Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding

TL;DR

This paper enhances the LaCAM algorithm for multi-agent pathfinding by introducing an anytime version that converges to optimal solutions and improves initial solution generation, demonstrating high efficiency on large benchmarks.

Contribution

It introduces LaCAM*, an anytime extension of LaCAM, and improves successor generation for faster initial solutions, advancing scalable MAPF algorithms.

Findings

LaCAM* solves 99% of benchmark instances within ten seconds.

LaCAM* converges to optimal solutions over time.

The methods scale to thousands of agents.

Abstract

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.