  Many multi-robot planning problems are burdened by the curse of
dimensionality, which compounds the difficulty of applying solutions to
large-scale problem instances. The use of learning-based methods in multi-robot
planning holds great promise as it enables us to offload the online
computational burden of expensive, yet optimal solvers, to an offline learning
procedure. Simply put, the idea is to train a policy to copy an optimal pattern
generated by a small-scale system, and then transfer that policy to much larger
systems, in the hope that the learned strategy scales, while maintaining
near-optimal performance. Yet, a number of issues impede us from leveraging
this idea to its full potential. This blue-sky paper elaborates some of the key
challenges that remain.
