Reinforcement learning offers efficient solutions for spatial resource allocation problems by optimizing decision-making processes.
This paper aims to summarize and review recent theoretical methods and applied research utilizing reinforcement learning to address spatial resource allocation problems, highlighting advantages such as rapid solution convergence and strong model generalization abilities.