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.

本文介绍了一种名为 HiMAP 的新型可扩展方法，应用了带启发式指导的模仿学习，并在分散化方式下进行训练，展示了在仅限于模仿学习的大规模多智能体路径规划领域能够取得竞争性的成功率和可扩展性结果，显示了模仿学习在路径规划领域的潜力。