用模仿学习训练大模型，高效解决多智能体路径规划问题
MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale
- 基于专家路径数据和Transformer架构，纯模仿学习构建地图规划基础模型
- 零样本泛化能力显著，对未见场景仍保持高成功率与低冲突率
- 无需额外启发式或通信机制，推理效率高，适合大规模部署
多智能体路径规划（MAPF）旨在为多个智能体在共享环境中寻找无碰撞路径。尽管在受限假设下求解最优解仍属NP难问题，但高效解决方案对自动化仓库、交通系统等应用至关重要。近年来，基于学习的方法受到关注，尤其是结合深度强化学习的方案。通常这类方法需附加单智能体规划或通信组件。本文提出仅依赖模仿学习的MAPF-GPT，利用大规模专家路径数据集与Transformer神经网络，构建一个无需额外启发式或通信机制的通用路径规划基础模型。MAPF-GPT具备零样本学习能力，可在训练中未出现的问题实例上有效运行。实验表明，其在多样化的测试场景中显著优于当前最优可学习型MAPF求解器，且推理过程计算高效。
原文摘要 · Abstract (English)
Multi-agent pathfinding (MAPF) is a problem that generally requires finding collision-free paths for multiple agents in a shared environment. Solving MAPF optimally, even under restrictive assumptions, is NP-hard, yet efficient solutions for this problem are critical for numerous applications, such as automated warehouses and transportation systems. Recently, learning-based approaches to MAPF have gained attention, particularly those leveraging deep reinforcement learning. Typically, such learning-based MAPF solvers are augmented with additional components like single-agent planning or communication. Orthogonally, in this work we rely solely on imitation learning that leverages a large dataset of expert MAPF solutions and transformer-based neural network to create a foundation model for MAPF called MAPF-GPT. The latter is capable of generating actions without additional heuristics or communication. MAPF-GPT demonstrates zero-shot learning abilities when solving the MAPF problems that are 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 computationally efficient during inference.
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。