d3rlpy.preprocessing.MinMaxScaler¶

class d3rlpy.preprocessing.MinMaxScaler(dataset=None, maximum=None, minimum=None)[source]¶

Min-Max normalization preprocessing.

\[x' = (x - \min{x}) / (\max{x} - \min{x})\]

from d3rlpy.dataset import MDPDataset
from d3rlpy.algos import CQL

dataset = MDPDataset(observations, actions, rewards, terminals)

# initialize algorithm with MinMaxScaler
cql = CQL(scaler='min_max')

# scaler is initialized from the given episodes
cql.fit(dataset.episodes)

You can also initialize with d3rlpy.dataset.MDPDataset object or manually.

from d3rlpy.preprocessing import MinMaxScaler

# initialize with dataset
scaler = MinMaxScaler(dataset)

# initialize manually
minimum = observations.min(axis=0)
maximum = observations.max(axis=0)
scaler = MinMaxScaler(minimum=minimum, maximum=maximum)

cql = CQL(scaler=scaler)

Parameters

dataset (d3rlpy.dataset.MDPDataset) – dataset object.
min (numpy.ndarray) – minimum values at each entry.
max (numpy.ndarray) – maximum values at each entry.
maximum (Optional[numpy.ndarray]) –
minimum (Optional[numpy.ndarray]) –

Methods

fit(episodes)[source]¶

Estimates scaling parameters from dataset.

Parameters: episodes (List[d3rlpy.dataset.Episode]) – list of episodes.
Return type: None

fit_with_env(env)[source]¶

Gets scaling parameters from environment.

Parameters: env (gym.core.Env) – gym environment.
Return type: None

get_params(deep=False)[source]¶

Returns scaling parameters.

Parameters: deep (bool) – flag to deeply copy objects.
Returns: scaler parameters.
Return type: Dict[str, Any]

get_type()¶

Returns a scaler type.

Returns: scaler type.
Return type: str

reverse_transform(x)[source]¶

Returns reversely transformed observations.

Parameters: x (torch.Tensor) – observation.
Returns: reversely transformed observation.
Return type: torch.Tensor

transform(x)[source]¶

Returns processed observations.

Parameters: x (torch.Tensor) – observation.
Returns: processed observation.
Return type: torch.Tensor

Attributes

TYPE: ClassVar[str] = 'min_max'¶