Multi-Agent Path Finding (MAPF) is a representative multi-agent coordination problem, where multiple agents are required to navigate to their respective goals without collisions. Solving MAPF optimally is known to be NP-hard, leading to the adoption of learning-based approaches to alleviate the online computational burden. Prevailing approaches, such as Graph Neural Networks (GNNs), are typically constrained to pairwise message passing between agents. However, this limitation leads to suboptimal behaviours and critical issues, such as attention dilution, particularly in dense environments where group (i.e. beyond just two agents) coordination is most critical. Despite the importance of such higher-order interactions, existing approaches have not been able to fully explore them. To address this representational bottleneck, we introduce HMAGAT (Hypergraph Multi-Agent Attention Network), a novel architecture that leverages attentional mechanisms over directed hypergraphs to explicitly capture group dynamics. Empirically, HMAGAT establishes a new state-of-the-art among learning-based MAPF solvers: e.g., despite having just 1M parameters and being trained on 100$\times$ less data, it outperforms the current SoTA 85M parameter model. Through detailed analysis of HMAGAT's attention values, we demonstrate how hypergraph representations mitigate the attention dilution inherent in GNNs and capture complex interactions where pairwise methods fail. Our results illustrate that appropriate inductive biases are often more critical than the training data size or sheer parameter count for multi-agent problems.

翻译：多智能体路径规划（MAPF）是一个典型的多智能体协同问题，其要求多个智能体在无碰撞条件下导航至各自目标。已知最优求解MAPF属于NP难问题，因此学界采用基于学习的方法以减轻在线计算负担。主流方法（如图神经网络）通常局限于智能体间的成对消息传递。然而，这种限制会导致次优行为与关键问题（如注意力稀释），在密集环境中尤为突出——此时群体（即超越双智能体）协同至关重要。尽管此类高阶交互具有重要意义，现有方法尚未能充分探索其潜力。为突破这一表征瓶颈，我们提出HMAGAT（超图多智能体注意力网络），该新型架构利用有向超图上的注意力机制显式捕获群体动态。实验表明，HMAGAT在学习型MAPF求解器中确立了新的性能标杆：例如，尽管仅包含100万参数且训练数据量减少100倍，其性能仍超越当前拥有8500万参数的SOTA模型。通过对HMAGAT注意力值的详细分析，我们论证了超图表征如何缓解GNN固有的注意力稀释问题，并捕获成对方法无法处理的复杂交互。研究结果表明，对于多智能体问题，恰当的归纳偏置往往比训练数据规模或单纯参数数量更为关键。