AI RESEARCH
LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning
arXiv CS.LG
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ArXi:2605.18077v1 Announce Type: cross Communication is a key component in multi-agent reinforcement learning (MARL) for mitigating partial observability, yet prior approaches often rely on inefficient information exchange or fail to transmit sufficient state information. To address this, we propose LLM-driven Multi-Agent Communication (LMAC), which leverages an LLM's reasoning capability to design a communication protocol that enables all agents to reconstruct the underlying state as accurately and uniformly as possible.