RangeZeroOneObservationNormalizationStrategy¶
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class
maze.core.wrappers.observation_normalization.normalization_strategies.range_zero_one.RangeZeroOneObservationNormalizationStrategy(observation_space: gym.spaces.Box, clip_range: Tuple[Union[float, int], Union[float, int]], axis: Optional[Union[int, Tuple[int], List[int]]])¶ Normalizes observations to value range [0, 1].
The strategy subtracts in a first step the minimum observed value to shift the lowest value after normalization to zero. In a subsequent step we divide the observation with the maximum of the previous step yielding observations in the range [0, 1].
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estimate_stats(observations: List[numpy.ndarray]) → Dict[str, Union[numpy.ndarray, float, int, Iterable[Union[float, int]]]]¶ (overrides
ObservationNormalizationStrategy)- Implementation of
ObservationNormalizationStrategyinterface.
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normalize_value(value: numpy.ndarray) → numpy.ndarray¶ (overrides
ObservationNormalizationStrategy)- Implementation of
ObservationNormalizationStrategyinterface.
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