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arXiv Machine Learning By Gong Gao, Weidong Zhao, Xianhui Liu

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

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The paper introduces BADA, a Boundary-Aware Data Augmentation technique for offline reinforcement learning. By interpolating neighboring states to create synthetic data that respects the original distribution, BADA improves in-distribution generalization and robustness. Experiments on limited offline datasets show that BADA achieves state-of-the-art performance across diverse benchmarks.

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