  Multi-Agent Path Finding (MAPF) finds conflict-free paths for multiple agents
from their respective start to goal locations. MAPF is challenging as the joint
configuration space grows exponentially with respect to the number of agents.
Among MAPF planners, search-based methods, such as CBS and M*, effectively
bypass the curse of dimensionality by employing a dynamically-coupled strategy:
agents are planned in a fully decoupled manner at first, where potential
conflicts between agents are ignored; and then agents either follow their
individual plans or are coupled together for planning to resolve the conflicts
between them. In general, the number of conflicts to be resolved decides the
run time of these planners and most of the existing work focuses on how to
efficiently resolve these conflicts. In this work, we take a different view and
aim to reduce the number of conflicts (and thus improve the overall search
efficiency) by improving each agent's individual plan. By leveraging a Visual
Transformer, we develop a learning-based single-agent planner, which plans for
a single agent while paying attention to both the structure of the map and
other agents with whom conflicts may happen. We then develop a novel
multi-agent planner called LM* by integrating this learning-based single-agent
planner with M*. Our results show that for both "seen" and "unseen" maps, in
comparison with M*, LM* has fewer conflicts to be resolved and thus, runs
faster and enjoys higher success rates. We empirically show that MAPF solutions
computed by LM* are near-optimal. Our code is available at
https://github.com/lakshayvirmani/learning-assisted-mstar .
