ObservationNormalizationWrapper¶
-
class
maze.core.wrappers.observation_normalization.observation_normalization_wrapper.
ObservationNormalizationWrapper
(*args, **kwds)¶ An observation normalization wrapper. It provides functionality for:
normalizing observations according to specified normalization strategies
clipping observations according to specified min and max values
estimating normalization statistics from observations collecting by interacting with the environment
manually overwriting the observation normalization parameters
The current implementation assumes that observation space is always a Dict (even if just a Dict-wrapped Box).
- Parameters
env – Environment/wrapper to wrap.
default_strategy – The default observation normalization strategy.
default_statistics – Manual default normalization statistics.
statistics_dump – Path to a pickle file dump of normalization statistics.
sampling_policy – The sampling policy for estimating the statistics.
exclude – List of observation keys to exclude from normalization.
manual_config – Additional manual configuration options.
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estimate_statistics
() → None¶ Estimates and sets the observation statistics from collected observations.
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get_statistics
() → Dict[str, Dict[str, Union[numpy.ndarray, float, int, Iterable[Union[float, int]]]]]¶ Returns the normalization statistics of the respective normalization strategy. :return: The normalization statistics for all sub steps and all dictionary observations.
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observation
(observation: Any) → Any¶ Collect observations for statistics computation or normalize them.
- Parameters
observation – The observation to be normalized.
- Returns
The normalized observation.
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classmethod
register_new_observation_normalization_strategy
(containing_submodule: Any)¶ Registers a new observation normalization strategy.
- Parameters
containing_submodule – Add all classes implementing ObservationNormalizationStrategy by walking the module recursively.