Anytime Multi-Agent Path Finding with an Adaptive Delay-Based Heuristic

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Phan, Thomy ; Zhang, Benran ; Chan, Shao-Hung ; Koenig, Sven:
Anytime Multi-Agent Path Finding with an Adaptive Delay-Based Heuristic.
In: Proceedings of the AAAI Conference on Artificial Intelligence. Bd. 39 (2025) Heft 22 . - S. 23286-23294.
ISSN 2159-5399
DOI: https://doi.org/10.1609/aaai.v39i22.34495

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Abstract

Anytime multi-agent path finding (MAPF) is a promising approach to scalable and collision-free path optimization in multi-agent systems. MAPF-LNS, based on Large Neighborhood Search (LNS), is the current state-of-the-art approach where a fast initial solution is iteratively optimized by destroying and repairing selected paths of the solution. Current MAPF-LNS variants commonly use an adaptive selection mechanism to choose among multiple destroy heuristics. However, to determine promising destroy heuristics, MAPF-LNS requires a considerable amount of exploration time. As common destroy heuristics are stationary, i.e., non-adaptive, any performance bottleneck caused by them cannot be overcome by adaptive heuristic selection alone, thus limiting the overall effectiveness of MAPF-LNS. In this paper, we propose Adaptive Delay-based Destroy-and-Repair Enhanced with Success-based Self-learning (ADDRESS) as a single-destroy-heuristic variant of MAPF-LNS. ADDRESS applies restricted Thompson Sampling to the top-K set of the most delayed agents to select a seed agent for adaptive LNS neighborhood generation. We evaluate ADDRESS in multiple maps from the MAPF benchmark set and demonstrate cost improvements by at least 50% in large-scale scenarios with up to a thousand agents, compared with the original MAPF-LNS and other state-of-the-art methods.