The facility location problem (FLP) is a classical combinatorial In real cities, facility layout tends to deviate from residential demands optimization challenge aimed at strategically laying out facilities for corresponding services, leading to costly travel [12, 13]. to maximize their accessibility. In this paper, we propose a reinforcement Therefore, optimizing accessibility by strategically locating urban learning method tailored to solve large-scale urban facilities is crucial for creating more sustainable and inclusive cities. FLP, capable of producing near-optimal solutions at superfast inference In fact, facility location problem (FLP) is a classic combinatorial speed. We distill the essential swap operation from local optimization (CO) problem [2, 8], which is notoriously challenging search, and simulate it by intelligently selecting edges on a graph due to the NP-hardness inherent in selecting urban regions to of urban regions, guided by a knowledge-informed graph neural place facilities from candidate regions [5]. As both and are network, thus sidestepping the need for heavy computation typically large in urban contexts, designing a reliable algorithm that of local search. Extensive experiments on four US cities with different delivers satisfactory solutions within reasonable timeframes is difficult.