Offline reinforcement learning (RL) addresses the challenge of expensive and high-risk data exploration inherent in RL by pre-training policies on vast amounts of offline data, enabling direct deployment or fine-tuning in real-world environments. However, this training paradigm can compromise policy robustness, leading to degraded performance in practical conditions due to observation perturbations or intentional attacks. While adversarial attacks and defenses have been extensively studied in deep learning, their application in offline RL is limited. This paper proposes a framework to enhance the robustness of offline RL models by leveraging advanced adversarial attacks and defenses. The framework attacks the actor and critic components by perturbing observations during training and using adversarial defenses as regularization to enhance the learned policy. Four attacks and two defenses are introduced and evaluated on the D4RL benchmark. The results show the vulnerability of both the actor and critic to attacks and the effectiveness of the defenses in improving policy robustness. This framework holds promise for enhancing the reliability of offline RL models in practical scenarios.

该研究提出了一个框架，通过利用先进的对抗攻击和防御来提高离线强化学习模型的稳健性，并以D4RL基准进行了评估，结果显示了演员和评论家对攻击的脆弱性以及防御策略在提高策略稳健性方面的有效性，为提高实际场景中离线强化学习模型的可靠性提供了希望。

走向强壮的策略：通过对抗攻击和防守增强离线强化学习