Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning
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.
Cite
@article{arxiv.2506.20039,
title = {Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning},
author = {Koorosh Moslemi and Chi-Guhn Lee},
journal= {arXiv preprint arXiv:2506.20039},
year = {2025}
}
Comments
Accepted to the 2nd Coordination and Cooperation in Multi-Agent Reinforcement Learning (CoCoMARL) Workshop at RLC 2025