# A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers

arXiv: https://arxiv.org/abs/2101.09614 · HTML: https://mlsanity.com/paper/2101.09614/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 24 Jan 2021.

## Builds on (most cited first, 9 of 9)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control](https://mlsanity.com/paper/1708.04133/) (2017)
- [A Survey on Traffic Signal Control Methods](https://mlsanity.com/paper/1904.08117/) (2019)
- [Diagnosing Reinforcement Learning for Traffic Signal Control](https://mlsanity.com/paper/1905.04716/) (2019)
- [Using a Deep Reinforcement Learning Agent for Traffic Signal Control](https://mlsanity.com/paper/1611.01142/) (2016)
- [Learning an Interpretable Traffic Signal Control Policy](https://mlsanity.com/paper/1912.11023/) (2019)
- [Intelligent Traffic Light Control Using Distributed Multi-agent Q Learning](https://mlsanity.com/paper/1711.10941/) (2017)
- [Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures](https://mlsanity.com/paper/1904.08353/) (2019)
- [An Open-Source Framework for Adaptive Traffic Signal Control](https://mlsanity.com/paper/1909.00395/) (2019)

## Abstract (arXiv)

This article proposes a methodology for the development of adaptive traffic signal controllers using reinforcement learning. Our methodology addresses the lack of standardization in the literature that renders the comparison of approaches in different works meaningless, due to differences in metrics, environments, and even experimental design and methodology. The proposed methodology thus comprises all the steps necessary to develop, deploy and evaluate an adaptive traffic signal controller -- from simulation setup to problem formulation and experimental design. We illustrate the proposed methodology in two simple scenarios, highlighting how its different steps address limitations found in the current literature.