d3rlpy.preprocessing.MinMaxActionScaler¶
- class d3rlpy.preprocessing.MinMaxActionScaler(minimum=None, maximum=None)[source]¶
Min-Max normalization action preprocessing.
Actions will be normalized in range
[-1.0, 1.0]
.\[a' = (a - \min{a}) / (\max{a} - \min{a}) * 2 - 1\]from d3rlpy.preprocessing import MinMaxActionScaler from d3rlpy.algos import CQLConfig # normalize based on datasets or environments cql = CQLConfig(action_scaler=MinMaxActionScaler()).create() # manually initialize minimum = actions.min(axis=0) maximum = actions.max(axis=0) action_scaler = MinMaxActionScaler(minimum=minimum, maximum=maximum) cql = CQLConfig(action_scaler=action_scaler).create()
- Parameters
minimum (numpy.ndarray) – Minimum values at each entry.
maximum (numpy.ndarray) – Maximum values at each entry.
- Return type
Methods
- classmethod deserialize(serialized_config)¶
- Parameters
serialized_config (str) –
- Return type
d3rlpy.serializable_config.TConfig
- classmethod deserialize_from_dict(dict_config)¶
- Parameters
dict_config (Dict[str, Any]) –
- Return type
d3rlpy.serializable_config.TConfig
- classmethod deserialize_from_file(path)¶
- Parameters
path (str) –
- Return type
d3rlpy.serializable_config.TConfig
- fit_with_env(env)[source]¶
Gets scaling parameters from environment.
- Parameters
env (gym.core.Env[Any, Any]) – Gym environment.
- Return type
- fit_with_trajectory_slicer(episodes, trajectory_slicer)[source]¶
Estimates scaling parameters from dataset.
- Parameters
episodes (Sequence[d3rlpy.dataset.components.EpisodeBase]) – List of episodes.
trajectory_slicer (d3rlpy.dataset.trajectory_slicers.TrajectorySlicerProtocol) – Trajectory slicer to process mini-batch.
- Return type
- fit_with_transition_picker(episodes, transition_picker)[source]¶
Estimates scaling parameters from dataset.
- Parameters
episodes (Sequence[d3rlpy.dataset.components.EpisodeBase]) – List of episodes.
transition_picker (d3rlpy.dataset.transition_pickers.TransitionPickerProtocol) – Transition picker to process mini-batch.
- Return type
- 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 (torch.Tensor) – input.
- Returns
Inversely transformed output.
- Return type
torch.Tensor
- reverse_transform_numpy(x)[source]¶
Returns reversely transformed output in numpy.
- Parameters
x (numpy.ndarray) – Input.
- Returns
Inversely transformed output.
- Return type
- 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 (torch.Tensor) – Input.
- Returns
Processed output.
- Return type
torch.Tensor
- transform_numpy(x)[source]¶
Returns processed output in numpy.
- Parameters
x (numpy.ndarray) – Input.
- Returns
Processed output.
- Return type
Attributes
- built¶
- maximum: Optional[numpy.ndarray] = None¶
- minimum: Optional[numpy.ndarray] = None¶