Improved Anonymous Multi-Agent Path Finding Algorithm

We consider the Anonymous Multi-Agent Path-Finding (AMAPF) problem where the agents are confined to a graph, a set of goal vertices is given, and each of these vertices has to be reached by some agent. The problem is to find an assignment of the goals to the agents as well as the collision-free paths, and we seek to find the solution with the minimal makespan. A well-established approach to solving this problem is by reducing it to a special type of graph search problem, i.e. to the problem of finding a maximum flow on an auxiliary graph induced by the input one. The size of the former graph may be very large, and the search on it may become a bottleneck. To this end, we suggest a specific search algorithm that leverages the idea of exploring the search space not through considering separate search states but rather bulks of them simultaneously. That is, we implicitly compress, store, and expand bulks of the search states as single states, reducing the runtime and memory consumption. Empirically, the resultant AMAPF solver demonstrates superior performance compared to the state-of-the-art competitor and is able to solve all publicly available MAPF instances from the wellknown MovingAI benchmark in less than 30 seconds.