<s> """<STR_LIT>""" <EOL> import sys <EOL> import logging <EOL> import numpy as np <EOL> from joblib import hash , dump , load <EOL> import os <EOL> from deer . default_parser import process_args <EOL> from deer . agent_ale import ALEAgent <EOL> from deer . q_networks . q_net_theano import MyQNetwork <EOL> from ALE_env import MyEnv as ALE_env <EOL> import deer . experiment . base_controllers as bc <EOL> class Defaults : <EOL> STEPS_PER_EPOCH = <NUM_LIT> <EOL> EPOCHS = <NUM_LIT> <EOL> STEPS_PER_TEST = <NUM_LIT> <EOL> PERIOD_BTW_SUMMARY_PERFS = <NUM_LIT:1> <EOL> FRAME_SKIP = <NUM_LIT:4> <EOL> UPDATE_RULE = '<STR_LIT>' <EOL> BATCH_ACCUMULATOR = '<STR_LIT>' <EOL> LEARNING_RATE = <NUM_LIT> <EOL> LEARNING_RATE_DECAY = <NUM_LIT> <EOL> DISCOUNT = <NUM_LIT> <EOL> DISCOUNT_INC = <NUM_LIT> <EOL> DISCOUNT_MAX = <NUM_LIT> <EOL> RMS_DECAY = <NUM_LIT> <EOL> RMS_EPSILON = <NUM_LIT> <EOL> MOMENTUM = <NUM_LIT:0> <EOL> CLIP_DELTA = <NUM_LIT:1.0> <EOL> EPSILON_START = <NUM_LIT:1.0> <EOL> EPSILON_MIN = <NUM_LIT> <EOL> EPSILON_DECAY = <NUM_LIT> <EOL> UPDATE_FREQUENCY = <NUM_LIT:1> <EOL> REPLAY_MEMORY_SIZE = <NUM_LIT> <EOL> BATCH_SIZE = <NUM_LIT:32> <EOL> NETWORK_TYPE = "<STR_LIT>" <EOL> FREEZE_INTERVAL = <NUM_LIT> <EOL> DETERMINISTIC = True <EOL> if __name__ == "<STR_LIT:__main__>" : <EOL> logging . basicConfig ( level = logging . INFO ) <EOL> parameters = process_args ( sys . argv [ <NUM_LIT:1> : ] , Defaults ) <EOL> if parameters . deterministic : <EOL> rng = np . random . RandomState ( <NUM_LIT> ) <EOL> else : <EOL> rng = np . random . RandomState ( ) <EOL> env = ALE_env ( rng , frame_skip = parameters . frame_skip , <EOL> ale_options = [ { "<STR_LIT:key>" : "<STR_LIT>" , "<STR_LIT:value>" : rng . randint ( <NUM_LIT> ) } , <EOL> { "<STR_LIT:key>" : "<STR_LIT>" , "<STR_LIT:value>" : True } , <EOL> { "<STR_LIT:key>" : "<STR_LIT>" , "<STR_LIT:value>" : <NUM_LIT:0.> } ] ) <EOL> qnetwork = MyQNetwork ( <EOL> env , <EOL> parameters . rms_decay , <EOL> parameters . rms_epsilon , <EOL> parameters . momentum , <EOL> parameters . clip_delta , <EOL> parameters . freeze_interval , <EOL> parameters . batch_size , <EOL> parameters . network_type , <EOL> parameters . update_rule , <EOL> parameters . batch_accumulator , <EOL> rng ) <EOL> agent = ALEAgent ( <EOL> env , <EOL> qnetwork , <EOL> parameters . replay_memory_size , <EOL> max ( env . inputDimensions ( ) [ i ] [ <NUM_LIT:0> ] for i in range ( len ( env . inputDimensions ( ) ) ) ) , <EOL> parameters . batch_size , <EOL> rng ) <EOL> h = hash ( vars ( parameters ) , hash_name = "<STR_LIT>" ) <EOL> fname = "<STR_LIT>" + h <EOL> print ( "<STR_LIT>" . format ( h ) ) <EOL> print ( "<STR_LIT>" . format ( parameters ) ) <EOL> agent . attach ( bc . VerboseController ( <EOL> evaluateOn = '<STR_LIT>' , <EOL> periodicity = <NUM_LIT:1> ) ) <EOL> agent . attach ( bc . TrainerController ( <EOL> evaluateOn = '<STR_LIT:action>' , <EOL> periodicity = parameters . update_frequency , <EOL> showEpisodeAvgVValue = True , <EOL> showAvgBellmanResidual = True ) ) <EOL> agent . attach ( bc . LearningRateController ( <EOL> initialLearningRate = parameters . learning_rate , <EOL> learningRateDecay = parameters . learning_rate_decay , <EOL> periodicity = <NUM_LIT:1> ) ) <EOL> agent . attach ( bc . DiscountFactorController ( <EOL> initialDiscountFactor = parameters . discount , <EOL> discountFactorGrowth = parameters . discount_inc , <EOL> discountFactorMax = parameters . discount_max , <EOL> periodicity = <NUM_LIT:1> ) ) <EOL> agent . attach ( bc . EpsilonController ( <EOL> initialE = parameters . epsilon_start , <EOL> eDecays = parameters . epsilon_decay , <EOL> eMin = parameters . epsilon_min , <EOL> evaluateOn = '<STR_LIT:action>' , <EOL> periodicity = <NUM_LIT:1> , <EOL> resetEvery = '<STR_LIT:none>' ) ) <EOL> agent . attach ( bc . FindBestController ( <EOL> validationID = ALE_env . VALIDATION_MODE , <EOL> testID = None , <EOL> unique_fname = fname ) ) <EOL> agent . attach ( bc . InterleavedTestEpochController ( <EOL> id = ALE_env . VALIDATION_MODE , <EOL> epochLength = parameters . steps_per_test , <EOL> controllersToDisable = [ <NUM_LIT:0> , <NUM_LIT:1> , <NUM_LIT:2> , <NUM_LIT:3> , <NUM_LIT:4> ] , <EOL> periodicity = <NUM_LIT:2> , <EOL> showScore = True , <EOL> summarizeEvery = <NUM_LIT:1> ) ) <EOL> try : <EOL> os . mkdir ( "<STR_LIT>" ) <EOL> except Exception : <EOL> pass <EOL> dump ( vars ( parameters ) , "<STR_LIT>" + fname + "<STR_LIT>" ) <EOL> agent . run ( parameters . epochs , parameters . steps_per_epoch ) <EOL> basename = "<STR_LIT>" + fname <EOL> scores = load ( basename + "<STR_LIT>" ) <EOL> plt . plot ( range ( <NUM_LIT:1> , len ( scores [ '<STR_LIT>' ] ) + <NUM_LIT:1> ) , scores [ '<STR_LIT>' ] , label = "<STR_LIT>" , color = '<STR_LIT:b>' ) <EOL> plt . legend ( ) <EOL> plt . xlabel ( "<STR_LIT>" ) <EOL> plt . ylabel ( "<STR_LIT>" ) <EOL> plt . savefig ( basename + "<STR_LIT>" ) <EOL> plt . show ( ) </s>
