def run_environment(h_size, middle_size, lstm_layers, learning_starts,
learning_freq, target_update_freq, lr, gamma, batch_size,
replay_buffer_size, epsilon_decay_steps, final_epsilon,
root_dir, num):
log_dir = os.path.join(root_dir, "{:03}".format(num))
os.makedirs(log_dir)
agent = DQN_Agent(h_size,
middle_size,
lstm_layers,
learning_starts,
learning_freq,
target_update_freq,
lr,
gamma,
batch_size,
replay_buffer_size,
epsilon_decay_steps,
final_epsilon,
verbose=True,
log_dir=log_dir)
Reward_func = getattr(reward, training['reward_func'])
agent.train(Reward_func, training['reward_settings'], training['episodes'],
training['targets'], training['reg_inits'])
agent.save("best", best=True)
performance = agent.global_performance()
best_performance, best_episode = agent.best_performance()
return performance + (1 / (1 + best_episode))
def main():
args = parse_arguments()
agent = DQN_Agent(args, memory_size=args.memory_size, burn_in=args.burn_in)
if args.train == 1:
if not os.path.exists(args.folder_prefix):
os.makedirs(args.folder_prefix)
sys.stdout = Logger(args.folder_prefix + args.logfile)
print_user_flags(args)
PolicyModel = args.folder_prefix + 'PolicyModel/'
TargetModel = args.folder_prefix + 'TargetModel/'
RewardsCSV = args.folder_prefix + 'RewardsCSV/'
if not os.path.exists(PolicyModel):
os.makedirs(PolicyModel)
elif args.reset_dir:
shutil.rmtree(PolicyModel, ignore_errors=True)
os.makedirs(PolicyModel)
if not os.path.exists(TargetModel):
os.makedirs(TargetModel)
elif args.reset_dir:
shutil.rmtree(TargetModel, ignore_errors=True)
os.makedirs(TargetModel)
if not os.path.exists(RewardsCSV):
os.makedirs(RewardsCSV)
elif args.reset_dir:
shutil.rmtree(RewardsCSV, ignore_errors=True)
os.makedirs(RewardsCSV)
agent.train()
else:
agent.test(test_epi=args.test_epi,
model_file=args.weight_file,
lookahead=agent.greedy_policy)
agent.agent_close()