# Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2502.02844 · HTML: https://mlsanity.com/paper/2502.02844/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 56 of its 83 references that are in the map.
- First posted on arXiv 5 Feb 2025.

## Builds on (most cited first, 30 of 56)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [The StarCraft Multi-Agent Challenge](https://mlsanity.com/paper/1902.04043/) (2019)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://mlsanity.com/paper/1706.05296/) (2017)
- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [Safe Model-based Reinforcement Learning with Stability Guarantees](https://mlsanity.com/paper/1705.08551/) (2017)
- [Adversarial Attacks on Neural Network Policies](https://mlsanity.com/paper/1702.02284/) (2017)
- [QPLEX: Duplex Dueling Multi-Agent Q-Learning](https://mlsanity.com/paper/2008.01062/) (2020)
- [Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations](https://mlsanity.com/paper/2003.08938/) (2020)
- [PettingZoo: Gym for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2009.14471/) (2020)
- [Action Robust Reinforcement Learning and Applications in Continuous Control](https://mlsanity.com/paper/1901.09184/) (2019)
- [Robust Deep Reinforcement Learning with Adversarial Attacks](https://mlsanity.com/paper/1712.03632/) (2017)
- [A Review of Cooperative Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1908.03963/) (2019)
- [Robust Reinforcement Learning on State Observations with Learned Optimal Adversary](https://mlsanity.com/paper/2101.08452/) (2021)
- [RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning](https://mlsanity.com/paper/2204.12581/) (2022)
- [Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity](https://mlsanity.com/paper/2208.05767/) (2022)
- [Adaptive Reward-Poisoning Attacks against Reinforcement Learning](https://mlsanity.com/paper/2003.12613/) (2020)
- [Reinforcement Learning with Perturbed Rewards](https://mlsanity.com/paper/1810.01032/) (2018)
- [Robust Reinforcement Learning for Continuous Control with Model Misspecification](https://mlsanity.com/paper/1906.07516/) (2019)
- [Robust Reinforcement Learning using Adversarial Populations](https://mlsanity.com/paper/2008.01825/) (2020)
- [Adversarial Attacks On Multi-Agent Communication](https://mlsanity.com/paper/2101.06560/) (2021)
- [On the Robustness of Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.03722/) (2020)
- [Robust Multi-Agent Reinforcement Learning with State Uncertainty](https://mlsanity.com/paper/2307.16212/) (2023)
- [Adversarial Model for Offline Reinforcement Learning](https://mlsanity.com/paper/2302.11048/) (2023)
- [Robustifying Reinforcement Learning Agents via Action Space Adversarial Training](https://mlsanity.com/paper/2007.07176/) (2020)
- [A Max-Min Entropy Framework for Reinforcement Learning](https://mlsanity.com/paper/2106.10517/) (2021)

## Built on by (most cited first, 6 of 6)

- [Advancing Language Multi-Agent Learning with Credit Re-Assignment for Interactive Environment Generalization](https://mlsanity.com/paper/2502.14496/) (2025)
- [Self-Improving Skill Learning for Robust Skill-based Meta-Reinforcement Learning](https://mlsanity.com/paper/2502.03752/) (2025)
- [Retaining Suboptimal Actions to Follow Shifting Optima in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2602.17062/) (2026)
- [LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2605.18077/) (2026)
- [Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2605.18024/) (2026)
- [Online Security Learning in Cooperative Multi-Agent Systems under Hidden Byzantine Attacks](https://mlsanity.com/paper/2608.06520/) (2026)

## Abstract (arXiv)

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.