# HYGMA: Hypergraph Coordination Networks with Dynamic Grouping for Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2505.07207 · HTML: https://mlsanity.com/paper/2505.07207/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2025.

## Built on by (most cited first, 3 of 3)

- [Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2507.23604/) (2025)
- [Breakthrough the Suboptimal Stable Point in Value-Factorization-Based Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2604.05297/) (2026)
- [When Does Hierarchy Help? Benchmarking Agent Coordination in Event-Driven Industrial Scheduling](https://mlsanity.com/paper/2605.13172/) (2026)

## Abstract (arXiv)

Cooperative multi-agent reinforcement learning faces significant challenges in effectively organizing agent relationships and facilitating information exchange, particularly when agents need to adapt their coordination patterns dynamically. This paper presents a novel framework that integrates dynamic spectral clustering with hypergraph neural networks to enable adaptive group formation and efficient information processing in multi-agent systems. The proposed framework dynamically constructs and updates hypergraph structures through spectral clustering on agents' state histories, enabling higher-order relationships to emerge naturally from agent interactions. The hypergraph structure is enhanced with attention mechanisms for selective information processing, providing an expressive and efficient way to model complex agent relationships. This architecture can be implemented in both value-based and policy-based paradigms through a unified objective combining task performance with structural regularization. Extensive experiments on challenging cooperative tasks demonstrate that our method significantly outperforms state-of-the-art approaches in both sample efficiency and final performance.