模仿狼群围猎策略，设计新型协同攻击框架提升多智能体强化学习鲁棒性。
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning
- 借鉴狼群协作捕猎，针对主目标及其帮手实施精准打击。
- 实验表明该攻击可严重破坏协作，而新训练框架显著提升防御能力。
- 适合研究多智能体系统安全与鲁棒强化学习的学者参考。
传统多智能体强化学习（MARL）的鲁棒方法在合作场景中常难以应对协同对抗攻击。为此，我们提出受狼群狩猎策略启发的狼群对抗攻击框架（Wolfpack Adversarial Attack），旨在攻击初始目标及其协助智能体以破坏协作。同时，提出狼群对抗学习框架（WALL），通过促进全局协作训练鲁棒的MARL策略以防御该攻击。实验结果表明，狼群攻击具有显著破坏力，而WALL框架能有效提升系统鲁棒性。代码已开源：https://github.com/sunwoolee0504/WALL。
原文摘要 · Abstract (English)
Traditional robust methods in multi-agent reinforcement learning (MARL) often struggle against coordinated adversarial attacks in cooperative scenarios. To address this limitation, we propose the Wolfpack Adversarial Attack framework, inspired by wolf hunting strategies, which targets an initial agent and its assisting agents to disrupt cooperation. Additionally, we introduce the Wolfpack-Adversarial Learning for MARL (WALL) framework, which trains robust MARL policies to defend against the proposed Wolfpack attack by fostering systemwide collaboration. Experimental results underscore the devastating impact of the Wolfpack attack and the significant robustness improvements achieved by WALL. Our code is available at https://github.com/sunwoolee0504/WALL.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。