# AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference

arXiv: https://arxiv.org/abs/2003.12205 · HTML: https://mlsanity.com/paper/2003.12205/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2020.

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

- [The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems using IoT, Big Data, and Machine Learning](https://mlsanity.com/paper/2304.09574/) (2023)
- [AIREX: Neural Network-based Approach for Air Quality Inference in Unmonitored Cities](https://mlsanity.com/paper/2108.07120/) (2021)

## Abstract (arXiv)

Urban air pollution has become a major environmental problem that threatens public health. It has become increasingly important to infer fine-grained urban air quality based on existing monitoring stations. One of the challenges is how to effectively select some relevant stations for air quality inference. In this paper, we propose a novel model based on reinforcement learning for urban air quality inference. The model consists of two modules: a station selector and an air quality regressor. The station selector dynamically selects the most relevant monitoring stations when inferring air quality. The air quality regressor takes in the selected stations and makes air quality inference with deep neural network. We conduct experiments on a real-world air quality dataset and our approach achieves the highest performance compared with several popular solutions, and the experiments show significant effectiveness of proposed model in tackling problems of air quality inference.