d3rlpy.preprocessing.StandardScaler¶
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class
d3rlpy.preprocessing.StandardScaler(dataset=None, mean=None, std=None)[source]¶ Standardization preprocessing.
\[x' = (x - \mu) / \sigma\]from d3rlpy.dataset import MDPDataset from d3rlpy.algos import CQL dataset = MDPDataset(observations, actions, rewards, terminals) # initialize algorithm with StandardScaler cql = CQL(scaler='standard') # scaler is initialized from the given episodes cql.fit(dataset.episodes)
You can initialize with
d3rlpy.dataset.MDPDatasetobject or manually.from d3rlpy.preprocessing import StandardScaler # initialize with dataset scaler = StandardScaler(dataset) # initialize manually mean = observations.mean(axis=0) std = observations.std(axis=0) scaler = StandardScaler(mean=mean, std=std) cql = CQL(scaler=scaler)
Parameters: - dataset (d3rlpy.dataset.MDPDataset) – dataset object.
- mean (numpy.ndarray) – mean values at each entry.
- std (numpy.ndarray) – standard deviation at each entry.
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mean¶ mean values at each entry.
Type: numpy.ndarray
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std¶ standard deviation values at each entry.
Type: numpy.ndarray
Methods
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fit(episodes)[source]¶ Fits mean and standard deviation from list of episodes.
Parameters: episodes (list(d3rlpy.dataset.Episode)) – list of episodes.