Loads the selected environment and wraps it with the specified wrappers.
tf_agents.environments.suite_gym.load(
environment_name: Text,
discount: tf_agents.typing.types.Float
= 1.0,
max_episode_steps: Optional[types.Int] = None,
gym_env_wrappers: Sequence[tf_agents.typing.types.GymEnvWrapper
] = (),
env_wrappers: Sequence[tf_agents.typing.types.PyEnvWrapper
] = (),
spec_dtype_map: Optional[Dict[gym.Space, np.dtype]] = None,
gym_kwargs: Optional[Dict[str, Any]] = None,
render_kwargs: Optional[Dict[str, Any]] = None
) -> tf_agents.environments.PyEnvironment
Used in the notebooks
Note that by default a TimeLimit wrapper is used to limit episode lengths
to the default benchmarks defined by the registered environments.
Args |
environment_name
|
Name for the environment to load.
|
discount
|
Discount to use for the environment.
|
max_episode_steps
|
If None the max_episode_steps will be set to the default
step limit defined in the environment's spec. No limit is applied if set
to 0 or if there is no max_episode_steps set in the environment's spec.
|
gym_env_wrappers
|
Iterable with references to wrapper classes to use
directly on the gym environment.
|
env_wrappers
|
Iterable with references to wrapper classes to use on the
gym_wrapped environment.
|
spec_dtype_map
|
A dict that maps gym spaces to np dtypes to use as the
default dtype for the arrays. An easy way how to configure a custom
mapping through Gin is to define a gin-configurable function that returns
desired mapping and call it in your Gin congif file, for example:
suite_gym.load.spec_dtype_map = @get_custom_mapping() .
|
gym_kwargs
|
Optional kwargs to pass to the Gym environment class.
|
render_kwargs
|
Optional kwargs for rendering to pass to render() of the
gym_wrapped environment.
|
Returns |
A PyEnvironment instance.
|