# Applying Reinforcement Learning to Optimize Traffic Light Cycles

arXiv: https://arxiv.org/abs/2402.14886 · HTML: https://mlsanity.com/paper/2402.14886/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 1 of 1)

- [Path Planning of Cleaning Robot with Reinforcement Learning](https://mlsanity.com/paper/2208.08211/) (2022)

## 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.