choices=['DQN', 'Double', 'Dueling'],
default='Double')
args = parser.parse_args()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.task != 'train':
assert args.load is not None
ROM_FILE = args.rom
METHOD = args.algo
# set num_actions
pl = AtariPlayer(ROM_FILE, viz=False)
NUM_ACTIONS = pl.get_action_space().num_actions()
del pl
if args.task != 'train':
cfg = PredictConfig(model=Model(),
session_init=get_model_loader(args.load),
input_names=['state'],
output_names=['Qvalue'])
if args.task == 'play':
play_model(cfg, get_player(viz=0.01))
elif args.task == 'eval':
eval_model_multithread(cfg, EVAL_EPISODE, get_player)
else:
config = get_config()
if args.load:
config.session_init = SaverRestore(args.load)
QueueInputTrainer(config).train()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.task != 'train':
assert args.load is not None
if args.task != 'train':
cfg = PredictConfig(
model=Model(),
session_init=SaverRestore(args.load),
input_var_names=['state'],
output_var_names=['logits:0'])
if args.task == 'play':
play_model(cfg)
elif args.task == 'eval':
eval_model_multithread(cfg, EVAL_EPISODE)
else:
nr_gpu = get_nr_gpu()
if nr_gpu > 1:
predict_tower = range(nr_gpu)[-nr_gpu/2:]
else:
predict_tower = [0]
PREDICTOR_THREAD = len(predict_tower) * PREDICTOR_THREAD_PER_GPU
config = get_config()
if args.load:
config.session_init = SaverRestore(args.load)
config.tower = range(nr_gpu)[:-nr_gpu/2] or [0]
logger.info("[BA3C] Train on gpu {} and infer on gpu {}".format(
','.join(map(str, config.tower)), ','.join(map(str, predict_tower))))
AsyncMultiGPUTrainer(config, predict_tower=predict_tower).train()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.task != 'train':
assert args.load is not None
if args.task != 'train':
cfg = PredictConfig(
model=Model(),
session_init=SaverRestore(args.load),
input_names=['state'],
output_names=['policy'])
if args.task == 'play':
play_model(cfg)
elif args.task == 'eval':
eval_model_multithread(cfg, args.episode)
elif args.task == 'gen_submit':
run_submission(cfg, args.output, args.episode)
else:
nr_gpu = get_nr_gpu()
if nr_gpu > 0:
if nr_gpu > 1:
predict_tower = list(range(nr_gpu))[-nr_gpu // 2:]
else:
predict_tower = [0]
PREDICTOR_THREAD = len(predict_tower) * PREDICTOR_THREAD_PER_GPU
train_tower = list(range(nr_gpu))[:-nr_gpu // 2] or [0]
logger.info("[BA3C] Train on gpu {} and infer on gpu {}".format(
','.join(map(str, train_tower)), ','.join(map(str, predict_tower))))
trainer = AsyncMultiGPUTrainer
else:
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.task != 'train':
assert args.load is not None
if args.task != 'train':
cfg = PredictConfig(
model=Model(),
session_init=SaverRestore(args.load),
input_names=['state'],
output_names=['policy'])
if args.task == 'play':
play_model(cfg, get_player(viz=0.01))
elif args.task == 'eval':
eval_model_multithread(cfg, args.episode, get_player)
elif args.task == 'gen_submit':
play_n_episodes(
get_player(train=False, dumpdir=args.output),
OfflinePredictor(cfg), args.episode)
# gym.upload(output, api_key='xxx')
else:
nr_gpu = get_nr_gpu()
if nr_gpu > 0:
if nr_gpu > 1:
predict_tower = list(range(nr_gpu))[-nr_gpu // 2:]
else:
predict_tower = [0]
PREDICTOR_THREAD = len(predict_tower) * PREDICTOR_THREAD_PER_GPU
train_tower = list(range(nr_gpu))[:-nr_gpu // 2] or [0]
logger.info("[BA3C] Train on gpu {} and infer on gpu {}".format(
ENV_NAME = args.env
USE_GYM = not ENV_NAME.endswith('.bin')
# set num_actions
num_actions = get_player().action_space.n
logger.info("ENV: {}, Num Actions: {}".format(args.env, num_actions))
state_shape = IMAGE_SIZE + (3, ) if USE_GYM else IMAGE_SIZE
model = Model(state_shape, FRAME_HISTORY, args.algo, num_actions)
if args.task != 'train':
assert args.load is not None
pred = OfflinePredictor(
PredictConfig(model=model,
session_init=SmartInit(args.load),
input_names=['state'],
output_names=['Qvalue']))
if args.task == 'play':
play_n_episodes(get_player(viz=0.01), pred, 100, render=True)
elif args.task == 'eval':
eval_model_multithread(pred, args.num_eval, get_player)
else:
logger.set_logger_dir(
os.path.join(
'train_log',
'DQN-{}'.format(os.path.basename(args.env).split('.')[0])))
config = get_config(model)
config.session_init = SmartInit(args.load)
launch_train_with_config(config, SimpleTrainer())