Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding

2026-02-26

AI Summary

This work addresses the limitations of existing guidance graphs in lifelong multi-agent path planning, which rely solely on edge weights to provide soft guidance and thus fail to effectively constrain agent behaviors, ultimately hindering pathfinding efficiency. To overcome this, the authors propose Mixed Guidance Graph Optimization (MGGO), a novel approach that, for the first time, incorporates edge direction into guidance graph design. MGGO employs a two-stage optimization process to jointly learn edge directions and weights, integrates quality-diversity algorithms to generate diverse high-performing neural network policies, and further enhances directional awareness by incorporating traffic pattern information. Experimental results demonstrate that MGGO significantly improves system throughput and path planning efficiency compared to existing methods.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Multi-Agent Path Finding (MAPF) aims to move agents from their start to goal vertices on a graph. Lifelong MAPF (LMAPF) continuously assigns new goals to agents as they complete current ones. To guide agents'movement in LMAPF, prior works have proposed Guidance Graph Optimization (GGO) methods to optimize a guidance graph, which is a bidirected weighted graph whose directed edges represent moving and waiting actions with edge weights being action costs. Higher edge weights represent higher action costs. However, edge weights only provide soft guidance. An edge with a high weight only discourages agents from using it, instead of prohibiting agents from traversing it. In this paper, we explore the need to incorporate edge directions optimization into GGO, providing strict guidance. We generalize GGO to Mixed Guidance Graph Optimization (MGGO), presenting two MGGO methods capable of optimizing both edge weights and directions. The first optimizes edge directions and edge weights in two phases separately. The second applies Quality Diversity algorithms to optimize a neural network capable of generating edge directions and weights. We also incorporate traffic patterns relevant to edge directions into a GGO method, making it capable of generating edge-direction-aware guidance graphs.

Problem

• Multi-Agent Path Finding
• Lifelong MAPF
• Guidance Graph Optimization
• Edge Direction
• Edge Weight

Innovation

• Mixed Guidance Graph Optimization
• Edge Direction Optimization
• Lifelong Multi-Agent Path Finding
• Quality Diversity
• Guidance Graph