Wraps given gym environment with TF Agent's GymWrapper.
tf_agents.environments.suite_gym.wrap_env(
gym_env: gym.Env,
discount: tf_agents.typing.types.Float
= 1.0,
max_episode_steps: Optional[types.Int] = None,
gym_env_wrappers: Sequence[tf_agents.typing.types.GymEnvWrapper
] = (),
time_limit_wrapper: tf_agents.environments.suite_gym.TimeLimitWrapperType
= tf_agents.environments.TimeLimit
,
env_wrappers: Sequence[tf_agents.typing.types.PyEnvWrapper
] = (),
spec_dtype_map: Optional[Dict[gym.Space, np.dtype]] = None,
auto_reset: bool = True,
render_kwargs: Optional[Dict[str, Any]] = None
) -> tf_agents.environments.PyEnvironment
Note that by default a TimeLimit wrapper is used to limit episode lengths
to the default benchmarks defined by the registered environments.
Args |
gym_env
|
An instance of OpenAI gym environment.
|
discount
|
Discount to use for the environment.
|
max_episode_steps
|
Used to create a TimeLimitWrapper. No limit is applied if
set to None or 0. Usually set to gym_spec.max_episode_steps in load .
|
gym_env_wrappers
|
Iterable with references to wrapper classes to use
directly on the gym environment.
|
time_limit_wrapper
|
Wrapper that accepts (env, max_episode_steps) params to
enforce a TimeLimit. Usuaully this should be left as the default,
wrappers.TimeLimit.
|
env_wrappers
|
Iterable with references to wrapper classes to use on the
gym_wrapped environment.
|
spec_dtype_map
|
A dict that maps gym specs to tf dtypes to use as the
default dtype for the tensors. 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 config file, for example:
suite_gym.load.spec_dtype_map = @get_custom_mapping() .
|
auto_reset
|
If True (default), reset the environment automatically after a
terminal state is reached.
|
render_kwargs
|
Optional dict of keywoard arguments for rendering.
|
Returns |
A PyEnvironment instance.
|