做MARL研究的团队终于有了一个利用LLM推理能力来设计通信协议的实用方案，LMAC直接解决了部分可观测性下的信息瓶颈问题，值得在实验环境中试试。
多智能体强化学习（MARL）中，通信是缓解部分可观测性的关键，但现有方法常存在信息交换低效或状态信息不足的问题。研究者提出LMAC（LLM驱动的多智能体通信），利用大语言模型的推理能力设计通信协议，让所有智能体尽可能准确且一致地重构底层状态。LMAC通过显式的状态感知准则迭代优化协议，改善状态恢复并缩小智能体间的知识差异。在多个MARL基准测试中，LMAC显著提升了智能体间的状态重建质量，并在性能上大幅超越之前的通信基线方法。
LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning
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. LMAC iteratively refines the protocol using an explicit state-awareness criterion, improving state recovery while narrowing differences in agents' knowledge. Experiments on diverse MARL benchmarks show that LMAC improves state reconstruction across agents and yields substantial performance gains over prior communication baselines.