d3rlpy.preprocessing.PixelScaler¶

class d3rlpy.preprocessing.PixelScaler[source]¶

Pixel normalization preprocessing.

\[x' = x / 255\]

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

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

# initialize algorithm with PixelScaler
cql = CQL(scaler='pixel')

cql.fit(dataset.episodes)

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] = 'pixel'¶