首个统一的中心化多智能体路径规划模型，能跨环境适配任意数量机器人。
RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding Across Different Environments and Tasks
- 用卷积网络构建地图驱动的统一策略，不依赖个体代理
- 在未训练过的地图和机器人数量下仍表现优异，零样本泛化能力强
- 适用于无人机编队、仓库调度等需动态适应的场景
多智能体路径规划（MAPF）旨在为多个机器人寻找无碰撞路径，广泛应用于空中编队与仓储自动化。由于该问题为NP难，基于学习的方法逐渐受到关注，尤其是利用深度神经网络的方案。然而，受智能体数量与地图规模变化影响，现有学习型规划器仍依赖去中心化策略。我们提出首个基于学习的中心化MAPF策略RAILGUN，其并非以智能体为中心，而是以地图为输入。通过卷积神经网络架构，RAILGUN可跨不同地图泛化并处理任意数量的智能体。我们使用规则方法生成轨迹进行监督训练。实验表明，RAILGUN优于多数基线方法，并在未见任务、地图和智能体数量上展现出出色的零样本泛化能力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。