# Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissions

arXiv: https://arxiv.org/abs/2310.13129 · HTML: https://mlsanity.com/paper/2310.13129/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control](https://mlsanity.com/paper/1903.04527/) (2019)
- [Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation](https://mlsanity.com/paper/1708.05144/) (2017)
- [True Online Temporal-Difference Learning](https://mlsanity.com/paper/1512.04087/) (2015)
- [Quantifying the Impact of Non-Stationarity in Reinforcement Learning-Based Traffic Signal Control](https://mlsanity.com/paper/2004.04778/) (2020)
- [A Comparative Study of Algorithms for Intelligent Traffic Signal Control](https://mlsanity.com/paper/2109.00937/) (2021)
- [Online Probabilistic Model Identification using Adaptive Recursive MCMC](https://mlsanity.com/paper/2210.12595/) (2022)
- [Adaptive Model Learning of Neural Networks with UUB Stability for Robot Dynamic Estimation](https://mlsanity.com/paper/2210.15055/) (2022)
- [Self-Organization in Traffic Lights: Evolution of Signal Control with Advances in Sensors and Communications](https://mlsanity.com/paper/1708.07188/) (2017)

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

- [DiffPoGAN: Diffusion Policies with Generative Adversarial Networks for Offline Reinforcement Learning](https://mlsanity.com/paper/2406.09089/) (2024)
- [Momentum Based Reward Design for Low Emission Traffic Signal Control](https://mlsanity.com/paper/2605.29693/) (2026)

## Abstract (arXiv)

Nowadays, transportation networks face the challenge of sub-optimal control policies that can have adverse effects on human health, the environment, and contribute to traffic congestion. Increased levels of air pollution and extended commute times caused by traffic bottlenecks make intersection traffic signal controllers a crucial component of modern transportation infrastructure. Despite several adaptive traffic signal controllers in literature, limited research has been conducted on their comparative performance. Furthermore, despite carbon dioxide (CO2) emissions' significance as a global issue, the literature has paid limited attention to this area. In this report, we propose EcoLight, a reward shaping scheme for reinforcement learning algorithms that not only reduces CO2 emissions but also achieves competitive results in metrics such as travel time. We compare the performance of tabular Q-Learning, DQN, SARSA, and A2C algorithms using metrics such as travel time, CO2 emissions, waiting time, and stopped time. Our evaluation considers multiple scenarios that encompass a range of road users (trucks, buses, cars) with varying pollution levels.