提出双向团队形成框架，提升动态多智能体系统协作性能。
Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning
- 设计双向团队匹配机制，智能体可双向选择合作对象。
- 在多数场景中表现优于传统单向组队，泛化能力更强。
- 适合动态环境下的多智能体协作研究者参考。
团队形成与基于团队的学习动态在多智能体强化学习（MARL）中备受关注。然而，现有研究主要聚焦于单向分组、预定义团队或固定规模设置，对算法层面的双向组队选择在动态群体中的影响仍探讨不足。为此，本文提出一种面向动态多智能体系统的双向团队形成学习框架。通过该研究，揭示了双向组队机制中哪些算法特性会影响策略性能与泛化能力。我们在广泛采用的多智能体场景中验证了该方法，结果表明其在多数场景中具备竞争力的表现和更优的泛化性。
原文摘要 · Abstract (English)
Team formation and the dynamics of team-based learning have drawn significant interest in the context of Multi-Agent Reinforcement Learning (MARL). However, existing studies primarily focus on unilateral groupings, predefined teams, or fixed-population settings, leaving the effects of algorithmic bilateral grouping choices in dynamic populations underexplored. To address this gap, we introduce a framework for learning two-sided team formation in dynamic multi-agent systems. Through this study, we gain insight into what algorithmic properties in bilateral team formation influence policy performance and generalization. We validate our approach using widely adopted multi-agent scenarios, demonstrating competitive performance and improved generalization in most scenarios.
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