Since more and more algorithms are proposed for multi-agent path finding (MAPF) and each of them has its strengths, choosing the correct one for a specific scenario that fulfills some specified requirements is an important task. Previous research in algorithm selection for MAPF built a standard workflow and showed that machine learning can help. In this paper, we study general solvers for MAPF, which further include suboptimal algorithms. We propose different groups of optimization objectives and learning tasks to handle the new tradeoff between runtime and solution quality. We conduct extensive experiments to show that the same loss can not be used for different groups of optimization objectives, and that standard computer vision models are no worse than customized architecture. We also provide insightful discussions on how feature-sensitive pre-processing is needed for learning for MAPF, and how different learning metrics are correlated to different learning tasks.

翻译：随着多智能体路径规划（MAPF）算法层出不穷，每种算法各有其优势，为满足特定需求的场景选择合适的算法成为重要任务。先前关于MAPF算法选择的研究构建了标准工作流程，并证明机器学习可发挥辅助作用。本文进一步研究包含次优算法在内的MAPF通用求解器。我们提出不同类别的优化目标与学习任务，以应对运行时间与解质量之间的新权衡。大量实验表明：同一损失函数不能适用于不同类别的优化目标，且标准计算机视觉模型的表现不逊于定制化架构。我们还就以下问题展开深刻讨论：MAPF学习为何需要特征敏感的预处理？不同学习指标如何与不同学习任务相关联？