Caching-Augmented Lifelong Multi-Agent Path Finding
<|reference_start|>Caching-Augmented Lifelong Multi-Agent Path Finding: Multi-Agent Path Finding (MAPF), which involves finding collision-free paths for multiple robots, is crucial in various applications. Lifelong MAPF, where targets are reassigned to agents as soon as they complete their initial targets, offers a more accurate approximation of real-world warehouse planning. In this paper, we present a novel mechanism named Caching-Augmented Lifelong MAPF (CAL-MAPF), designed to improve the performance of Lifelong MAPF. We have developed a new type of map grid called cache for temporary item storage and replacement, and created a locking mechanism to improve the planning solution's stability. A task assigner (TA) is designed for CAL-MAPF to allocate target locations to agents and control agent status in different situations. CAL-MAPF has been evaluated using various cache replacement policies and input task distributions. We have identified three main factors significantly impacting CAL-MAPF performance through experimentation: suitable input task distribution, high cache hit rate, and smooth traffic. In general, CAL-MAPF has demonstrated potential for performance improvements in certain task distributions, map and agent configurations.<|reference_end|>
@article{tang2024caching-augmented,
    title={Caching-Augmented Lifelong Multi-Agent Path Finding},
    author={Yimin Tang, Zhenghong Yu, Yi Zheng, T. K. Satish Kumar, Jiaoyang Li,
  Sven Koenig},
    journal={arXiv preprint arXiv:2403.13421},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13421},
    primaryClass={cs.RO cs.AI cs.MA}
}