Multi-Agent Path Finding (MAPF), which focuses on finding collision-free paths for multiple robots, is crucial for applications ranging from aerial swarms to warehouse automation. Solving MAPF is NP-hard so learning-based approaches for MAPF have gained attention, particularly those leveraging deep neural networks. Nonetheless, despite the community's continued efforts, all learning-based MAPF planners still rely on decentralized planning due to variability in the number of agents and map sizes. We have developed the first centralized learning-based policy for MAPF problem called RAILGUN. RAILGUN is not an agent-based policy but a map-based policy. By leveraging a CNN-based architecture, RAILGUN can generalize across different maps and handle any number of agents. We collect trajectories from rule-based methods to train our model in a supervised way. In experiments, RAILGUN outperforms most baseline methods and demonstrates great zero-shot generalization capabilities on various tasks, maps and agent numbers that were not seen in the training dataset.

翻译：多智能体路径规划（MAPF）专注于为多个机器人寻找无碰撞路径，其应用范围从无人机集群到仓库自动化都至关重要。求解MAPF是NP难问题，因此基于学习的方法，特别是那些利用深度神经网络的方法，已受到关注。然而，尽管学界持续努力，由于智能体数量和地图尺寸的可变性，所有基于学习的MAPF规划器仍依赖于分散式规划。我们开发了首个用于MAPF问题的集中式学习策略，称为RAILGUN。RAILGUN并非基于智能体的策略，而是基于地图的策略。通过利用基于CNN的架构，RAILGUN能够泛化到不同的地图，并处理任意数量的智能体。我们收集基于规则方法产生的轨迹，以监督方式训练我们的模型。在实验中，RAILGUN在大多数基线方法上表现更优，并在训练数据集中未见过的各种任务、地图和智能体数量上展现出强大的零样本泛化能力。