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：空间资源分配问题广泛存在于交通、工业及日常生活等多个领域。随着现实问题规模的持续扩大和对实时性需求的提升，传统算法面临巨大的计算压力，难以兼顾最优效率与实时响应能力。近年来，随着计算机算力的持续提升，强化学习在围棋、机器人等领域的显著成果证明了其强大的学习与序列决策能力。基于这些进展，涌现出大量利用强化学习解决空间资源分配问题的新方法。这类方法展现出解算快速收敛、模型泛化能力强等优势，为解决空间资源分配问题提供了全新视角。为此，本文旨在系统梳理近年来基于强化学习解决空间资源分配问题的理论方法与应用研究，并对其基本原理、相关方法论及应用研究进行总结与全面综述。最后，本文指出了该方向未来亟待关注的若干未解决的重要问题。