提出新方法让多智能体各展所长，提升性能与多样性
Breaking the mold: The challenge of large scale MARL specialization
- 通过分阶段训练，让每个智能体基于自身优势进行专业化
- 个体性能提升13.2%，行为多样性增加14.9%（相比最先进方法）
- 适合追求个体能力优化的复杂多智能体系统研究
在多智能体学习中，主流方法侧重泛化能力，常忽略个体智能体的优化。这种对泛化的过度强调限制了智能体发挥自身优势，导致效率低下。本文提出比较优势最大化（CAM）方法，旨在提升多智能体系统中个体智能体的专业化水平。CAM采用两阶段流程：先进行集中式群体训练，再通过比较优势最大化实现个体专业化。实验表明，CAM在个体智能体性能上提升13.2%，行为多样性增加14.9%，显著优于现有先进系统。该结果凸显了个体专业化的重要性，为多智能体系统的发展提供了新方向。
原文摘要 · Abstract (English)
In multi-agent learning, the predominant approach focuses on generalization, often neglecting the optimization of individual agents. This emphasis on generalization limits the ability of agents to utilize their unique strengths, resulting in inefficiencies. This paper introduces Comparative Advantage Maximization (CAM), a method designed to enhance individual agent specialization in multiagent systems. CAM employs a two-phase process, combining centralized population training with individual specialization through comparative advantage maximization. CAM achieved a 13.2% improvement in individual agent performance and a 14.9% increase in behavioral diversity compared to state-of-the-art systems. The success of CAM highlights the importance of individual agent specialization, suggesting new directions for multi-agent system development.
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