mlsanityData to 14 Oct 2026

Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks

arXiv 1803.11115 ↗First posted 29 Mar 2018cs.LG cs.AI stat.MLcited by 52

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 9; 2022: 10; 2023: 10; 2024: 5; 2025: 11; 2026: 7.

52 papers.

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  1. 2021Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Surveycited by 28
  2. 2025SceneDiffuser++: City-Scale Traffic Simulation via a Generative World Modelcited by 20
  3. 2022SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Controlcited by 15
  4. 2023UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Controlcited by 13
  5. 2024X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learnercited by 10
  6. 2022The Real Deal: A Review of Challenges and Opportunities in Moving Reinforcement Learning-Based Traffic Signal Control Systems Towards Realitycited by 9
  7. 2024Position: Foundation Agents as the Paradigm Shift for Decision Makingcited by 9
  8. 2022CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement Learningcited by 6
  9. 2025HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agentscited by 6
  10. 2021Independent Reinforcement Learning for Weakly Cooperative Multiagent Traffic Control Problemcited by 5
  11. 2022Reinforcement learning for traffic signal control in hybrid action spacecited by 5
  12. 2021Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial Detectioncited by 4
38 more
  1. 2023Large-Scale Traffic Signal Control by a Nash Deep Q-network Approachcited by 4
  2. 2023AI Agent as Urban Planner: Steering Stakeholder Dynamics in Urban Planning via Consensus-based Multi-Agent Reinforcement Learningcited by 4
  3. 2021Uncertainty-Aware Model-Based Reinforcement Learning with Application to Autonomous Drivingcited by 3
  4. 2021Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systemscited by 3
  5. 2022ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement Learningcited by 3
  6. 2023Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systemscited by 3
  7. 2025CoT-VLM4Tar: Chain-of-Thought Guided Vision-Language Models for Traffic Anomaly Resolutioncited by 3
  8. 2025VLMLight: Safety-Critical Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning Architecturecited by 3
  9. 2022Real-time Cooperative Vehicle Coordination at Unsignalized Road Intersectionscited by 2
  10. 2023Self-supervised Pretraining for Decision Foundation Model: Formulation, Pipeline and Challengescited by 2
  11. 2025FuzzyLight: A Robust Two-Stage Fuzzy Approach for Traffic Signal Control Works in Real Citiescited by 2
  12. 2026CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Controlcited by 2
  13. 2021Back to Basics: Deep Reinforcement Learning in Traffic Signal Controlcited by 1
  14. 2021Intelligent Traffic Light via Policy-based Deep Reinforcement Learningcited by 1
  15. 2022DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine Systemcited by 1
  16. 2023A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learningcited by 1
  17. 2023Multi-Agent Probabilistic Ensembles with Trajectory Sampling for Connected Autonomous Vehiclescited by 1
  18. 2024GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control Agentscited by 1
  19. 2025Incorporating graph neural network into route choice modelcited by 1
  20. 2026Sharing the Control Authority Between Deep Reinforcement Learning and Model Predictive Control: Application to Multi-Class Transportation Networkscited by 1
  21. 2026A Multi-Modal AI Framework for Real-Time Queue Prediction, Management and Optimisation in Intelligent Border Control Systemscited by 1
  22. 2021Effects of Smart Traffic Signal Control on Air Qualitycited by 0
  23. 2021Modelling and Reasoning Techniques for Context Aware Computing in Intelligent Transportation Systemcited by 0
  24. 2022Standardized feature extraction from pairwise conflicts applied to the train rescheduling problemcited by 0
  25. 2022DynLight: Realize dynamic phase duration with multi-level traffic signal controlcited by 0
  26. 2022Automatic Discovery of Multi-perspective Process Model using Reinforcement Learningcited by 0
  27. 2023Joint Optimization of Traffic Signal Control and Vehicle Routing in Signalized Road Networks using Multi-Agent Deep Reinforcement Learningcited by 0
  28. 2023Combat Urban Congestion via Collaboration: Heterogeneous GNN-based MARL for Coordinated Platooning and Traffic Signal Controlcited by 0
  29. 2023A DRL solution to help reduce the cost in waiting time of securing a traffic light for cyclistscited by 0
  30. 2024MoveLight: Enhancing Traffic Signal Control through Movement-Centric Deep Reinforcement Learningcited by 0
  31. 2024Adaptive traffic signal safety and efficiency improvement by multi objective deep reinforcement learning approachcited by 0
  32. 2025A Parallel Hybrid Action Space Reinforcement Learning Model for Real-world Adaptive Traffic Signal Controlcited by 0
  33. 2025Multi-agent Uncertainty-Aware Pessimistic Model-Based Reinforcement Learning for Connected Autonomous Vehiclescited by 0
  34. 2025Topology-Assisted Spatio-Temporal Pattern Disentangling for Scalable MARL in Large-scale Autonomous Traffic Controlcited by 0
  35. 2025A Semi Centralized Training Decentralized Execution Architecture for Multi Agent Deep Reinforcement Learning in Traffic Signal Controlcited by 0
  36. 2025CFLight: Enhancing Safety with Traffic Signal Control through Counterfactual Learningcited by 0
  37. 2026Balancing Efficiency and Fairness in Traffic Light Control through Deep Reinforcement Learningcited by 0
  38. 2026Momentum Based Reward Design for Low Emission Traffic Signal Controlcited by 0

50 most cited of 52 All 52 (JSON)