def test_worker(args, shared_model, total_steps, optimizer):
args.environment.clip_rewards = False
env = make_env(args)
log_path = '{}/{}'.format(args.train.experiment_path, 'log.txt')
logging.basicConfig(filename=log_path, level=logging.INFO)
logging.info("STARTED TRAINING PROCESS {}".format(
time.strftime("%Y.%m.%d_%H:%M", time.localtime())))
model = ActorCritic(env.observation_space.shape, env.action_space.n)
model.eval()
start_time = time.time()
reward_history = []
while True:
model.load_state_dict(shared_model.state_dict())
if (len(reward_history) + 1) % args.train.save_frequency == 0:
save_progress(args, model, optimizer, total_steps.value)
total_reward, _ = play_game(model, env)
reward_history.append(total_reward)
log_message = "Time {}, num steps {}, FPS {:.0f}, curr episode reward {}, mean episode reward: {}".format(
time.strftime("%Hh %Mm %Ss",
time.gmtime(time.time() - start_time)),
total_steps.value,
total_steps.value / (time.time() - start_time),
total_reward,
np.mean(reward_history[-60:]),
)
print(log_message)
logging.info(log_message)
time.sleep(60)
def test_worker(args, shared_model, total_steps, optimizer):
args.environment.clip_rewards = False
env = make_env(args.environment)
log_path = '{}/{}'.format(args.train.experiment_folder, 'log.txt')
logging.basicConfig(filename=log_path, level=logging.INFO)
logging.info("STARTED TRAINING PROCESS {}".format(time.strftime("%Y.%m.%d_%H:%M", time.localtime())))
model = ActorCritic(env.observation_space.shape, env.action_space.n)
model = BaseWrapper(model)
if (args.train.use_pixel_control or
args.train.use_reward_prediction):
model = ExperienceWrapper(model)
if args.train.use_pixel_control:
model = PixelControlWrapper(model, args.train.gamma, args.train.pc_coef)
if args.train.use_reward_prediction:
model = RewardPredictionWrapper(model, args.train.rp_coef)
if args.train.use_value_replay:
model = ValueReplayWrapper(model)
model.config = args
model.eval()
start_time = time.time()
reward_history = []
while True:
model.load_state_dict(shared_model.state_dict())
if (len(reward_history) + 1) % args.train.save_frequency == 0:
save_progress(args, model, optimizer, total_steps.value)
stats = play_game(model, env)
reward_history.append(stats['total_reward'])
log_message = (
'Time {}, num steps {}, FPS {:.0f}, '+
'curr episode reward {:.2f}, mean episode reward: {:.2f}, '+
'mean policy loss {:.2f}, mean value loss {:.2f}, '+
'mean entropy percentage {:.2f}'
).format(
time.strftime("%Hh %Mm %Ss", time.gmtime(time.time() - start_time)),
total_steps.value,
total_steps.value / (time.time() - start_time),
stats['total_reward'],
np.mean(reward_history[-60:]),
stats['policy_loss'],
stats['value_loss'],
stats['entropy']
)
if args.train.use_pixel_control:
log_message += ', pixel control loss %.2f' %stats['pc_loss']
if args.train.use_reward_prediction:
log_message += ', reward prediction loss %.2f' %stats['rp_loss']
if args.train.use_value_replay:
log_message += ', value replay loss %.2f' %stats['vr_loss']
print(log_message)
logging.info(log_message)
time.sleep(60)
parser = argparse.ArgumentParser(description='A3C')
parser.add_argument('--env-name',
default='SpaceInvaders-v0',
help='environment to train on (default: SpaceInvaders-v0)')
parser.add_argument('--logs-path',
default="./log.txt",
help='path to logs (default: `./log.txt`)')
parser.add_argument(
'--pretrained-weights',
default=None,
help='path to pretrained weights (default: None – evaluate random model)')
if __name__ == '__main__':
cmd_args = parser.parse_args()
logging.basicConfig(filename=cmd_args.logs_path, level=logging.INFO)
env = make_atari(cmd_args.env_name, clip=False)
model = ActorCritic(env.observation_space.shape[0], env.action_space.n)
if cmd_args.pretrained_weights is not None:
model.load_weights(cmd_args.pretrained_weights)
else:
print(
"You have not specified path to model weigths, random plays will be performed"
)
model.eval()
results = record_video(model, env)
log_message = "evaluated on pretrained weights: {}, results: {}".format(
cmd_args.pretrained_weights, results)
print(log_message)
logging.info(log_message)