d3rlpy.preprocessing.MinMaxRewardScaler¶

class d3rlpy.preprocessing.MinMaxRewardScaler(minimum=None, maximum=None, multiplier=1.0)[source]¶

Min-Max reward normalization preprocessing.

Rewards will be normalized in range [0.0, 1.0].

\[r' = (r - \min(r)) / (\max(r) - \min(r))\]

from d3rlpy.preprocessing import MinMaxRewardScaler
from d3rlpy.algos import CQLConfig

# normalize based on datasets
cql = CQLConfig(reward_scaler=MinMaxRewardScaler()).create()

# initialize manually
reward_scaler = MinMaxRewardScaler(minimum=0.0, maximum=10.0)
cql = CQLConfig(reward_scaler=reward_scaler).create()

Parameters

minimum (float) – Minimum value.
maximum (float) – Maximum value.
multiplier (float) – Constant multiplication value.

Return type

None

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)¶

Gets scaling parameters from environment.

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

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

None

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

None

classmethod from_dict(kvs, *, infer_missing=False)¶

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

static get_type()[source]¶

Return type: str

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: numpy.ndarray

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

dataclasses_json.mm.SchemaF[dataclasses_json.api.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 (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: numpy.ndarray

Attributes

built¶

maximum: Optional[float] = None¶

minimum: Optional[float] = None¶

multiplier: float = 1.0¶