Data Augmentation¶

d3rlpy provides data augmentation techniques tightly integrated with reinforcement learning algorithms.

Kostrikov et al., Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels.
Laskin et al., Reinforcement Learning with Augmented Data.

Efficient data augmentation potentially boosts algorithm performance significantly.

from d3rlpy.algos import DiscreteCQL

# choose data augmentation types
cql = DiscreteCQL(augmentation=['random_shift', 'intensity'],
                  n_augmentations=2)

You can also tune data augmentation parameters by yourself.

from d3rlpy.augmentation.image import RandomShift

random_shift = RandomShift(shift_size=10)

cql = DiscreteCQL(augmentation=[random_shift, 'intensity'],
                  n_augmentations=2)

Image Observation¶

`d3rlpy.augmentation.image.RandomShift`	Random shift augmentation.
`d3rlpy.augmentation.image.Cutout`	Cutout augmentation.
`d3rlpy.augmentation.image.HorizontalFlip`	Horizontal flip augmentation.
`d3rlpy.augmentation.image.VerticalFlip`	Vertical flip augmentation.
`d3rlpy.augmentation.image.RandomRotation`	Random rotation augmentation.
`d3rlpy.augmentation.image.Intensity`	Intensity augmentation.
`d3rlpy.augmentation.image.ColorJitter`	Color Jitter augmentation.

Vector Observation¶

`d3rlpy.augmentation.vector.SingleAmplitudeScaling`	Single Amplitude Scaling augmentation.
`d3rlpy.augmentation.vector.MultipleAmplitudeScaling`	Multiple Amplitude Scaling augmentation.