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.

翻译：通信是多智能体强化学习中缓解部分可观测性的关键组成部分，然而现有方法通常依赖于低效的信息交换或无法传输足够的状态信息。为此，我们提出LLM驱动的多智能体通信（LMAC），该方法利用LLM的推理能力设计通信协议，使所有智能体能够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性标准迭代优化协议，在改进状态恢复的同时缩小智能体间的知识差异。在多样化的多智能体强化学习基准测试上的实验表明，LMAC提升了智能体间的状态重构效果，相较于现有通信基线方法取得了显著的性能提升。