Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuning

TL;DR

This paper introduces MAPF-GPT-DDG, a fine-tuned learning-based multi-agent pathfinding solver that significantly improves solution quality and scalability, handling up to one million agents in complex environments.

Contribution

It presents a novel fine-tuning method with delta-data generation that enhances a pre-trained MAPF model's performance and scalability beyond existing solutions.

Findings

Outperforms all existing learning-based MAPF solvers.

Handles environments with up to 1 million agents.

Achieves significant improvements in solution quality.

Abstract

Multi-agent pathfinding (MAPF) is a common abstraction of multi-robot trajectory planning problems, where multiple homogeneous robots simultaneously move in the shared environment. While solving MAPF optimally has been proven to be NP-hard, scalable, and efficient, solvers are vital for real-world applications like logistics, search-and-rescue, etc. To this end, decentralized suboptimal MAPF solvers that leverage machine learning have come on stage. Building on the success of the recently introduced MAPF-GPT, a pure imitation learning solver, we introduce MAPF-GPT-DDG. This novel approach effectively fine-tunes the pre-trained MAPF model using centralized expert data. Leveraging a novel delta-data generation mechanism, MAPF-GPT-DDG accelerates training while significantly improving performance at test time. Our experiments demonstrate that MAPF-GPT-DDG surpasses all existing learning-based MAPF solvers, including the original MAPF-GPT, regarding solution quality across many testing scenarios. Remarkably, it can work with MAPF instances involving up to 1 million agents in a single environment, setting a new milestone for scalability in MAPF domains.