ICML 2025

HYGMA: Hypergraph Coordination Networks with Dynamic Grouping
for Multi-Agent Reinforcement Learning
Chiqiang Liu 1 Dazi Li 1
Abstract
Cooperative multi-agent reinforcement learning
faces significant challenges in effectively orga-
nizing agent relationships and facilitating infor-
mation exchange, particularly when agents need
to adapt their coordination patterns dynamically.
This paper presents a novel framework that in-
tegrates dynamic spectral clustering with hyper-
graph neural networks to enable adaptive group
formation and efficient information processing
in multi-agent systems.
The proposed frame-
work dynamically constructs and updates hyper-
graph structures through spectral clustering on
agents’ state histories, enabling higher-order rela-
tionships to emerge naturally from agent interac-
tions. 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 uni-
fied 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. The code is available at:
https://github.com/mysteryelder/HYGMA.
1. Introduction
Cooperative multi-agent reinforcement learning (MARL)
has emerged as a promising paradigm for addressing com-
plex real-world challenges that require coordinated decision-
making among multiple agents (Charbonnier et al., 2025;
Si et al., 2025). A fundamental challenge in MARL is the
1College of Information Science and Technology, Beijing Uni-
versity of Chemical Technology, Beijing, China. Correspondence
to: Dazi Li <lidz@mail.buct.edu.cn>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyri...