# Traffic Light Control with Reinforcement Learning

arXiv: https://arxiv.org/abs/2308.14295 · HTML: https://mlsanity.com/paper/2308.14295/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 47 references that are in the map.
- First posted on arXiv 28 Aug 2023.

## Builds on (most cited first, 11 of 11)

- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [CoLight: Learning Network-level Cooperation for Traffic Signal Control](https://mlsanity.com/paper/1905.05717/) (2019)
- [Recent Advances in Reinforcement Learning in Finance](https://mlsanity.com/paper/2112.04553/) (2021)
- [Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2005.00935/) (2020)
- [Learning Phase Competition for Traffic Signal Control](https://mlsanity.com/paper/1905.04722/) (2019)
- [A Survey on Traffic Signal Control Methods](https://mlsanity.com/paper/1904.08117/) (2019)
- [Using a Deep Reinforcement Learning Agent for Traffic Signal Control](https://mlsanity.com/paper/1611.01142/) (2016)
- [Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network](https://mlsanity.com/paper/1705.02755/) (2017)
- [Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data](https://mlsanity.com/paper/2012.07731/) (2020)
- [Individual Path Recommendation Under Public Transit Service Disruptions Considering Behavior Uncertainty](https://mlsanity.com/paper/2301.00916/) (2023)
- [Robust Path Recommendations During Public Transit Disruptions Under Demand Uncertainty](https://mlsanity.com/paper/2201.01437/) (2022)

## Built on by (most cited first, 1 of 1)

- [Smart Traffic Signals: Comparing MARL and Fixed-Time Strategies](https://mlsanity.com/paper/2505.14544/) (2025)

## Abstract (arXiv)

Traffic light control is important for reducing congestion in urban mobility systems. This paper proposes a real-time traffic light control method using deep Q learning. Our approach incorporates a reward function considering queue lengths, delays, travel time, and throughput. The model dynamically decides phase changes based on current traffic conditions. The training of the deep Q network involves an offline stage from pre-generated data with fixed schedules and an online stage using real-time traffic data. A deep Q network structure with a "phase gate" component is used to simplify the model's learning task under different phases. A "memory palace" mechanism is used to address sample imbalance during the training process. We validate our approach using both synthetic and real-world traffic flow data on a road intersecting in Hangzhou, China. Results demonstrate significant performance improvements of the proposed method in reducing vehicle waiting time (57.1% to 100%), queue lengths (40.9% to 100%), and total travel time (16.8% to 68.0%) compared to traditional fixed signal plans.