Robust Communicative Multi-Agent Reinforcement Learning with Active Defense
Lebin Yu,
Yunbo Qiu,
Quanming Yao
et al.
Abstract:Communication in multi-agent reinforcement learning (MARL) has been proven to effectively promote cooperation among agents recently. Since communication in real-world scenarios is vulnerable to noises and adversarial attacks, it is crucial to develop robust communicative MARL technique. However, existing research in this domain has predominantly focused on passive defense strategies, where agents receive all messages equally, making it hard to balance performance and robustness. We propose an active defense st… Show more
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