Algorithm Selection for Optimal Multi-Agent Path Finding via Graph Embedding

TL;DR

This paper introduces MAG, a novel algorithm selection method for MAPF problems using graph embeddings with FEATHER, demonstrating improved or comparable performance over existing approaches.

Contribution

The paper proposes a new graph-based encoding and a graph embedding approach for MAPF algorithm selection, advancing beyond prior hand-crafted or image-based methods.

Findings

MAG outperforms existing algorithm selection methods in experiments.

Graph embeddings effectively encode MAPF problems for algorithm selection.

The approach is scalable and adaptable to various MAPF scenarios.

Abstract

Multi-agent path finding (MAPF) is the problem of finding paths for multiple agents such that they do not collide. This problem manifests in numerous real-world applications such as controlling transportation robots in automated warehouses, moving characters in video games, and coordinating self-driving cars in intersections. Finding optimal solutions to MAPF is NP-Hard, yet modern optimal solvers can scale to hundreds of agents and even thousands in some cases. Different solvers employ different approaches, and there is no single state-of-the-art approach for all problems. Furthermore, there are no clear, provable, guidelines for choosing when each optimal MAPF solver to use. Prior work employed Algorithm Selection (AS) techniques to learn such guidelines from past data. A major challenge when employing AS for choosing an optimal MAPF algorithm is how to encode the given MAPF problem. Prior work either used hand-crafted features or an image representation of the problem. We explore graph-based encodings of the MAPF problem and show how they can be used on-the-fly with a modern graph embedding algorithm called FEATHER. Then, we show how this encoding can be effectively joined with existing encodings, resulting in a novel AS method we call MAPF Algorithm selection via Graph embedding (MAG). An extensive experimental evaluation of MAG on several MAPF algorithm selection tasks reveals that it is either on-par or significantly better than existing methods.