用大模型设计通信协议，让多智能体更准确地共享状态信息。
LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning
- 用大模型生成通信规则，提升信息传递效率
- 实验显示状态重建准确率显著提升
- 适合需要高效协作的多智能体系统研究者
通信是多智能体强化学习中缓解部分可观测性问题的关键，但以往方法常依赖低效的信息交换或无法传递充分的状态信息。为此，我们提出基于大模型的多智能体通信（LMAC），利用大模型的推理能力设计通信协议，使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，提升状态恢复效果并缩小智能体间知识差异。在多个MARL基准测试中，LMAC显著改善了智能体间状态重建性能，并大幅超越已有通信基线方法。
原文摘要 · Abstract (English)
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.
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