The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Therefore, this paper aims to summarize and review recent theoretical methods and applied research utilizing reinforcement learning to address spatial resource allocation problems. It provides a summary and comprehensive overview of its fundamental principles, related methodologies, and applied research. Additionally, it highlights several unresolved issues that urgently require attention in this direction for the future.

翻译：空间资源分配问题在交通、工业及日常生活等多个领域普遍存在。随着现实问题规模持续扩大与实时性需求日益增长，传统算法面临显著计算压力，难以同时实现最优效率与实时响应能力。近年来，随着计算机算力的不断提升，强化学习在围棋和机器人等领域的突出成就展现了其强大的学习与序贯决策能力。基于这些进展，涌现出大量采用强化学习解决空间资源分配问题的新方法。这些方法具有求解收敛速度快、模型泛化能力强等优势，为空间资源分配问题提供了全新的解决视角。为此，本文旨在系统梳理并评述近期利用强化学习解决空间资源分配问题的理论方法与应用研究，对其基本原理、相关方法论及应用研究进行总结与全面综述，同时指出该方向未来亟待解决的若干关键问题。