Automatic Data Augmentation for Generalization in Deep Reinforcement Learning

Understand

Deep reinforcement learning (RL) agents often fail to generalize to unseen scenarios, even when they are trained on many instances of semantically similar environments.

• Data augmentation has recently been shown to improve the sample efficiency and generalization of RL agents.
• However, different tasks tend to benefit from different kinds of data augmentation.
• In this paper, we compare three approaches for automatically finding an appropriate augmentation.