HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding
<|reference_start|>HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding: Large-scale multi-agent pathfinding (MAPF) presents significant challenges in several areas. As systems grow in complexity with a multitude of autonomous agents operating simultaneously, efficient and collision-free coordination becomes paramount. Traditional algorithms often fall short in scalability, especially in intricate scenarios. Reinforcement Learning (RL) has shown potential to address the intricacies of MAPF; however, it has also been shown to struggle with scalability, demanding intricate implementation, lengthy training, and often exhibiting unstable convergence, limiting its practical application. In this paper, we introduce Heuristics-Informed Multi-Agent Pathfinding (HiMAP), a novel scalable approach that employs imitation learning with heuristic guidance in a decentralized manner. We train on small-scale instances using a heuristic policy as a teacher that maps each single agent observation information to an action probability distribution. During pathfinding, we adopt several inference techniques to improve performance. With a simple training scheme and implementation, HiMAP demonstrates competitive results in terms of success rate and scalability in the field of imitation-learning-only MAPF, showing the potential of imitation-learning-only MAPF equipped with inference techniques.<|reference_end|>
@article{tang2024himap:,
    title={HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent
  Pathfinding},
    author={Huijie Tang, Federico Berto, Zihan Ma, Chuanbo Hua, Kyuree Ahn,
  Jinkyoo Park},
    journal={arXiv preprint arXiv:2402.15546},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15546},
    primaryClass={cs.MA cs.AI cs.LG cs.RO}
}