Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911021273186304 |
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| author | Moslemi, Koorosh Lee, Chi-Guhn |
| author_facet | Moslemi, Koorosh Lee, Chi-Guhn |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20039 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning Moslemi, Koorosh Lee, Chi-Guhn Multiagent Systems Artificial Intelligence Computer Science and Game Theory Machine Learning 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. |
| title | Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning |
| topic | Multiagent Systems Artificial Intelligence Computer Science and Game Theory Machine Learning |
| url | https://arxiv.org/abs/2506.20039 |