Public resource allocation involves the efficient distribution of resources, including urban infrastructure, energy, and transportation, to effectively meet societal demands. However, existing methods focus on optimizing the movement of individual resources independently, without considering their capacity constraints. To address this limitation, we propose a novel and more practical problem: Collaborative Public Resource Allocation (CPRA), which explicitly incorporates capacity constraints and spatio-temporal dynamics in real-world scenarios. We propose a new framework called Game-Theoretic Spatio-Temporal Reinforcement Learning (GSTRL) for solving CPRA. Our contributions are twofold: 1) We formulate the CPRA problem as a potential game and demonstrate that there is no gap between the potential function and the optimal target, laying a solid theoretical foundation for approximating the Nash equilibrium of this NP-hard problem; and 2) Our designed GSTRL framework effectively captures the spatio-temporal dynamics of the overall system. We evaluate GSTRL on two real-world datasets, where experiments show its superior performance. Our source codes are available in the supplementary materials.

翻译：公共资源分配涉及对城市基础设施、能源和交通等资源的高效配置，以满足社会需求。然而，现有方法侧重于独立优化单个资源的流动，未考虑其容量约束。为克服这一局限，我们提出一个新颖且更贴近实际的问题：协作式公共资源分配（CPRA），该问题在现实场景中明确纳入容量约束与时空动态特性。我们提出名为博弈论时空强化学习（GSTRL）的新框架以求解CPRA问题。我们的贡献包括两方面：1）将CPRA问题建模为势博弈，证明势函数与最优目标间无间隙，为逼近这一NP难问题的纳什均衡奠定了坚实的理论基础；2）所设计的GSTRL框架能有效捕捉整体系统的时空动态特性。我们在两个真实世界数据集上评估GSTRL，实验结果表明其性能优越。源代码已附于补充材料中。