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

Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning arXiv:2605.18024v2 Announce Type: replace-cross 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,