# Large-scale Urban Facility Location Selection with Knowledge-informed Reinforcement Learning

arXiv: https://arxiv.org/abs/2409.01588 · HTML: https://mlsanity.com/paper/2409.01588/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 2 of 2)

- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Road Planning for Slums via Deep Reinforcement Learning](https://mlsanity.com/paper/2305.13060/) (2023)

## Built on by (most cited first, 2 of 2)

- [UrbanPlanBench: A Comprehensive Urban Planning Benchmark for Evaluating Large Language Models](https://mlsanity.com/paper/2504.21027/) (2025)
- [A Survey of Physics-Informed AI for Complex Urban Systems](https://mlsanity.com/paper/2506.13777/) (2025)

## Abstract (arXiv)

The facility location problem (FLP) is a classical combinatorial optimization challenge aimed at strategically laying out facilities to maximize their accessibility. In this paper, we propose a reinforcement learning method tailored to solve large-scale urban FLP, capable of producing near-optimal solutions at superfast inference speed. We distill the essential swap operation from local search, and simulate it by intelligently selecting edges on a graph of urban regions, guided by a knowledge-informed graph neural network, thus sidestepping the need for heavy computation of local search. Extensive experiments on four US cities with different geospatial conditions demonstrate that our approach can achieve comparable performance to commercial solvers with less than 5% accessibility loss, while displaying up to 1000 times speedup. We deploy our model as an online geospatial application at https://huggingface.co/spaces/randommmm/MFLP.