lit_sb3.utils.env

source module lit_sb3.utils.env

Environment creation and wrapping utilities.

Functions

maybe_make_env — If env is a string, make the environment; otherwise, return env.
unwrap_wrapper — Retrieve a VecEnvWrapper object by recursively searching.
is_wrapped — Check if a given environment has been wrapped with a given wrapper.
make_vec_env — Create a wrapped, monitored VecEnv.
make_atari_env — Create a wrapped, monitored VecEnv for Atari.
create_test_env — Create a vectorised environment for evaluating a trained agent.

source maybe_make_env(env: GymEnv | str, verbose: int) → GymEnv

If env is a string, make the environment; otherwise, return env.

Parameters

env : GymEnv | str — The environment to learn from.
verbose : int — Verbosity level: 0 for no output, 1 for indicating if environment is created :return A Gym (vector) environment.

source unwrap_wrapper(env: gym.Env, wrapper_class: type[gym.Wrapper]) → gym.Wrapper | None

Retrieve a VecEnvWrapper object by recursively searching.

Parameters

env : gym.Env — Environment to unwrap
wrapper_class : type[gym.Wrapper] — Wrapper to look for

Returns

gym.Wrapper | None — Environment unwrapped till wrapper_class if it has been wrapped with it

source is_wrapped(env: gym.Env, wrapper_class: type[gym.Wrapper]) → bool

Check if a given environment has been wrapped with a given wrapper.

Parameters

env : gym.Env — Environment to check
wrapper_class : type[gym.Wrapper] — Wrapper class to look for

Returns

bool — True if environment has been wrapped with wrapper_class.

Create a wrapped, monitored VecEnv.

By default it uses a DummyVecEnv which is usually faster than a SubprocVecEnv.

Parameters

env_id : str | Callable[..., gym.Env] — either the env ID, the env class or a callable returning an env
n_envs : int — the number of environments you wish to have in parallel
seed : int | None — the initial seed for the random number generator
start_index : int — start rank index
monitor_dir : str | Path | None — 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 : Callable[[gym.Env], gym.Env] | None — Additional wrapper to use on the environment. This can also be a function with single argument that wraps the environment in many things. Note: the wrapper specified by this parameter will be applied after the Monitor wrapper. if some cases (e.g. with TimeLimit wrapper) this can lead to undesired behavior. See here for more details: https://github.com/DLR-RM/stable-baselines3/issues/894
env_kwargs : dict[str, Any] | None — Optional keyword argument to pass to the env constructor
vec_env_cls : type[DummyVecEnv | SubprocVecEnv] | None — A custom VecEnv class constructor. Default: None.
vec_env_kwargs : dict[str, Any] | None — Keyword arguments to pass to the VecEnv class constructor.
monitor_kwargs : dict[str, Any] | None — Keyword arguments to pass to the Monitor class constructor.
wrapper_kwargs : dict[str, Any] | None — Keyword arguments to pass to the Wrapper class constructor.

Returns

VecEnv — The wrapped environment

Create a wrapped, monitored VecEnv for Atari.

It is a wrapper around make_vec_env that includes common preprocessing for Atari games.

.. note:: By default, the AtariWrapper uses terminal_on_life_loss=True, which causes env.reset() to perform a no-op step instead of truly resetting when the environment terminates due to a loss of life (but not game over). To ensure reset() always resets the env, pass wrapper_kwargs=dict(terminal_on_life_loss=False).

Parameters

env_id : str | Callable[..., gym.Env] — either the env ID, the env class or a callable returning an env
n_envs : int — the number of environments you wish to have in parallel
seed : int | None — the initial seed for the random number generator
start_index : int — start rank index
monitor_dir : str | Path | None — 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_kwargs : dict[str, Any] | None — Optional keyword argument to pass to the AtariWrapper
env_kwargs : dict[str, Any] | None — Optional keyword argument to pass to the env constructor
vec_env_cls : type[DummyVecEnv] | type[SubprocVecEnv] | None — A custom VecEnv class constructor. Default: None.
vec_env_kwargs : dict[str, Any] | None — Keyword arguments to pass to the VecEnv class constructor.
monitor_kwargs : dict[str, Any] | None — Keyword arguments to pass to the Monitor class constructor.

Returns

VecEnv — The wrapped environment

Create a vectorised environment for evaluating a trained agent.

Parameters

env_id : str — Gymnasium environment ID.
n_envs : int — Number of parallel environments.
stats_path : str | Path | None — Folder containing saved VecNormalize stats.
seed : int — Random seed.
log_dir : str | Path | None — Optional directory for episode reward logging.
should_render : bool — Add render_mode="human" when True.
hyperparams : dict[str, Any] | None — Extra hyperparams (e.g. frame_stack, normalize).
env_kwargs : dict[str, Any] | None — Keyword arguments passed to the env constructor.
vec_env_cls : type[VecEnv] | None — VecEnv class to use (defaults to DummyVecEnv).
vec_env_kwargs : dict[str, Any] | None — Keyword arguments for the VecEnv constructor.

Returns

Wrapped — class:~lit_sb3.vec_env.VecEnv.

Raises

ValueError