# Negotiating Team Formation Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2010.10380 · HTML: https://mlsanity.com/paper/2010.10380/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 13 of 13)

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- [Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming](https://mlsanity.com/paper/2110.12929/) (2021)
- [Coalitional Bargaining via Reinforcement Learning: An Application to Collaborative Vehicle Routing](https://mlsanity.com/paper/2310.17458/) (2023)
- [Using Cooperative Game Theory to Prune Neural Networks](https://mlsanity.com/paper/2311.10468/) (2023)
- [Rethinking Teacher-Student Curriculum Learning through the Cooperative Mechanics of Experience](https://mlsanity.com/paper/2404.03084/) (2024)
- [Differentially Private Reinforcement Learning with Self-Play](https://mlsanity.com/paper/2404.07559/) (2024)
- [Markovian Embeddings for Coalitional Bargaining Games](https://mlsanity.com/paper/2306.11104/) (2023)
- [RobEthiChor: Automated Context-aware Ethics-based Negotiation for Autonomous Robots](https://mlsanity.com/paper/2507.22664/) (2025)
- [Beyond the high score: Prosocial ability profiles of multi-agent populations](https://mlsanity.com/paper/2509.14485/) (2025)

## Abstract (arXiv)

When autonomous agents interact in the same environment, they must often cooperate to achieve their goals. One way for agents to cooperate effectively is to form a team, make a binding agreement on a joint plan, and execute it. However, when agents are self-interested, the gains from team formation must be allocated appropriately to incentivize agreement. Various approaches for multi-agent negotiation have been proposed, but typically only work for particular negotiation protocols. More general methods usually require human input or domain-specific data, and so do not scale. To address this, we propose a framework for training agents to negotiate and form teams using deep reinforcement learning. Importantly, our method makes no assumptions about the specific negotiation protocol, and is instead completely experience driven. We evaluate our approach on both non-spatial and spatially extended team-formation negotiation environments, demonstrating that our agents beat hand-crafted bots and reach negotiation outcomes consistent with fair solutions predicted by cooperative game theory. Additionally, we investigate how the physical location of agents influences negotiation outcomes.