = self . phi_length ) <EOL> self . test_data_set = ale_data_set . DataSet ( width = self . image_width , <EOL> height = self . image_height , <EOL> rng = rng , <EOL> max_steps = self . phi_length * <NUM_LIT:2> , <EOL> phi_length = self . phi_length ) <EOL> self . epsilon = self . epsilon_start <EOL> if self . epsilon_decay != <NUM_LIT:0> : <EOL> self . epsilon_rate = ( ( self . epsilon_start - self . epsilon_min ) / <EOL> self . epsilon_decay ) <EOL> else : <EOL> self . epsilon_rate = <NUM_LIT:0> <EOL> self . testing = False <EOL> self . _open_results_file ( ) <EOL> self . _open_learning_file ( ) <EOL> self . episode_counter = <NUM_LIT:0> <EOL> self . batch_counter = <NUM_LIT:0> <EOL> self . holdout_data = None <EOL> self . last_img = None <EOL> self . last_action = None <EOL> def _open_results_file ( self ) : <EOL> self . results_file = open ( self . results_filepath , '<STR_LIT:w>' , <NUM_LIT:0> ) <EOL> self . results_file . write ( '<STR_LIT>' ) <EOL> self . results_file . flush ( ) <EOL> def _open_learning_file ( self ) : <EOL> self . learning_file = open ( self . learning_filepath , '<STR_LIT:w>' , <NUM_LIT:0> ) <EOL> self . learning_file . write ( '<STR_LIT>' ) <EOL> self . learning_file . flush ( ) <EOL> def _update_results_file ( self , epoch , num_episodes , holdout_sum ) : <EOL> out = "<STR_LIT>" . format ( epoch , num_episodes , self . total_reward , <EOL> self . total_reward / float ( num_episodes ) , <EOL> holdout_sum ) <EOL> self . results_file . write ( out ) <EOL> self . results_file . flush ( ) <EOL> if self . s3_utility is not None : <EOL> self . s3_utility . upload_file ( '<STR_LIT>' , self . results_filepath ) <EOL> def _update_learning_file ( self ) : <EOL> out = "<STR_LIT>" . format ( np . mean ( self . loss_averages ) , <EOL> self . epsilon ) <EOL> self . learning_file . write ( out ) <EOL> self . learning_file . flush ( ) <EOL> if self . s3_utility is not None : <EOL> self . s3_utility . upload_file ( '<STR_LIT>' , self . learning_filepath ) <EOL> def start_episode ( self , observation ) : <EOL> """<STR_LIT>""" <EOL> self . step_counter = <NUM_LIT:0> <EOL> self . batch_counter = <NUM_LIT:0> <EOL> self . episode_reward = <NUM_LIT:0> <EOL> self . loss_averages = [ ] <EOL> self . start_time = time . time ( ) <EOL> return_action = self . rng . randint ( <NUM_LIT:0> , self . num_actions ) <EOL> self . last_action = return_action <EOL> self . last_img = observation <EOL> return return_action <EOL> def _show_phis ( self , phi1 , phi2 ) : <EOL> import matplotlib . pyplot as plt <EOL> for p in range ( self . phi_length ) : <EOL> plt . subplot ( <NUM_LIT:2> , self . phi_length , p + <NUM_LIT:1> ) <EOL> plt . imshow ( phi1 [ p , : , : ] , interpolation = '<STR_LIT:none>' , cmap = "<STR_LIT>" ) <EOL> plt . grid ( color = '<STR_LIT:r>' , linestyle = '<STR_LIT:->' , linewidth = <NUM_LIT:1> ) <EOL> for p in range ( self . phi_length ) : <EOL> plt . subplot ( <NUM_LIT:2> , self . phi_length , p + <NUM_LIT:5> ) <EOL> plt . imshow ( phi2 [ p , : , : ] , interpolation = '<STR_LIT:none>' , cmap = "<STR_LIT>" ) <EOL> plt . grid ( color = '<STR_LIT:r>' , linestyle = '<STR_LIT:->' , linewidth = <NUM_LIT:1> ) <EOL> plt . show ( ) <EOL> def step ( self , reward , observation ) : <EOL> """<STR_LIT>""" <EOL> self . step_counter += <NUM_LIT:1> <EOL> if self . testing : <EOL> self . episode_reward += reward <EOL> action = self . _choose_action ( self . test_data_set , <NUM_LIT> , <EOL> observation , np . clip ( reward , - <NUM_LIT:1> , <NUM_LIT:1> ) ) <EOL> else : <EOL> if len ( self . data_set ) > self . replay_start_size : <EOL> self . epsilon = max ( self . epsilon_min , <EOL> self . epsilon - self . epsilon_rate ) <EOL> action = self . _choose_action ( self . data_set , self . epsilon , <EOL> observation , <EOL> np . clip ( reward , - <NUM_LIT:1> , <NUM_LIT:1> ) ) <EOL> if self . step_counter % self . update_frequency == <NUM_LIT:0> : <EOL> loss = self . _do_training ( ) <EOL> self . batch_counter += <NUM_LIT:1> <EOL> self . loss_averages . append ( loss ) <EOL> else : <EOL> action = self . _choose_action ( self . data_set , self . epsilon , <EOL> observation , <EOL> np . clip ( reward , - <NUM_LIT:1> , <NUM_LIT:1> ) ) <EOL> self . last_action = action <EOL> self . last_img = observation <EOL> return action <EOL> def _choose_action ( self , data_set , epsilon , cur_img , reward ) : <EOL> """<STR_LIT>""" <EOL> data_set . add_sample ( self . last_img , self . last_action , reward , False ) <EOL> if self . step_counter >= self . phi_length : <EOL> phi = data_set . phi ( cur_img ) <EOL> action = self . network . choose_action ( phi , epsilon ) <EOL> else : <EOL> action = self . rng . randint ( <NUM_LIT:0> , self . num_actions ) <EOL> return action <EOL> def _do_training ( self ) : <EOL> """<STR_LIT>""" <EOL> states , actions , rewards , next_states , terminals = self . data_set . random_batch ( <EOL> self . network . batch_size ) <EOL> return self . network . train ( states , actions , rewards , <EOL> next_states , terminals ) <EOL> def end_episode ( self , reward , terminal = True ) : <EOL> """<STR_LIT>""" <EOL> self . episode_reward += reward <EOL> self . step_counter += <NUM_LIT:1> <EOL> total_time = time . time ( ) - self . start_time <EOL> if self . testing : <EOL> if terminal or self . episode_counter == <NUM_LIT:0> : <EOL> self . episode_counter += <NUM_LIT:1> <EOL> self . total_reward += self . episode_reward <EOL> else : <EOL> self . data_set . add_sample ( self . last_img , <EOL> self . last_action , <EOL> np . clip ( reward , - <NUM_LIT:1> , <NUM_LIT:1> ) , <EOL> True ) <EOL> logging . info ( "<STR_LIT>" . format ( self . step_counter / total_time ) ) <EOL> if self . batch_counter > <NUM_LIT:0> : <EOL> self . _update_learning_file ( ) <EOL> logging . info ( "<STR_LIT>" . format ( np . mean ( self . loss_averages ) ) ) <EOL> def finish_epoch ( self , epoch ) : <EOL> net_filename = '<STR_LIT>' + str ( epoch ) + '<STR_LIT>' <EOL> net_filepath = self . exp_dir + net_filename <EOL> net_file = open ( net_filepath , '<STR_LIT:w>' ) <EOL> cPickle . dump ( self . network , net_file , - <NUM_LIT:1> ) <EOL> net_file . close ( ) <EOL> if self . s3_utility is not None : <EOL> self . s3_utility . upload_file ( net_filename , net_filepath ) <EOL> def start_testing ( self ) : <EOL> self . testing = True <EOL> self . total_reward = <NUM_LIT:0> <EOL> self . episode_counter = <NUM_LIT:0> <EOL> def finish_testing ( self , epoch ) : <EOL> self . testing = False <EOL> holdout_size = <NUM_LIT> <EOL> if self . holdout_data is None and len ( self . data_set ) > holdout_size : <EOL> self . holdout_data = self . data_set . random_batch ( holdout_size ) [ <NUM_LIT:0> ] <EOL> holdout_sum = <NUM_LIT:0> <EOL> if self . holdout_data is not None : <EOL> for i in range ( holdout_size ) : <EOL> holdout_sum += np . max ( <EOL> self . network . q_vals ( self . holdout_data [ i , ... ] ) ) <EOL> self . _update_results_file ( epoch , self . episode_counter , <EOL> holdout_sum / holdout_size ) <EOL> if __name__ == "<STR_LIT:__main__>" : <EOL> pass </s>
