d3rlpy.preprocessing.MinMaxObservationScaler¶
- class d3rlpy.preprocessing.MinMaxObservationScaler(minimum=None, maximum=None)[source]¶
Min-Max normalization preprocessing.
Observations will be normalized in range
[-1.0, 1.0]
.\[x' = (x - \min{x}) / (\max{x} - \min{x}) * 2 - 1\]from d3rlpy.preprocessing import MinMaxObservationScaler from d3rlpy.algos import CQLConfig # normalize based on datasets or environments cql = CQLConfig(observation_scaler=MinMaxObservationScaler()).create() # manually initialize minimum = observations.min(axis=0) maximum = observations.max(axis=0) observation_scaler = MinMaxObservationScaler( minimum=minimum, maximum=maximum, ) cql = CQLConfig(observation_scaler=observation_scaler).create()
- Parameters:
minimum (numpy.ndarray) – Minimum values at each entry.
maximum (numpy.ndarray) – Maximum values at each entry.
Methods
- classmethod deserialize(serialized_config)¶
- Parameters:
serialized_config (str) –
- Return type:
TConfig
- classmethod deserialize_from_dict(dict_config)¶
- fit_with_trajectory_slicer(episodes, trajectory_slicer)[source]¶
Estimates scaling parameters from dataset.
- Parameters:
episodes (Sequence[EpisodeBase]) – List of episodes.
trajectory_slicer (TrajectorySlicerProtocol) – Trajectory slicer to process mini-batch.
- Return type:
None
- fit_with_transition_picker(episodes, transition_picker)[source]¶
Estimates scaling parameters from dataset.
- Parameters:
episodes (Sequence[EpisodeBase]) – List of episodes.
transition_picker (TransitionPickerProtocol) – Transition picker to process mini-batch.
- Return type:
None
- classmethod from_dict(kvs, *, infer_missing=False)¶
- classmethod from_json(s, *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw)¶
- reverse_transform(x)[source]¶
Returns reversely transformed output.
- Parameters:
x (Tensor) – input.
- Returns:
Inversely transformed output.
- Return type:
Tensor
- classmethod schema(*, infer_missing=False, only=None, exclude=(), many=False, context=None, load_only=(), dump_only=(), partial=False, unknown=None)¶
- to_dict(encode_json=False)¶
- to_json(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, indent=None, separators=None, default=None, sort_keys=False, **kw)¶
- transform(x)[source]¶
Returns processed output.
- Parameters:
x (Tensor) – Input.
- Returns:
Processed output.
- Return type:
Tensor
Attributes
- built¶