Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning

2025-02-05 · Sunwoo Lee, Jaebak Hwang, Yonghyeon Jo, Seungyul Han

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.

📄 PDF Abstract BibTeX arXiv:2502.02844

Code (1)

sunwoolee0504/wall 공식 구현 pytorch

Tasks

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