# Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2506.20039 · HTML: https://mlsanity.com/paper/2506.20039/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 24 Jun 2025.

## Builds on (most cited first, 16 of 16)

- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [The StarCraft Multi-Agent Challenge](https://mlsanity.com/paper/1902.04043/) (2019)
- [Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://mlsanity.com/paper/1706.05296/) (2017)
- [RODE: Learning Roles to Decompose Multi-Agent Tasks](https://mlsanity.com/paper/2010.01523/) (2020)
- [Competing Bandits in Matching Markets](https://mlsanity.com/paper/1906.05363/) (2019)
- [Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.04222/) (2020)
- [Multi-Agent Common Knowledge Reinforcement Learning](https://mlsanity.com/paper/1810.11702/) (2018)
- [Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition](https://mlsanity.com/paper/2105.08692/) (2021)
- [Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers](https://mlsanity.com/paper/2212.11498/) (2022)
- [Multi-Agent Collaboration via Reward Attribution Decomposition](https://mlsanity.com/paper/2010.08531/) (2020)
- [Improved Bandits in Many-to-one Matching Markets with Incentive Compatibility](https://mlsanity.com/paper/2401.01528/) (2024)
- [Putting Gale & Shapley to Work: Guaranteeing Stability Through Learning](https://mlsanity.com/paper/2410.04376/) (2024)
- [Stable Matching with Uncertain Linear Preferences](https://mlsanity.com/paper/1607.02917/) (2016)
- [Field Deployment of Multi-Agent Reinforcement Learning Based Variable Speed Limit Controllers](https://mlsanity.com/paper/2407.08021/) (2024)

## Abstract (arXiv)

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.