HHC: Hierarchical Hypergraph Communication for Multi-Agent Systems

Cooperative multi-agent reinforcement learning ({MARL}) faces significant coordination challenges due to partial observability. Although communication can mitigate these issues, traditional methods often overlook the fact that agents can simultaneously belong to multiple collaborative groups, playing diverse roles. This limitation hinders the effective integration of tactical and strategic information. To address this, we propose Hierarchical Hypergraph Communication (HHC). In HHC, agents are modeled via a tactical-level hypergraph that supports overlapping multi-group memberships, facilitating the extraction of fine-grained tactical features. Simultaneously, these tactical hyperedges serve as virtual nodes to integrate and communicate strategic intents within a strategic-level hypergraph. This dual-layered architecture empowers agents to align individual actions with global strategies through a hierarchical communication process, thereby enhancing decision-making capabilities. Extensive experiments demonstrate the effectiveness of HHC in complex coordination tasks.