20 citations · 51 across the 11 of their papers we have counts for
4 papers · 1 filter
Online Guidance Graph Optimization for Lifelong Multi-Agent Path Finding
Hongzhi Zang, Yulun Zhang, He Jiang +4
We study the problem of optimizing a guidance policy capable of dynamically guiding the agents for lifelong Multi-Agent Path Finding based on real-time traffic patterns. Multi-Agen…
Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding
He Jiang, Yutong Wang, Rishi Veerapaneni +3
Lifelong Multi-Agent Path Finding (LMAPF) repeatedly finds collision-free paths for multiple agents that are continually assigned new goals when they reach current ones. Recently,…
Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities
He Jiang, Yulun Zhang, Rishi Veerapaneni +1
Multi-Agent Path Finding (MAPF) is the problem of moving multiple agents from starts to goals without collisions. Lifelong MAPF (LMAPF) extends MAPF by continuously assigning new g…
Guidance Graph Optimization for Lifelong Multi-Agent Path Finding
Yulun Zhang, He Jiang, Varun Bhatt +2
We study how to use guidance to improve the throughput of lifelong Multi-Agent Path Finding (MAPF). Previous studies have demonstrated that, while incorporating guidance, such as h…