  In a multi-agent pathfinding (MAPF) problem, agents need to navigate from
their start to their goal locations without colliding into each other. There
are various MAPF algorithms, including Windowed Hierarchical Cooperative A*,
Flow Annotated Replanning, and Bounded Multi-Agent A*. It is often the case
that there is no a single algorithm that dominates all MAPF instances.
Therefore, in this paper, we investigate the use of deep learning to
automatically select the best MAPF algorithm from a portfolio of algorithms for
a given MAPF problem instance. Empirical results show that our automatic
algorithm selection approach, which uses an off-the-shelf convolutional neural
network, is able to outperform any individual MAPF algorithm in our portfolio.
