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

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. The code is available at: https://github.com/mysteryelder/HYGMA.

Teaching agents to work together as a team is challenging. Imagine coaching a soccer team where players need to figure out who should pass to whom or when to form a defensive line. That's similar to what we're trying to solve in AI. We've created a new approach that helps computer programs learn to collaborate more effectively. Instead of making all AI agents talk to everyone else (which gets chaotic) or putting them in permanent teams (which isn't flexible), our method lets them figure out their own groupings based on what they're doing. It's like how soccer players naturally form small groups during a game - defenders work together, forwards coordinate, but these groups change as the game evolves. Our system uses a clever math technique to spot these natural groupings as the agents interact. When we tested our approach in different team challenges, the agents learned to coordinate much better and faster than with existing methods. They could adapt their teamwork patterns when needed, just like good human teams do.