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.

Methods

classmethod deserialize(serialized_config)¶

Parameters:: serialized_config (str) –
Return type:: TConfig

classmethod deserialize_from_dict(dict_config)¶

Parameters:: dict_config (Dict[str, Any]) –
Return type:: TConfig

classmethod deserialize_from_file(path)¶

Parameters:: path (str) –
Return type:: TConfig

fit_with_env(env)[source]¶

Gets scaling parameters from environment.

Parameters:: env (Union[Env[Any, Any], Env[Any, Any]]) – Gym environment.
Return type:: None

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)¶

Parameters:: kvs (Optional[Union[dict, list, str, int, float, bool]]) –
Return type:: A

classmethod from_json(s, *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw)¶

Parameters:: s (Union[str, bytes, bytearray]) –
Return type:: A

static get_type()[source]¶

Return type:: str

reverse_transform(x)[source]¶

Returns reversely transformed output.

Parameters:: x (Tensor) – input.
Returns:: Inversely transformed output.
Return type:: Tensor

reverse_transform_numpy(x)[source]¶

Returns reversely transformed output in numpy.

Parameters:: x (ndarray[Any, dtype[Any]]) – Input.
Returns:: Inversely transformed output.
Return type:: ndarray[Any, dtype[Any]]

classmethod schema(*, infer_missing=False, only=None, exclude=(), many=False, context=None, load_only=(), dump_only=(), partial=False, unknown=None)¶

Parameters:

infer_missing (bool) –
many (bool) –
partial (bool) –

Return type:

SchemaF[A]

serialize()¶

Return type:: str

serialize_to_dict()¶

Return type:: Dict[str, Any]

to_dict(encode_json=False)¶

Return type:: Dict[str, Optional[Union[dict, list, str, int, float, bool]]]

to_json(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, indent=None, separators=None, default=None, sort_keys=False, **kw)¶

Parameters:

skipkeys (bool) –
ensure_ascii (bool) –
check_circular (bool) –
allow_nan (bool) –
indent (Optional[Union[int, str]]) –
separators (Optional[Tuple[str, str]]) –
default (Optional[Callable]) –
sort_keys (bool) –

Return type:

str

transform(x)[source]¶

Returns processed output.

Parameters:: x (Tensor) – Input.
Returns:: Processed output.
Return type:: Tensor

transform_numpy(x)[source]¶

Returns processed output in numpy.

Parameters:: x (ndarray[Any, dtype[Any]]) – Input.
Returns:: Processed output.
Return type:: ndarray[Any, dtype[Any]]

Attributes

built¶

maximum: Optional[ndarray[Any, dtype[Any]]] = None¶

minimum: Optional[ndarray[Any, dtype[Any]]] = None¶