Preprocessing

d3rlpy provides several preprocessors tightly incorporated with algorithms. Each preprocessor is implemented with PyTorch operation, which will be included in the model exported by save_policy method.

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

dataset = MDPDataset(...)

# choose from ['pixel', 'min_max', 'standard'] or None
cql = CQL(scaler='standard')

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

# preprocesing is included in TorchScript
cql.save_policy('policy.pt')

# you don't need to take care of preprocessing at production
policy = torch.jit.load('policy.pt')
action = policy(unpreprocessed_x)

You can also initialize scalers by yourself.

from d3rlpy.preprocessing import StandardScaler

scaler = StandardScaler(mean=..., std=...)

cql = CQL(scaler=scaler)

d3rlpy.preprocessing.PixelScaler	Pixel normalization preprocessing.
d3rlpy.preprocessing.MinMaxScaler	Min-Max normalization preprocessing.
d3rlpy.preprocessing.StandardScaler	Standardization preprocessing.