logger.log_tabular('Time', time.time() - start_time)
logger.dump_tabular()
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--env', type=str, default='HalfCheetah-v2')
parser.add_argument('--hid', type=int, default=64)
parser.add_argument('--l', type=int, default=2)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--seed', '-s', type=int, default=0)
parser.add_argument('--cpu', type=int, default=4)
parser.add_argument('--steps', type=int, default=4000)
parser.add_argument('--epochs', type=int, default=50)
parser.add_argument('--exp_name', type=str, default='vpg')
args = parser.parse_args()
mpi_fork(args.cpu) # run parallel code with mpi
from spinup.utils.run_utils import setup_logger_kwargs
logger_kwargs = setup_logger_kwargs(args.exp_name, args.seed)
vpg(lambda: gym.make(args.env),
actor_critic=core.MLPActorCritic,
ac_kwargs=dict(hidden_sizes=[args.hid] * args.l),
gamma=args.gamma,
seed=args.seed,
steps_per_epoch=args.steps,
epochs=args.epochs,
logger_kwargs=logger_kwargs)
if __name__ == '__main__':
parser = get_arg_parser()
parser.add_argument('--env', type=str, default='CartPole-v1')
parser.add_argument('--exp_name', type=str, default=None)
parser.add_argument('--allow_run_as_root', action='store_true')
parser.add_argument('--hidden_size', type=int, default=256)
parser.add_argument('--num_hidden', type=int, default=2)
parser.add_argument('--continue_training', '-c', action='store_true')
parser.add_argument('--saved_model_file', '-f', type=str, default=None)
args = parser.parse_args()
if args.cpu > 1:
mpi_tools.mpi_fork(args.cpu, allow_run_as_root=args.allow_run_as_root
) # run parallel code with mpi
# Setup experiment name
env = gym.make(args.env)
from spinup.utils.run_utils import setup_logger_kwargs
experiment_name = args.exp_name or env.spec.id
logger_kwargs = setup_logger_kwargs(experiment_name, args.seed)
# Load or create model
saved_model_file = None
if args.saved_model_file:
saved_model_file = pathlib.Path(args.saved_model_file)
elif args.continue_training:
save_dir = pathlib.Path(logger_kwargs['output_dir'], 'pyt_save')
