Online Guidance Graph Optimization for Lifelong Multi-Agent Path Finding

TL;DR

This paper introduces an optimized guidance policy for lifelong multi-agent path finding that adapts to real-time traffic, improving upon existing rule-based algorithms and handling dynamic task distributions.

Contribution

It proposes two novel pipelines to incorporate adaptive guidance into PIBT, enhancing solution quality and addressing dynamic task scenarios in lifelong MAPF.

Findings

Optimized guidance outperforms static and human-designed policies.

The approach effectively handles changing task distributions over time.

Guidance optimization improves pathfinding efficiency in real-time traffic conditions.

Abstract

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.