Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in large-scale multi-agent systems. State-of-the-art anytime MAPF is based on Large Neighborhood Search (LNS), where a fast initial solution is iteratively optimized by destroying and repairing a fixed number of parts, i.e., the neighborhood, of the solution, using randomized destroy heuristics and prioritized planning. Despite their recent success in various MAPF instances, current LNS-based approaches lack exploration and flexibility due to greedy optimization with a fixed neighborhood size which can lead to low quality solutions in general. So far, these limitations have been addressed with extensive prior effort in tuning or offline machine learning beyond actual planning. In this paper, we focus on online learning in LNS and propose Bandit-based Adaptive LArge Neighborhood search Combined with Exploration (BALANCE). BALANCE uses a bi-level multi-armed bandit scheme to adapt the selection of destroy heuristics and neighborhood sizes on the fly during search. We evaluate BALANCE on multiple maps from the MAPF benchmark set and empirically demonstrate cost improvements of at least 50% compared to state-of-the-art anytime MAPF in large-scale scenarios. We find that Thompson Sampling performs particularly well compared to alternative multi-armed bandit algorithms.

翻译：任意时刻多智能体路径规划（MAPF）是实现大规模多智能体系统可扩展路径优化的有效方法。当前最先进的任意时刻MAPF方法基于大邻域搜索（LNS），通过随机破坏启发式算法和优先级规划，对快速生成的初始解进行迭代优化——每次固定破坏并修复解结构中的若干部分（即邻域）。尽管该方法在多种MAPF场景中取得了成功，但其基于固定邻域大小的贪婪优化策略缺乏探索性和灵活性，易导致整体解质量偏低。目前，这些局限性主要通过规划之外的离线调参或机器学习方法解决。本文聚焦LNS中的在线学习，提出基于多臂赌博机的自适应大邻域搜索（BALANCE）。BALANCE采用双层多臂赌博机架构，在搜索过程中动态适应破坏启发式算法与邻域大小的选择。我们在MAPF基准测试集的多张地图上评估BALANCE，实验表明在大规模场景中，相较现有最先进任意时刻MAPF方法，BALANCE至少降低50%的路径成本。研究发现，与替代多臂赌博机算法相比，汤普森采样表现出更优性能。