def main(args):
arg_parser = common_arg_parser()
args, unknown_args = arg_parser.parse_known_args(args)
extra_args = parse_cmdline_kwargs(unknown_args)
print(args)
#args.env="MountainCarContinuous-v0"
train_copos(args)
def main():
arg_parser = common_arg_parser()
args, unknown_args = arg_parser.parse_known_args()
extra_args = {
k: run.parse(v)
for k, v in parse_unknown_args(unknown_args).items()
}
train(args, extra_args)
def main():
# configure logger, disable logging in child MPI processes (with rank > 0)
arg_parser = common_arg_parser()
args, unknown_args = arg_parser.parse_known_args()
extra_args = parse_cmdline_kwargs(unknown_args)
if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
rank = 0
logger.configure()
else:
logger.configure(format_strs=[])
rank = MPI.COMM_WORLD.Get_rank()
model, env = train(args, extra_args)
env.close()
if args.save_path is not None and rank == 0:
save_path = osp.expanduser(args.save_path)
model.save(save_path)
if args.play:
logger.log("Running trained model")
env = build_env(args)
obs = env.reset()
def initialize_placeholders(nlstm=128,**kwargs):
return np.zeros((args.num_env or 1, 2*nlstm)), np.zeros((1))
state, dones = initialize_placeholders(**extra_args)
while True:
actions, _, state, _ = model.step(obs,S=state, M=dones)
obs, _, done, _ = env.step(actions)
env.render()
done = done.any() if isinstance(done, np.ndarray) else done
if done:
obs = env.reset()
env.close()
def main(args):
# configure logger, disable logging in child MPI processes (with rank > 0)
arg_parser = common_arg_parser()
ppo_parser = ppo_arg_parser()
network_parser = network_arg_parser()
args, ppo_args = arg_parser.parse_known_args(args)
ppo_args, network_args = ppo_parser.parse_known_args(ppo_args)
network_args, extra_args = network_parser.parse_known_args(network_args)
extra_args = parse_cmdline_kwargs(extra_args)
dt = datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
save_dir = osp.join(os.getenv('OPENAI_LOGDIR'), args.env, dt)
if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
rank = 0
logger.configure(dir=save_dir)
with open(osp.join(logger.get_dir(), 'run.conf'), 'wt') as fh:
print(datetime.now().isoformat(), file=fh)
print(json.dumps(vars(args), indent=2), file=fh)
print(json.dumps(vars(ppo_args), indent=2), file=fh)
print(json.dumps(vars(network_args), indent=2), file=fh)
if extra_args:
print(json.dumps(vars(extra_args), indent=2), file=fh)
else:
logger.configure(format_strs=[]) # disable logging
rank = MPI.COMM_WORLD.Get_rank()
model, env = train(args, ppo_args, network_args, extra_args)
if args.save_path is not None and rank == 0:
save_path = osp.join(save_dir, '{}_{}'.format(args.env,
args.num_timesteps))
model.save(save_path)
if args.play:
logger.log("Running trained model")
obs = env.reset()
state = model.initial_state if hasattr(model,
'initial_state') else None
dones = np.zeros((1, ))
episode_rew = 0
while True:
if state is not None:
actions, _, state, _ = model.step(obs, S=state, M=dones)
else:
actions, _, _, _ = model.step(obs)
obs, rew, done, _ = env.step(actions)
episode_rew += rew[0] if isinstance(env, VecEnv) else rew
env.render()
done = done.any() if isinstance(done, np.ndarray) else done
if done:
print('episode_rew={}'.format(episode_rew))
episode_rew = 0
obs = env.reset()
env.close()
return model
def main(args):
arg_parser = common_arg_parser()
args, unknown_args = arg_parser.parse_known_args(args)
extra_args = parse_cmdline_kwargs(unknown_args)
print(args)
train_copos(args)