AI RESEARCH
Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning
arXiv CS.LG
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ArXi:2604.08728v1 Announce Type: new Cooperation in multi-agent reinforcement learning (MARL) benefits from inter-agent communication, yet most approaches assume idealized channels and existing value decomposition methods ignore who successfully shared information with whom. We propose CLOVER, a cooperative MARL framework whose centralized value mixer is conditioned on the communication graph realized under a realistic wireless channel. This graph