Applying Reinforcement Learning to Optimize Traffic Light Cycles
On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic
Abstract (arXiv)
Manual optimization of traffic light cycles is a complex and time-consuming task, necessitating the development of automated solutions. In this paper, we propose the application of reinforcement learning to optimize traffic light cycles in real-time. We present a case study using the Simulation Urban Mobility simulator to train a Deep Q-Network algorithm. The experimental results showed 44.16% decrease in the average number of Emergency stops, showing the potential of our approach to reduce traffic congestion and improve traffic flow. Furthermore, we discuss avenues for future research and enhancements to the reinforcement learning model.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 11 references that are in the map. First posted on arXiv 22 Feb 2024.
Builds on
1 of 11 references.
References · 1
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2024MoveLight: Enhancing Traffic Signal Control through Movement-Centric Deep Reinforcement Learning
- 2026Balancing Efficiency and Fairness in Traffic Light Control through Deep Reinforcement Learning
- 2024Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion
- 2024MTLight: Efficient Multi-Task Reinforcement Learning for Traffic Signal Control
- 2025Optimizing Efficiency of Mixed Traffic through Reinforcement Learning: A Topology-Independent Approach and Benchmark
- 2024CycLight: learning traffic signal cooperation with a cycle-level strategy
- 2025Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning
- 2025Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning