AI RESEARCH
Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning
arXiv CS.LG
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ArXi:2605.18024v1 Announce Type: new 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.