Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning

Koorosh Moslemi; Chi-Guhn Lee

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arXiv · 2506.20039

Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning

Koorosh Moslemi·Chi-Guhn Lee

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Abstract

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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Koorosh Moslemi, Chi-Guhn Lee. 2025-06-24. Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning. https://arxiv.org/abs/2506.20039

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