Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning

Sunwoo Lee; Mingu Kang; Yonghyeon Jo; Seungyul Han

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arXiv · 2605.18024

Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning

Sunwoo Lee·Mingu Kang·Yonghyeon Jo·Seungyul Han

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Abstract

Cooperation is central to multi-agent reinforcement learning (MARL), yet learned coordination can be fragile when external perturbations disrupt inter-agent interactions. Prior robust MARL methods have primarily considered value-oriented attacks, leaving a gap in robustness when interaction structures themselves are corrupted. In this paper, we propose an interaction-breaking adversarial learning (IBAL) framework that takes an information-theoretic view to construct attacks that impede coordination by perturbing agents' observations and actions, and trains agents to perform reliably under such disruptions. Empirically, our approach improves robustness over existing robust MARL baselines across diverse attack settings and yields stronger performance even under agent-missing scenarios. Our code is available at https://sunwoolee0504.github.io/IBAL.

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Sunwoo Lee, Mingu Kang, Yonghyeon Jo, Seungyul Han. 2026-05-18. Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning. https://arxiv.org/abs/2605.18024

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