# import corresponding configuration , neural networks and envs
if args.case == 'classic_control':
from config.classic_control import run_config
elif args.case == 'box2d':
from config.box2d import run_config
elif args.case == 'mujoco':
from config.mujoco import run_config
elif args.case == 'dm_control':
from config.dm_control import run_config
else:
raise Exception('Invalid --case option.')
# set config as per arguments
run_config.set_config(args)
log_base_path = make_results_dir(run_config.exp_path, args)
if args.use_wandb:
os.makedirs(args.wandb_dir, exist_ok=True)
# set-up logger
init_logger(log_base_path)
logging.getLogger('root').info('cmd args:{}'.format(' '.join(
sys.argv[1:]))) # log command line arguments.
try:
if args.opr == 'train':
if args.use_wandb:
os.makedirs(args.wandb_dir, exist_ok=True)
os.environ['WANDB_DIR'] = str(args.wandb_dir)
import wandb
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# import corresponding configuration , neural networks and envs
if args.case == 'atari':
from config.atari import muzero_config
elif args.case == 'box2d':
from config.classic_control import muzero_config # just using same config as classic_control for now
elif args.case == 'classic_control':
from config.classic_control import muzero_config
else:
raise Exception('Invalid --case option')
# set config as per arguments
exp_path = muzero_config.set_config(args)
exp_path, log_base_path = make_results_dir(exp_path, args)
# set-up logger
init_logger(log_base_path)
try:
if args.opr == 'train':
summary_writer = SummaryWriter(exp_path, flush_secs=10)
train(muzero_config, summary_writer)
elif args.opr == 'test':
assert os.path.exists(muzero_config.model_path), 'model not found at {}'.format(muzero_config.model_path)
model = muzero_config.get_uniform_network().to('cpu')
model.load_state_dict(torch.load(muzero_config.model_path, map_location=torch.device('cpu')))
test_score = test(muzero_config, model, args.test_episodes, device='cpu', render=args.render,
save_video=True)
parser.add_argument(
'--log_suffix',
type=str,
default='',
help=
'Log Suffix Attached to the resulting directory (default: %(default)s)'
)
# Process arguments
args = parser.parse_args()
args.device = 'cuda' if (
not args.no_cuda) and torch.cuda.is_available() else 'cpu'
# create relative paths to store results
exp_path = make_results_dir(
os.path.join(args.result_dir, args.env, 'seed_{}'.format(args.seed)),
args)
# set-up logger and tensorboard
init_logger(os.path.join(exp_path, args.opr + '.log'))
logger = logging.getLogger()
summary_writer = SummaryWriter(exp_path, flush_secs=10)
# seeding random iterators
np.random.seed(args.seed)
torch.manual_seed(args.seed)
try:
# import corresponding configuration , neural networks and envs
if args.case == 'atari':
from config.atari import muzero_config