  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.
