用博弈论与强化学习协同分配公共资源，兼顾容量与时空动态。
A Game-Theoretic Spatio-Temporal Reinforcement Learning Framework for Collaborative Public Resource Allocation
- 将资源分配建模为潜在博弈，理论保证逼近最优解
- 在两个真实数据集上表现优于现有方法
- 适合城市规划、交通调度等需要协同决策的场景
公共资源配置涉及城市基础设施、能源和交通等资源的高效分配，以满足社会需求。现有方法通常独立优化单个资源的移动，未考虑其容量限制。为此，我们提出更具实际意义的新问题：协作式公共资源分配（CPRA），显式引入容量约束与时空动态。本文提出一种基于博弈论的时空强化学习框架（GSTRL）来解决该问题。主要贡献包括：1）将CPRA建模为潜在博弈，并证明潜在函数与最优目标无差距，为近似纳什均衡提供坚实的理论基础；2）所设计的GSTRL框架能有效捕捉系统整体的时空动态。我们在两个真实世界数据集上进行了评估，实验结果表明该方法性能更优。源代码可在补充材料中获取。
原文摘要 · Abstract (English)
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.
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