A Game-Theoretic Spatio-Temporal Reinforcement Learning Framework for Collaborative Public Resource Allocation

Abstract

Public resource allocation involves distributing resources, including urban infrastructure, energy, and transportation, which are typically limited in capacity, to meet social demands. In real-world scenarios, resources are typically limited in capacity, which makes coordination among multiple resources essential. However, existing methods often optimize resource movements in an isolated manner and do not explicitly account for capacity-aware collaboration under spatio-temporal dynamics. To address this limitation, we introduce the Collaborative Public Resource Allocation (CPRA) problem, and propose a Game-Theoretic Spatio-Temporal Reinforcement Learning (GSTRL) framework to solve it. Our contributions are twofold: 1) We formulate CPRA as a potential game and construct the potential function based on the objective function of CPRA, laying a theoretical foundation for approximating the Nash equilibrium of this NP-hard problem; and 2) Our 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 at https://github.com/thunderlrr/GSTRL.