利用终身多代理寻路的经验
Leveraging Experience in Lifelong Multi-Agent Pathfinding
摘要：
在终身多智能体路径查找（L-MAPF）中，一组智能体执行由共享图上的智能体访问的多个位置组成的任务流，同时避免彼此冲突。L-MAPF通常通过将其划分为多个连续的，因此类似的“一次性”MAPF查询来处理，如滚动时域冲突解决（RHCR）算法。因此，对一个查询的解决方案通知下一个查询，这导致关于代理的开始和目标位置的相似性，以及从一个查询到下一个查询需要如何解决冲突。因此，解决一个MAPF查询的经验可以潜在地用于加速解决下一个。尽管有这种直觉，目前的L-MAPF规划器从头开始解决连续的MAPF查询。在本文中，我们介绍了一种新的RHCR启发的方法，称为exRHCR，利用其组成的MAPF查询的经验。特别地，exRHCR采用基于优先级的搜索（PBS）的扩展，这是最先进的MAPF求解器。我们称之为exPBS的扩展允许使用PBS在以前的MAPF实例中使用的代理之间的优先级来热启动搜索。我们的经验表明，exRHCR解决L-MAPF实例高达39%的速度比RHCR，并有可能增加系统吞吐量给定的任务流，通过增加代理的数量规划可以应付给定的时间预算。
英文摘要：
In Lifelong Multi-Agent Path Finding (L-MAPF) a team of agents performs a stream of tasks consisting of multiple locations to be visited by the agents on a shared graph while avoiding collisions with one another. L-MAPF is typically tackled by partitioning it into multiple consecutive, and hence similar, "one-shot" MAPF queries, as in the Rolling-Horizon Collision Resolution (RHCR) algorithm. Therefore, a solution to one query informs the next query, which leads to similarity with respect to the agents' start and goal positions, and how collisions need to be resolved from one query to the next. Thus, experience from solving one MAPF query can potentially be used to speedup solving the next one. Despite this intuition, current L-MAPF planners solve consecutive MAPF queries from scratch. In this paper, we introduce a new RHCR-inspired approach called exRHCR, which exploits experience in its constituent MAPF queries. In particular, exRHCR employs an extension of Priority-Based Search (PBS), a state-of-the-art MAPF solver. The extension, which we call exPBS, allows to warm-start the search with the priorities between agents used by PBS in the previous MAPF instances. We demonstrate empirically that exRHCR solves L-MAPF instances up to 39% faster than RHCR, and has the potential to increase system throughput for given task streams by increasing the number of agents a planner can cope with for a given time budget.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
