Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding

Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding

Keisuke Okumura

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Main Track. Pages 243-251. https://doi.org/10.24963/ijcai.2023/28

This study extends the recently-developed LaCAM algorithm for multi-agent pathfinding (MAPF). LaCAM is a sub-optimal search-based algorithm that uses lazy successor generation to dramatically reduce the planning effort. We present two enhancements. First, we propose its anytime version, called LaCAM*, which eventually converges to optima, provided that solution costs are accumulated transition costs. Second, we improve the successor generation to quickly obtain initial solutions. Exhaustive experiments demonstrate their utility. For instance, LaCAM* sub-optimally solved 99% of the instances retrieved from the MAPF benchmark, where the number of agents varied up to a thousand, within ten seconds on a standard desktop PC, while ensuring eventual convergence to optima; developing a new horizon of MAPF algorithms.
Keywords:
Agent-based and Multi-agent Systems: MAS: Multi-agent planning
Planning and Scheduling: PS: Distributed and multi-agent planning
Robotics: ROB: Motion and path planning
Planning and Scheduling: PS: Planning algorithms