Abstract: Multi-agent path finding is a computationally challenging problem that is relevant to many areas in robotics. Experience-based planning methods have been shown to significantly reduce the planning time of this problem, but the type of problem in which experience can be used has so far been limited to warehouse-like environments with ample open space. We present an experience-based multi-agent path finding algorithm that specifically addresses narrow corridors of width 1 (also known as doorways). This expands the domain of experience-based problems to include environments such as most houses, office spaces, retail spaces, and hospitals. We also present novel techniques for conflict resolution strategies that result in up to a $94\%$ decrease in waiting steps per robot and final paths closer to the optimal decoupled path by up to $71\%$ than the strategies used in current experience-based methods. We demonstrate our planner solving problems with hundreds of robots in congested environments in seconds, finding solutions in an allotted time more often than existing state of the art optimal methods.
