# Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks

arXiv: https://arxiv.org/abs/1803.11115 · HTML: https://mlsanity.com/paper/1803.11115/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2018.

## Built on by (most cited first, 30 of 52)

- [Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey](https://mlsanity.com/paper/2110.01411/) (2021)
- [SceneDiffuser++: City-Scale Traffic Simulation via a Generative World Model](https://mlsanity.com/paper/2506.21976/) (2025)
- [SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control](https://mlsanity.com/paper/2211.10871/) (2022)
- [UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Control](https://mlsanity.com/paper/2312.05090/) (2023)
- [X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner](https://mlsanity.com/paper/2404.12090/) (2024)
- [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)
- [Position: Foundation Agents as the Paradigm Shift for Decision Making](https://mlsanity.com/paper/2405.17009/) (2024)
- [CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement Learning](https://mlsanity.com/paper/2201.13143/) (2022)
- [HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents](https://mlsanity.com/paper/2503.08241/) (2025)
- [Independent Reinforcement Learning for Weakly Cooperative Multiagent Traffic Control Problem](https://mlsanity.com/paper/2104.10917/) (2021)
- [Reinforcement learning for traffic signal control in hybrid action space](https://mlsanity.com/paper/2211.12956/) (2022)
- [Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial Detection](https://mlsanity.com/paper/2109.14337/) (2021)
- [Large-Scale Traffic Signal Control by a Nash Deep Q-network Approach](https://mlsanity.com/paper/2301.00637/) (2023)
- [AI Agent as Urban Planner: Steering Stakeholder Dynamics in Urban Planning via Consensus-based Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2310.16772/) (2023)
- [Uncertainty-Aware Model-Based Reinforcement Learning with Application to Autonomous Driving](https://mlsanity.com/paper/2106.12194/) (2021)
- [Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems](https://mlsanity.com/paper/2110.15926/) (2021)
- [ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement Learning](https://mlsanity.com/paper/2210.13378/) (2022)
- [Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems](https://mlsanity.com/paper/2302.03669/) (2023)
- [CoT-VLM4Tar: Chain-of-Thought Guided Vision-Language Models for Traffic Anomaly Resolution](https://mlsanity.com/paper/2503.01632/) (2025)
- [VLMLight: Safety-Critical Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning Architecture](https://mlsanity.com/paper/2505.19486/) (2025)
- [Real-time Cooperative Vehicle Coordination at Unsignalized Road Intersections](https://mlsanity.com/paper/2205.01278/) (2022)
- [Self-supervised Pretraining for Decision Foundation Model: Formulation, Pipeline and Challenges](https://mlsanity.com/paper/2401.00031/) (2023)
- [FuzzyLight: A Robust Two-Stage Fuzzy Approach for Traffic Signal Control Works in Real Cities](https://mlsanity.com/paper/2501.15820/) (2025)
- [CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control](https://mlsanity.com/paper/2603.24366/) (2026)
- [Back to Basics: Deep Reinforcement Learning in Traffic Signal Control](https://mlsanity.com/paper/2109.07180/) (2021)
- [Intelligent Traffic Light via Policy-based Deep Reinforcement Learning](https://mlsanity.com/paper/2112.13817/) (2021)
- [DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System](https://mlsanity.com/paper/2210.06399/) (2022)
- [A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning](https://mlsanity.com/paper/2311.15920/) (2023)
- [Multi-Agent Probabilistic Ensembles with Trajectory Sampling for Connected Autonomous Vehicles](https://mlsanity.com/paper/2312.13910/) (2023)
- [GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control Agents](https://mlsanity.com/paper/2407.10811/) (2024)

## Abstract (arXiv)

Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and dynamically adjusting the traffic light duration accordingly is a must. In terms of how to dynamically adjust traffic signals' duration, existing works either split the traffic signal into equal duration or extract limited traffic information from the real data. In this paper, we study how to decide the traffic signals' duration based on the collected data from different sensors and vehicular networks. We propose a deep reinforcement learning model to control the traffic light. In the model, we quantify the complex traffic scenario as states by collecting data and dividing the whole intersection into small grids. The timing changes of a traffic light are the actions, which are modeled as a high-dimension Markov decision process. The reward is the cumulative waiting time difference between two cycles. To solve the model, a convolutional neural network is employed to map the states to rewards. The proposed model is composed of several components to improve the performance, such as dueling network, target network, double Q-learning network, and prioritized experience replay. We evaluate our model via simulation in the Simulation of Urban MObility (SUMO) in a vehicular network, and the simulation results show the efficiency of our model in controlling traffic lights.