Improving Generalization and Robustness in Offline Reinforcement Learning via Boundary-Aware Data Augmentation

Current offline reinforcement learning (ORL) algorithms tend to overfit the training dataset and exhibit poor in-distribution generalization and robustness performance when deployed to real environments, thus compromising their effectiveness. Existing methods typically enhance in-distribution genera