Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning
Abstract
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 system-wide 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.
Cite
Text
Lee et al. "Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning." Proceedings of the 42nd International Conference on Machine Learning, 2025.Markdown
[Lee et al. "Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning." Proceedings of the 42nd International Conference on Machine Learning, 2025.](https://mlanthology.org/icml/2025/lee2025icml-wolfpack/)BibTeX
@inproceedings{lee2025icml-wolfpack,
title = {{Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning}},
author = {Lee, Sunwoo and Hwang, Jaebak and Jo, Yonghyeon and Han, Seungyul},
booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
year = {2025},
pages = {33025-33056},
volume = {267},
url = {https://mlanthology.org/icml/2025/lee2025icml-wolfpack/}
}