# Intelligent Traffic Light Control Using Distributed Multi-agent Q Learning

arXiv: https://arxiv.org/abs/1711.10941 · HTML: https://mlsanity.com/paper/1711.10941/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2017.

## Built on by (most cited first, 7 of 7)

- [The Real Deal: A Review of Challenges and Opportunities in Moving Reinforcement Learning-Based Traffic Signal Control Systems Towards Reality](https://mlsanity.com/paper/2206.11996/) (2022)
- [Reinforcement Learning for Traffic Signal Control: Comparison with Commercial Systems](https://mlsanity.com/paper/2104.10455/) (2021)
- [Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems](https://mlsanity.com/paper/2302.03669/) (2023)
- [FuzzyLight: A Robust Two-Stage Fuzzy Approach for Traffic Signal Control Works in Real Cities](https://mlsanity.com/paper/2501.15820/) (2025)
- [Real-Time Network-Level Traffic Signal Control: An Explicit Multiagent Coordination Method](https://mlsanity.com/paper/2306.08843/) (2023)
- [A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers](https://mlsanity.com/paper/2101.09614/) (2021)
- [Smart Traffic Signals: Comparing MARL and Fixed-Time Strategies](https://mlsanity.com/paper/2505.14544/) (2025)

## Abstract (arXiv)

The combination of Artificial Intelligence (AI) and Internet-of-Things (IoT), which is denoted as AI-powered Internet-of-Things (AIoT), is capable of processing huge amount of data generated from a large number of devices and handling complex problems in social infrastructures. As AI and IoT technologies are becoming mature, in this paper, we propose to apply AIoT technologies for traffic light control, which is an essential component for intelligent transportation system, to improve the efficiency of smart city's road system. Specifically, various sensors such as surveillance cameras provide real-time information for intelligent traffic light control system to observe the states of both motorized traffic and non-motorized traffic. In this paper, we propose an intelligent traffic light control solution by using distributed multi-agent Q learning, considering the traffic information at the neighboring intersections as well as local motorized and non-motorized traffic, to improve the overall performance of the entire control system. By using the proposed multi-agent Q learning algorithm, our solution is targeting to optimize both the motorized and non-motorized traffic. In addition, we considered many constraints/rules for traffic light control in the real world, and integrate these constraints in the learning algorithm, which can facilitate the proposed solution to be deployed in real operational scenarios. We conducted numerical simulations for a real-world map with real-world traffic data. The simulation results show that our proposed solution outperforms existing solutions in terms of vehicle and pedestrian queue lengths, waiting time at intersections, and many other key performance metrics.