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

2025-10-30

AI Summary

This paper addresses the limitations of existing approaches to public resource allocation—specifically, their neglect of capacity constraints and spatiotemporal dynamics—by formalizing a novel problem: Collaborative Public Resource Allocation (CPRA). Methodologically, we first model CPRA as a potential game, thereby eliminating the misalignment between the potential function and the global optimization objective. We then propose a distributed Graph-Structured Spatiotemporal Reinforcement Learning (GSTRL) framework that integrates potential game theory with capacity-aware spatiotemporal decision-making, enabling efficient approximation of Nash equilibria for this NP-hard problem. Experiments on two real-world urban datasets demonstrate that our approach significantly outperforms state-of-the-art baselines, achieving consistent improvements in resource utilization, fairness, and timeliness—thereby exhibiting strong practical deployability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Game Theory and Economic Paradigms: Coordination and Collaboration

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: The sharing economy
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Addresses collaborative public resource allocation with capacity constraints
• Models spatio-temporal dynamics in real-world resource distribution scenarios
• Solves NP-hard problem by approximating Nash equilibrium through game theory

Innovation

• Game-theoretic reinforcement learning for resource allocation
• Potential game formulation with Nash equilibrium approximation
• Spatio-temporal dynamics modeling in collaborative systems