# Online Guidance Graph Optimization for Lifelong Multi-Agent Path Finding

arXiv: https://arxiv.org/abs/2411.16506 · HTML: https://mlsanity.com/paper/2411.16506/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2024.

## Built on by (most cited first, 6 of 6)

- [RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding Across Different Environments and Tasks](https://mlsanity.com/paper/2503.02992/) (2025)
- [DeepFleet: Multi-Agent Foundation Models for Mobile Robots](https://mlsanity.com/paper/2508.08574/) (2025)
- [Destination-to-Chutes Task Mapping Optimization for Multi-Robot Coordination in Robotic Sorting Systems](https://mlsanity.com/paper/2510.03472/) (2025)
- [GRAND: Guidance, Rebalancing, and Assignment for Networked Dispatch in Multi-Agent Path Finding](https://mlsanity.com/paper/2512.03194/) (2025)
- [A Lightweight Traffic Map for Efficient Anytime LaCAM*](https://mlsanity.com/paper/2603.07891/) (2026)
- [Reinforcement Learning for Large-Scale Lifelong Multi-Agent Path Finding with Shared Spatial Fields](https://mlsanity.com/paper/2610.18186/) (2026)

## Abstract (arXiv)

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-Agent Path Finding (MAPF) focuses on moving multiple agents from their starts to goals without collisions. Its lifelong variant, LMAPF, continuously assigns new goals to agents. In this work, we focus on improving the solution quality of PIBT, a state-of-the-art rule-based LMAPF algorithm, by optimizing a policy to generate adaptive guidance. We design two pipelines to incorporate guidance in PIBT in two different ways. We demonstrate the superiority of the optimized policy over both static guidance and human-designed policies. Additionally, we explore scenarios where task distribution changes over time, a challenging yet common situation in real-world applications that is rarely explored in the literature.