Command Utils

Helpers for scripts like run_atari.py.

stable_baselines.common.cmd_util.arg_parser()

Create an empty argparse.ArgumentParser.

Returns:(ArgumentParser)

stable_baselines.common.cmd_util.atari_arg_parser()

Create an argparse.ArgumentParser for run_atari.py.

Returns:(ArgumentParser) parser {‘–env’: ‘BreakoutNoFrameskip-v4’, ‘–seed’: 0, ‘–num-timesteps’: int(1e7)}

stable_baselines.common.cmd_util.make_atari_env(env_id, num_env, seed, wrapper_kwargs=None, start_index=0, allow_early_resets=True, start_method=None, use_subprocess=False)

Create a wrapped, monitored VecEnv for Atari.

Parameters:

• env_id – (str) the environment ID
• num_env – (int) the number of environment you wish to have in subprocesses
• seed – (int) the initial seed for RNG
• wrapper_kwargs – (dict) the parameters for wrap_deepmind function
• start_index – (int) start rank index
• allow_early_resets – (bool) allows early reset of the environment
• start_method – (str) method used to start the subprocesses. See SubprocVecEnv doc for more information
• use_subprocess – (bool) Whether to use SubprocVecEnv or DummyVecEnv when num_env > 1, DummyVecEnv is usually faster. Default: False

Returns:

(VecEnv) The atari environment

stable_baselines.common.cmd_util.make_mujoco_env(env_id, seed, allow_early_resets=True)

Create a wrapped, monitored gym.Env for MuJoCo.

Parameters:

• env_id – (str) the environment ID
• seed – (int) the initial seed for RNG
• allow_early_resets – (bool) allows early reset of the environment

Returns:

(Gym Environment) The mujoco environment

stable_baselines.common.cmd_util.make_robotics_env(env_id, seed, rank=0, allow_early_resets=True)

Create a wrapped, monitored gym.Env for MuJoCo.

Parameters:

• env_id – (str) the environment ID
• seed – (int) the initial seed for RNG
• rank – (int) the rank of the environment (for logging)
• allow_early_resets – (bool) allows early reset of the environment

Returns:

(Gym Environment) The robotic environment

stable_baselines.common.cmd_util.make_vec_env(env_id, n_envs=1, seed=None, start_index=0, monitor_dir=None, wrapper_class=None, env_kwargs=None, vec_env_cls=None, vec_env_kwargs=None)

Create a wrapped, monitored VecEnv. By default it uses a DummyVecEnv which is usually faster than a SubprocVecEnv.

Parameters:

• env_id – (str or Type[gym.Env]) the environment ID or the environment class
• n_envs – (int) the number of environments you wish to have in parallel
• seed – (int) the initial seed for the random number generator
• start_index – (int) start rank index
• monitor_dir – (str) Path to a folder where the monitor files will be saved. If None, no file will be written, however, the env will still be wrapped in a Monitor wrapper to provide additional information about training.
• wrapper_class – (gym.Wrapper or callable) Additional wrapper to use on the environment. This can also be a function with single argument that wraps the environment in many things.
• env_kwargs – (dict) Optional keyword argument to pass to the env constructor
• vec_env_cls – (Type[VecEnv]) A custom VecEnv class constructor. Default: None.
• vec_env_kwargs – (dict) Keyword arguments to pass to the VecEnv class constructor.

Returns:

(VecEnv) The wrapped environment

stable_baselines.common.cmd_util.mujoco_arg_parser()

Create an argparse.ArgumentParser for run_mujoco.py.

Returns:(ArgumentParser) parser {‘–env’: ‘Reacher-v2’, ‘–seed’: 0, ‘–num-timesteps’: int(1e6), ‘–play’: False}

stable_baselines.common.cmd_util.robotics_arg_parser()

Create an argparse.ArgumentParser for run_mujoco.py.

Returns:(ArgumentParser) parser {‘–env’: ‘FetchReach-v0’, ‘–seed’: 0, ‘–num-timesteps’: int(1e6)}