# An Efficient Deep Reinforcement Learning Model for Urban Traffic Control

arXiv: https://arxiv.org/abs/1808.01876 · HTML: https://mlsanity.com/paper/1808.01876/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control](https://mlsanity.com/paper/2310.02435/) (2023)
- [CycLight: learning traffic signal cooperation with a cycle-level strategy](https://mlsanity.com/paper/2401.08121/) (2024)
- [Controlling dynamics of stochastic systems with deep reinforcement learning](https://mlsanity.com/paper/2502.18111/) (2025)
- [Traffic Management of Autonomous Vehicles using Policy Based Deep Reinforcement Learning and Intelligent Routing](https://mlsanity.com/paper/2206.14608/) (2022)

## Abstract (arXiv)

Urban Traffic Control (UTC) plays an essential role in Intelligent Transportation System (ITS) but remains difficult. Since model-based UTC methods may not accurately describe the complex nature of traffic dynamics in all situations, model-free data-driven UTC methods, especially reinforcement learning (RL) based UTC methods, received increasing interests in the last decade. However, existing DL approaches did not propose an efficient algorithm to solve the complicated multiple intersections control problems whose state-action spaces are vast. To solve this problem, we propose a Deep Reinforcement Learning (DRL) algorithm that combines several tricks to master an appropriate control strategy within an acceptable time. This new algorithm relaxes the fixed traffic demand pattern assumption and reduces human invention in parameter tuning. Simulation experiments have shown that our method outperforms traditional rule-based approaches and has the potential to handle more complex traffic problems in the real world.