sinergym.utils.wrappers.NormalizeObservation

class sinergym.utils.wrappers.NormalizeObservation(env: Any, ranges: Dict[str, Sequence[Any]])

__init__(env: Any, ranges: Dict[str, Sequence[Any]])

Observations normalized to range [0, 1].

Parameters:

env (Any) – Original Sinergym environment.
ranges (Dict[str, Sequence[Any]]) – Observation variables ranges to apply normalization (rely on environment).

Methods

`__init__`(env, ranges)	Observations normalized to range [0, 1].
`class_name`()	Returns the class name of the wrapper.
`close`()	Closes the wrapper and `env`.
`get_unwrapped_obs`()	Get last environment observation without normalization.
`get_wrapper_attr`(name)	Gets an attribute from the wrapper and lower environments if name doesn't exist in this object.
`observation`(obs)	Applies normalization to observation.
`render`()	Uses the `render()` of the `env` that can be overwritten to change the returned data.
`reset`(*[, seed, options])	Modifies the `env` after calling `reset()`, returning a modified observation using `self.observation()`.
`step`(action)	Modifies the `env` after calling `step()` using `self.observation()` on the returned observations.
`wrapper_spec`(**kwargs)	Generates a WrapperSpec for the wrappers.

Attributes

`action_space`	Return the `Env` `action_space` unless overwritten then the wrapper `action_space` is used.
`metadata`	Returns the `Env` `metadata`.
`np_random`	Returns the `Env` `np_random` attribute.
`observation_space`	Return the `Env` `observation_space` unless overwritten then the wrapper `observation_space` is used.
`render_mode`	Returns the `Env` `render_mode`.
`reward_range`	Return the `Env` `reward_range` unless overwritten then the wrapper `reward_range` is used.
`spec`	Returns the `Env` `spec` attribute with the WrapperSpec if the wrapper inherits from EzPickle.
`unwrapped`	Returns the base environment of the wrapper.

get_unwrapped_obs() → ndarray | None

Get last environment observation without normalization.

Returns:: Last original observation. If it is the first observation, this value is None.
Return type:: Optional[np.ndarray]

observation(obs: ndarray) → ndarray

Applies normalization to observation.