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

• URL: http://arxiv.org/abs/2408.02960v1
• Date: Tue, 6 Aug 2024 05:15:35 GMT
• Title: Anytime Multi-Agent Path Finding with an Adaptive Delay-Based Heuristic
• Authors: Thomy Phan, Benran Zhang, Shao-Hung Chan, Sven Koenig,
• Abstract summary: We propose Adaptive Delay-based Destroy-and-Repair with Enhanced Success-based Self-Learning (ADDRESS) as a single-destroy-heuristic variant of MAPF-LNS. We demonstrate cost improvements by at least 50% in large-scale scenarios with up to a thousand agents, compared with the original MAPF-LNS.
• Score: 16.4408116214332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anytime multi-agent path finding (MAPF) is a promising approach to scalable 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 non-adaptive, any performance bottleneck caused by these heuristics cannot be overcome via adaptive heuristic selection alone, thus limiting the overall effectiveness of MAPF-LNS in terms of solution cost. 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.

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