Qs.extend(logger.load_Qs())
if has_entropy:
Es.extend(logger.load_policy_entropies())
stats = logger.load_stats()
if stats['best_J'] > best_J:
best_stats = stats
best_agent = logger.load_best_agent()
if skip_cnt > 0: print('NUMBER OF FAILED RUNS:', '{}/{}'.format(skip_cnt, len(run_dirs)))
print('Name:', res_id)
print('Directory:', res_dir)
logger = BenchmarkLogger(log_dir=res_dir, log_id=res_id, use_timestamp=False)
logger.save_Js(Js)
logger.save_Rs(Rs)
logger.save_Qs(Qs)
if has_entropy:
logger.save_policy_entropies(Es)
logger.save_stats(best_stats)
logger.save_best_agent(best_agent)
visualizer = BenchmarkVisualizer(logger)
visualizer.save_report()
if __name__ == '__main__':
res_dir, res_id = read_arguments_aggregate()
run(res_dir, res_id)
os.path.join(log_dir, 'agent_builder.pkl'))
env_builder = BenchmarkLogger._load_pickle(
os.path.join(log_dir, 'environment_builder.pkl'))
logger = BenchmarkLogger(log_dir=log_dir,
log_id=log_id,
use_timestamp=False)
logger.info('Starting experiment.')
result = exec_run(agent_builder, env_builder, **run_args)
logger.info('Saving result.')
cmp_E = agent_builder.compute_policy_entropy
logger.save_Js([result['Js']])
logger.save_Rs([result['Rs']])
logger.save_Qs([result['Qs']])
if cmp_E:
logger.save_policy_entropies([result['Es']])
new_score = result['score']
new_agent = result['builders']
stats = dict(best_J=new_score[0], best_R=new_score[1], best_Q=new_score[2])
if cmp_E:
stats.update(dict(best_E=new_score[3]))
logger.save_stats(stats=stats)
logger.save_best_agent(new_agent)
logger.info('Finished execution.')