Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding
arXiv:2602.23468v1 Announce Type: cross 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
arXiv:2602.23468v1 Announce Type: cross 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.
Executive Summary
This article proposes Mixed Guidance Graph Optimization (MGGO), an extension of Guidance Graph Optimization (GGO) methods, to optimize both edge weights and directions for Lifelong Multi-Agent Path Finding (LMAPF). The authors present two MGGO methods: a two-phase approach and a Quality Diversity algorithm-based method. They also incorporate traffic patterns into a GGO method to generate edge-direction-aware guidance graphs. The proposed methods provide strict guidance, improving the efficiency and effectiveness of LMAPF. The article highlights the importance of considering edge directions in guidance graph optimization, leading to improved agent movement and reduced travel costs.
Key Points
- ▸ MGGO optimizes edge weights and directions for LMAPF
- ▸ Two MGGO methods are presented: a two-phase approach and a Quality Diversity algorithm-based method
- ▸ Traffic patterns are incorporated into a GGO method to generate edge-direction-aware guidance graphs
Merits
Strength in addressing edge direction optimization
The authors address a crucial limitation of prior GGO methods by incorporating edge direction optimization, providing strict guidance for LMAPF.
Improved agent movement and reduced travel costs
The proposed MGGO methods lead to improved agent movement and reduced travel costs, enhancing the efficiency and effectiveness of LMAPF.
Demerits
Potential computational complexity
The two-phase MGGO method may suffer from high computational complexity due to the separate optimization of edge directions and weights.
Limited evaluation of edge-direction-aware guidance graphs
The article primarily focuses on the MGGO methods, and the evaluation of edge-direction-aware guidance graphs is limited, requiring further investigation.
Expert Commentary
The article presents a significant contribution to the field of LMAPF by addressing the limitation of prior GGO methods. The proposed MGGO methods provide strict guidance, improving agent movement and reducing travel costs. However, the computational complexity of the two-phase MGGO method and the limited evaluation of edge-direction-aware guidance graphs are areas that require further investigation. The article's findings have practical implications for real-world applications and inform policy decisions in areas like urban planning and traffic management. Future research should explore the generalizability of MGGO methods to various domains and the development of more efficient optimization algorithms.
Recommendations
- ✓ Further investigation is needed to optimize the computational complexity of the two-phase MGGO method
- ✓ Evaluation of edge-direction-aware guidance graphs should be expanded to explore their potential benefits and limitations