Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning
This addresses vulnerabilities in multi-agent systems for applications like robotics or autonomous vehicles, though it appears incremental as it builds on existing robust MARL methods.
The paper tackles the problem of coordinated adversarial attacks in cooperative multi-agent reinforcement learning by proposing the Wolfpack Adversarial Attack framework to disrupt cooperation and the WALL framework to defend against it, resulting in significant robustness improvements.
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.