Multi-Agent Path Finding (MAPF) aims to move agents from their start to goal vertices on a graph. Lifelong MAPF (LMAPF) continuously assigns new goals to agents as they complete current ones. To guide agents' movement in LMAPF, prior works have proposed Guidance Graph Optimization (GGO) methods to optimize a guidance graph, which is a bidirected weighted graph whose directed edges represent moving and waiting actions with edge weights being action costs. Higher edge weights represent higher action costs. However, edge weights only provide soft guidance. An edge with a high weight only discourages agents from using it, instead of prohibiting agents from traversing it. In this paper, we explore the need to incorporate edge directions optimization into GGO, providing strict guidance. We generalize GGO to Mixed Guidance Graph Optimization (MGGO), presenting two MGGO methods capable of optimizing both edge weights and directions. The first optimizes edge directions and edge weights in two phases separately. The second applies Quality Diversity algorithms to optimize a neural network capable of generating edge directions and weights. We also incorporate traffic patterns relevant to edge directions into a GGO method, making it capable of generating edge-direction-aware guidance graphs.

翻译：多智能体路径规划（MAPF）旨在将智能体从起点移动到图上的目标顶点。终身MAPF（LMAPF）在智能体完成当前目标后持续为其分配新目标。为引导LMAPF中智能体的移动，先前研究提出了引导图优化（GGO）方法，该方法优化的引导图是一个双向加权图，其有向边表示移动与等待动作，边权重代表动作成本。较高的边权重对应较高的动作成本。然而，边权重仅提供软性引导——高权重边仅会降低智能体使用该边的倾向，而非禁止智能体通过。本文探讨了将边方向优化纳入GGO以提供严格引导的必要性。我们将GGO推广至混合引导图优化（MGGO），提出两种能同时优化边权重与方向的MGGO方法：第一种采用两阶段分别优化边方向与边权重；第二种应用质量多样性算法优化能够生成边方向与权重的神经网络。我们还将与边方向相关的交通模式整合至GGO方法中，使其能够生成具备边方向感知能力的引导图。