Multi-Agent Systems (MAS) have emerged as a fundamental paradigm for distributed problem-solving, where autonomous agents collaborate to achieve complex objectives. Within this framework, Multi-Agent Reinforcement Learning (MARL) with communication has demonstrated remarkable success in cooperative tasks. However, existing approaches predominantly pursue performance gains through increasingly complex architectures and expanding communication overhead, lacking principled metrics to evaluate the efficiency of information exchange. In this paper, we focus on enabling agents to learn efficient multi-agent communication protocols that balance performance and information compactness. We propose the Information Entropy Efficiency Index (IEI), a novel metric that quantifies the ratio between message entropy and task performance in learned communication protocols. A lower IEI indicates more compact and efficient message representations. By incorporating IEI into training loss functions, we encourage agents to develop communication protocols that achieve high performance with improved communication efficiency. Extensive experiments across diverse MARL algorithms demonstrate that our approach achieves equivalent or superior task performance compared to baseline methods while improving communication efficiency. These findings challenge the prevailing assumption that performance improvements require complex architectures or increased communication overhead and highlight the potential of improving both task success and communication efficiency to enable scalable MAS.

翻译：多智能体系统（MAS）已成为分布式问题求解的基础范式，其中自主智能体通过协作实现复杂目标。在此框架下，具备通信能力的多智能体强化学习（MARL）在协作任务中展现出显著成效。然而，现有方法主要追求通过日益复杂的架构和不断扩大的通信开销来提升性能，缺乏评估信息交换效率的原则性指标。本文致力于使智能体能够学习兼顾性能与信息紧凑性的高效多智能体通信协议。我们提出信息熵效率指数（IEI）这一新型度量指标，用于量化已学习通信协议中消息熵与任务性能的比值。较低的IEI值代表更紧凑高效的消息表征。通过将IEI纳入训练损失函数，我们促使智能体发展出能以更优通信效率实现高性能的通信协议。基于多种MARL算法的广泛实验表明，我们的方法在提升通信效率的同时，能达到与基线方法相当或更优的任务性能。这些发现挑战了"性能提升需要复杂架构或增加通信开销"的普遍假设，凸显了在提升任务成功率与通信效率方面实现可扩展MAS的巨大潜力。