# Using a Deep Reinforcement Learning Agent for Traffic Signal Control

arXiv: https://arxiv.org/abs/1611.01142 · HTML: https://mlsanity.com/paper/1611.01142/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2016.

## Built on by (most cited first, 26 of 26)

- [Mixture-of-Personas Language Models for Population Simulation](https://mlsanity.com/paper/2504.05019/) (2025)
- [SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control](https://mlsanity.com/paper/2211.10871/) (2022)
- [Applications of deep learning in traffic congestion detection, prediction and alleviation: A survey](https://mlsanity.com/paper/2102.09759/) (2021)
- [TransGPT: Multi-modal Generative Pre-trained Transformer for Transportation](https://mlsanity.com/paper/2402.07233/) (2024)
- [Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning](https://mlsanity.com/paper/2104.09936/) (2021)
- [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)
- [Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control](https://mlsanity.com/paper/2310.02435/) (2023)
- [SocialLight: Distributed Cooperation Learning towards Network-Wide Traffic Signal Control](https://mlsanity.com/paper/2305.16145/) (2023)
- [Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline](https://mlsanity.com/paper/2101.09640/) (2021)
- [Adaptive traffic signal control optimization using a novel road partition and multi-channel state representation method](https://mlsanity.com/paper/2602.12296/) (2026)
- [Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems](https://mlsanity.com/paper/2110.15926/) (2021)
- [A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization](https://mlsanity.com/paper/2107.06115/) (2021)
- [Transformer-based Map Matching Model with Limited Ground-Truth Data using Transfer-Learning Approach](https://mlsanity.com/paper/2108.00439/) (2021)
- [A Critical Review of Traffic Signal Control and A Novel Unified View of Reinforcement Learning and Model Predictive Control Approaches for Adaptive Traffic Signal Control](https://mlsanity.com/paper/2211.14426/) (2022)
- [CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control](https://mlsanity.com/paper/2603.24366/) (2026)
- [Traffic Light Control with Reinforcement Learning](https://mlsanity.com/paper/2308.14295/) (2023)
- [Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion](https://mlsanity.com/paper/2408.15751/) (2024)
- [Communication Strategy on Macro-and-Micro Traffic State in Cooperative Deep Reinforcement Learning for Regional Traffic Signal Control](https://mlsanity.com/paper/2502.13248/) (2025)
- [A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers](https://mlsanity.com/paper/2101.09614/) (2021)
- [Traffic Management of Autonomous Vehicles using Policy Based Deep Reinforcement Learning and Intelligent Routing](https://mlsanity.com/paper/2206.14608/) (2022)
- [A DRL solution to help reduce the cost in waiting time of securing a traffic light for cyclists](https://mlsanity.com/paper/2311.13905/) (2023)
- [Exploring the impact of traffic signal control and connected and automated vehicles on intersections safety: A deep reinforcement learning approach](https://mlsanity.com/paper/2405.19236/) (2024)
- [Using Petri Nets as an Integrated Constraint Mechanism for Reinforcement Learning Tasks](https://mlsanity.com/paper/2407.04481/) (2024)
- [An Offline Meta Black-box Optimization Framework for Adaptive Design of Urban Traffic Light Management Systems](https://mlsanity.com/paper/2408.07327/) (2024)
- [HALO: Hierarchical Reinforcement Learning for Large-Scale Adaptive Traffic Signal Control](https://mlsanity.com/paper/2506.14391/) (2025)
- [Learning to Move Cities: Deep Meta-Models and Reinforcement Policies for Calibration and Control in Urban Networks](https://mlsanity.com/paper/2609.21945/) (2026)

## Abstract (arXiv)

Ensuring transportation systems are efficient is a priority for modern society. Technological advances have made it possible for transportation systems to collect large volumes of varied data on an unprecedented scale. We propose a traffic signal control system which takes advantage of this new, high quality data, with minimal abstraction compared to other proposed systems. We apply modern deep reinforcement learning methods to build a truly adaptive traffic signal control agent in the traffic microsimulator SUMO. We propose a new state space, the discrete traffic state encoding, which is information dense. The discrete traffic state encoding is used as input to a deep convolutional neural network, trained using Q-learning with experience replay. Our agent was compared against a one hidden layer neural network traffic signal control agent and reduces average cumulative delay by 82%, average queue length by 66% and average travel time by 20%.