We study how to use guidance to improve the throughput of lifelong Multi-Agent Path Finding (MAPF). Previous studies have demonstrated that while incorporating guidance, such as highways, can accelerate MAPF algorithms, this often results in a trade-off with solution quality. In addition, how to generate good guidance automatically remains largely unexplored, with current methods falling short of surpassing manually designed ones. In this work, we introduce the directed guidance graph as a versatile representation of guidance for lifelong MAPF, framing Guidance Graph Optimization (GGO) as the task of optimizing its edge weights. We present two GGO algorithms to automatically generate guidance for arbitrary lifelong MAPF algorithms and maps. The first method directly solves GGO by employing CMA-ES, a black-box optimization algorithm. The second method, PIU, optimizes an update model capable of generating guidance, demonstrating the ability to transfer optimized guidance graphs to larger maps with similar layouts. Empirically, we show that (1) our guidance graphs improve the throughput of three representative lifelong MAPF algorithms in four benchmark maps, and (2) our update model can generate guidance graphs for as large as $93 \times 91$ maps and as many as 3000 agents.

翻译：我们研究了如何利用引导（guidance）来提升终身多智能体路径规划（MAPF）的吞吐量。先前研究表明，虽然引入引导（例如高速公路）可以加速MAPF算法，但这通常会导致与解质量的权衡。此外，如何自动生成高质量的引导仍是一个尚未充分探索的问题，现有方法未能超越人工设计的引导。本文提出将有向引导图（directed guidance graph）作为终身MAPF引导的通用表示，将引导图优化（GGO）定义为优化其边权重的任务。我们提出了两种GGO算法，能够针对任意终身MAPF算法和地图自动生成引导。第一种方法直接利用黑盒优化算法CMA-ES求解GGO；第二种方法PIU通过优化可生成引导的更新模型，展示了将优化的引导图迁移至布局相似的大型地图的能力。实验结果表明：（1）在四个基准地图上，我们的引导图提升了三种代表性终身MAPF算法的吞吐量；（2）我们的更新模型能够为多达$93 \times 91$的地图和3000个智能体生成引导图。