def get_player(viz=False, train=False):
env = AtariPlayer(ROM_FILE, frame_skip=ACTION_REPEAT, viz=viz,
live_lost_as_eoe=train, max_num_frames=30000)
env = FireResetEnv(env)
env = WarpFrame(env, IMAGE_SIZE)
if not train:
# in training, history is taken care of in expreplay buffer
env = FrameStack(env, FRAME_HISTORY)
return env
def get_player(viz=False, train=False):
pl = AtariPlayer(ROM_FILE, frame_skip=ACTION_REPEAT,
image_shape=IMAGE_SIZE[::-1], viz=viz, live_lost_as_eoe=train)
if not train:
pl = MapPlayerState(pl, lambda im: im[:, :, np.newaxis])
pl = HistoryFramePlayer(pl, FRAME_HISTORY)
pl = PreventStuckPlayer(pl, 30, 1)
pl = LimitLengthPlayer(pl, 30000)
return pl
def get_player(rom, viz=False, train=False):
env = AtariPlayer(rom,
frame_skip=ACTION_REPEAT,
viz=viz,
live_lost_as_eoe=train,
max_num_frames=60000)
env = FireResetEnv(env)
env = MapState(env, lambda im: cv2.resize(im, IMAGE_SIZE))
if not train:
# in training, context is taken care of in expreplay buffer
env = FrameStack(env, CONTEXT_LEN)
return env
def get_player(viz=False, train=False):
env = AtariPlayer(ROM_FILE,
frame_skip=ACTION_REPEAT,
viz=viz,
live_lost_as_eoe=train,
max_num_frames=60000)
env = FireResetEnv(env)
env = MapState(env,
lambda im: cv2.resize(im, IMAGE_SIZE)[:, :, np.newaxis])
if not train:
# in training, history is taken care of in expreplay buffer
env = FrameStack(env, FRAME_HISTORY)
return env
def get_player(viz=False, train=False):
if USE_GYM:
env = gym.make(ENV_NAME)
else:
from atari import AtariPlayer
env = AtariPlayer(ENV_NAME, frame_skip=4, viz=viz,
live_lost_as_eoe=train, max_num_frames=60000)
env = FireResetEnv(env)
env = MapState(env, lambda im: resize_keepdims(im, IMAGE_SIZE))
if not train:
# in training, history is taken care of in expreplay buffer
env = FrameStack(env, FRAME_HISTORY)
if train and USE_GYM:
env = LimitLength(env, 60000)
return env
def get_player(viz=False, train=False):
pl = AtariPlayer(ROM_FILE, frame_skip=ACTION_REPEAT,
image_shape=IMAGE_SIZE[::-1], viz=viz, live_lost_as_eoe=train)
global NUM_ACTIONS
NUM_ACTIONS = pl.get_action_space().num_actions()
if not train:
pl = HistoryFramePlayer(pl, FRAME_HISTORY)
pl = PreventStuckPlayer(pl, 30, 1)
pl = LimitLengthPlayer(pl, 30000)
return pl
def get_player(rom,
image_size,
viz=False,
train=False,
frame_skip=1,
context_len=1):
env = AtariPlayer(rom,
frame_skip=frame_skip,
viz=viz,
live_lost_as_eoe=train,
max_num_frames=60000)
env = FireResetEnv(env)
env = MapState(env, lambda im: cv2.resize(im, image_size))
if not train:
# in training, context is taken care of in expreplay buffer
env = FrameStack(env, context_len)
return env
def get_player(train=False, dumpdir=None):
use_gym = not ENV_NAME.endswith(".bin")
if use_gym:
env = gym.make(ENV_NAME)
else:
from atari import AtariPlayer
env = AtariPlayer(ENV_NAME,
frame_skip=4,
viz=False,
live_lost_as_eoe=train,
max_num_frames=60000,
grayscale=False)
if dumpdir:
env = gym.wrappers.Monitor(env, dumpdir, video_callable=lambda _: True)
env = FireResetEnv(env)
env = MapState(env, lambda im: cv2.resize(im, IMAGE_SIZE))
env = FrameStack(env, 4)
if train and use_gym:
env = LimitLength(env, 60000)
return env
parser.add_argument('--rom', help='atari rom', required=True)
parser.add_argument('--algo',
help='algorithm',
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:
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--load', help='load model')
parser.add_argument('--task', help='task to perform',
choices=['play', 'eval', 'train'], default='train')
parser.add_argument('--rom', help='atari rom', required=True)
parser.add_argument('--algo', help='algorithm',
choices=['DQN', 'Double', 'Dueling'], default='Double')
args = parser.parse_args()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
ROM_FILE = args.rom
METHOD = args.algo
# set num_actions
NUM_ACTIONS = AtariPlayer(ROM_FILE).action_space.n
logger.info("ROM: {}, Num Actions: {}".format(ROM_FILE, NUM_ACTIONS))
if args.task != 'train':
assert args.load is not None
pred = OfflinePredictor(PredictConfig(
model=Model(),
session_init=get_model_loader(args.load),
input_names=['state'],
output_names=['Qvalue']))
if args.task == 'play':
play_n_episodes(get_player(viz=0.01), pred, 100)
elif args.task == 'eval':
eval_model_multithread(pred, EVAL_EPISODE, get_player)
else:
logger.set_logger_dir(
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--load', help='load model')
parser.add_argument('--task', help='task to perform',
choices=['play', 'eval', 'train'], default='train')
parser.add_argument('--rom', help='atari rom', required=True)
parser.add_argument('--algo', help='algorithm',
choices=['DQN', 'Double', 'Dueling'], default='Double')
args = parser.parse_args()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
ROM_FILE = args.rom
METHOD = args.algo
# set num_actions
NUM_ACTIONS = AtariPlayer(ROM_FILE).get_action_space().num_actions()
logger.info("ROM: {}, Num Actions: {}".format(ROM_FILE, NUM_ACTIONS))
if args.task != 'train':
assert args.load is not None
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:
logger.set_logger_dir(