d3rlpy.preprocessing.MinMaxScaler¶
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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.MDPDatasetobject 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.
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minimum¶ minimum values at each entry.
Type: numpy.ndarray
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maximum¶ maximum values at each entry.
Type: numpy.ndarray
Methods
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fit(episodes)[source]¶ Fits minimum and maximum from list of episodes.
Parameters: episodes (list(d3rlpy.dataset.Episode)) – list of episodes.