d3rlpy.preprocessing.MinMaxActionScaler¶
-
class
d3rlpy.preprocessing.
MinMaxActionScaler
(dataset=None, maximum=None, minimum=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.dataset import MDPDataset from d3rlpy.algos import CQL dataset = MDPDataset(observations, actions, rewards, terminals) # initialize algorithm with MinMaxActionScaler cql = CQL(action_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 MinMaxActionScaler # initialize with dataset scaler = MinMaxActionScaler(dataset) # initialize manually minimum = actions.min(axis=0) maximum = actions.max(axis=0) action_scaler = MinMaxActionScaler(minimum=minimum, maximum=maximum) cql = CQL(action_scaler=action_scaler)
- Parameters
dataset (d3rlpy.dataset.MDPDataset) – dataset object.
min (numpy.ndarray) – minimum values at each entry.
max (numpy.ndarray) – maximum values at each entry.
Methods
-
fit
(episodes)[source]¶ Estimates scaling parameters from dataset.
- Parameters
episodes (List[d3rlpy.dataset.Episode]) – a list of episode objects.
- Return type
-
fit_with_env
(env)[source]¶ Gets scaling parameters from environment.
- Parameters
env (gym.core.Env) – gym environment.
- Return type
-
reverse_transform
(action)[source]¶ Returns reversely transformed action.
- Parameters
action (torch.Tensor) – action vector.
- Returns
reversely transformed action.
- Return type
torch.Tensor
-
transform
(action)[source]¶ Returns processed action.
- Parameters
action (torch.Tensor) – action vector.
- Returns
processed action.
- Return type
torch.Tensor
Attributes