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arXiv · 2506.23793

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

Anton Andreychuk·Konstantin Yakovlev·Aleksandr Panov·Alexey Skrynnik
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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.

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Anton Andreychuk, Konstantin Yakovlev, Aleksandr Panov, Alexey Skrynnik. 2025-06-30. Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuning. https://arxiv.org/abs/2506.23793

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