MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale
<|reference_start|>MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale: Multi-agent pathfinding (MAPF) is a challenging computational problem that typically requires to find collision-free paths for multiple agents in a shared environment. Solving MAPF optimally is NP-hard, yet efficient solutions are critical for numerous applications, including automated warehouses and transportation systems. Recently, learning-based approaches to MAPF have gained attention, particularly those leveraging deep reinforcement learning. Following current trends in machine learning, we have created a foundation model for the MAPF problems called MAPF-GPT. Using imitation learning, we have trained a policy on a set of pre-collected sub-optimal expert trajectories that can generate actions in conditions of partial observability without additional heuristics, reward functions, or communication with other agents. The resulting MAPF-GPT model demonstrates zero-shot learning abilities when solving the MAPF problem instances that were not present in the training dataset. We show that MAPF-GPT notably outperforms the current best-performing learnable-MAPF solvers on a diverse range of problem instances and is efficient in terms of computation (in the inference mode).<|reference_end|>
@article{andreychuk2024mapf-gpt:,
    title={MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale},
    author={Anton Andreychuk, Konstantin Yakovlev, Aleksandr Panov, Alexey
  Skrynnik},
    journal={arXiv preprint arXiv:2409.00134},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00134},
    primaryClass={cs.MA cs.AI cs.LG}
}