采用集成防御方法提升深度强化学习的鲁棒性
Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach
- AImotion Bavaria, Technische Hochschule Ingolstadt（巴伐利亚AImotion、英格尔施泰特技术大学）
- School of Computation, Information and Technology, TU Munich（计算、信息与技术学院，慕尼黑技术大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对自动驾驶DRL面临的对抗攻击，本文提出集成多种防御的架构，实验表明其在FGSM攻击下显著提升奖励并降低碰撞率。
AI中文摘要：
深度强化学习（DRL）的最新进展已证明其适用于机器人、医疗保健、能源优化和自动驾驶等多个领域。然而，一个关键问题仍然存在：当面对对抗攻击时，DRL模型的鲁棒性如何？尽管对抗训练和蒸馏等现有防御机制增强了DRL模型的恢复能力，但尤其在自动驾驶场景中，关于整合多种防御仍存在显著研究空白。本文通过提出一种新颖的基于集成的防御架构来缓解自动驾驶中的对抗攻击，从而解决这一空白。我们的评估表明，所提出的架构显著增强了DRL模型的鲁棒性。在FGSM攻击下，与基线相比，我们的集成方法在高速公路场景和汇入场景中将平均奖励从5.87提升至18.38（增幅超过213%），并将平均碰撞率从0.50降至0.09（下降82%），优于所有独立防御策略。
英文摘要：
Recent advancements in Deep Reinforcement Learning (DRL) have demonstrated its applicability across various domains, including robotics, healthcare, energy optimization, and autonomous driving. However, a critical question remains: How robust are DRL models when exposed to adversarial attacks? While existing defense mechanisms such as adversarial training and distillation enhance the resilience of DRL models, there remains a significant research gap regarding the integration of multiple defenses in autonomous driving scenarios specifically. This paper addresses this gap by proposing a novel ensemble-based defense architecture to mitigate adversarial attacks in autonomous driving. Our evaluation demonstrates that the proposed architecture significantly enhances the robustness of DRL models. Compared to the baseline under FGSM attacks, our ensemble method improves the mean reward from 5.87 to 18.38 (over 213% increase) and reduces the mean collision rate from 0.50 to 0.09 (an 82% decrease) in the highway scenario and merge scenario, outperforming all standalone defense strategies.
