  Solving the Multi-Agent Path Finding (MAPF) problem optimally is known to be
NP-Hard for both make-span and total arrival time minimization. While many
algorithms have been developed to solve MAPF problems, there is no dominating
optimal MAPF algorithm that works well in all types of problems and no standard
guidelines for when to use which algorithm. In this work, we develop the deep
convolutional network MAPFAST (Multi-Agent Path Finding Algorithm SelecTor),
which takes a MAPF problem instance and attempts to select the fastest
algorithm to use from a portfolio of algorithms. We improve the performance of
our model by including single-agent shortest paths in the instance embedding
given to our model and by utilizing supplemental loss functions in addition to
a classification loss. We evaluate our model on a large and diverse dataset of
MAPF instances, showing that it outperforms all individual algorithms in its
portfolio as well as the state-of-the-art optimal MAPF algorithm selector. We
also provide an analysis of algorithm behavior in our dataset to gain a deeper
understanding of optimal MAPF algorithms' strengths and weaknesses to help
other researchers leverage different heuristics in algorithm designs.
