# Reinforcement Learning Based Traffic Signal Design to Minimize Queue Lengths

arXiv: https://arxiv.org/abs/2509.21745 · HTML: https://mlsanity.com/paper/2509.21745/

## 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 4 of its 22 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## 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, 4 of 4)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [K-Planes: Explicit Radiance Fields in Space, Time, and Appearance](https://mlsanity.com/paper/2301.10241/) (2023)
- [Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU](https://mlsanity.com/paper/1611.06256/) (2016)
- [Implementing the Deep Q-Network](https://mlsanity.com/paper/1711.07478/) (2017)

## Abstract (arXiv)

Efficient traffic signal control (TSC) is crucial for reducing congestion, travel delays, pollution, and for ensuring road safety. Traditional approaches, such as fixed signal control and actuated control, often struggle to handle dynamic traffic patterns. In this study, we propose a novel adaptive TSC framework that leverages Reinforcement Learning (RL), using the Proximal Policy Optimization (PPO) algorithm, to minimize total queue lengths across all signal phases. The challenge of efficiently representing highly stochastic traffic conditions for an RL controller is addressed through multiple state representations, including an expanded state space, an autoencoder representation, and a K-Planes-inspired representation. The proposed algorithm has been implemented using the Simulation of Urban Mobility (SUMO) traffic simulator and demonstrates superior performance over both traditional methods and other conventional RL-based approaches in reducing queue lengths. The best performing configuration achieves an approximately 29% reduction in average queue lengths compared to the traditional Webster method. Furthermore, comparative evaluation of alternative reward formulations demonstrates the effectiveness of the proposed queue-based approach, showcasing the potential for scalable and adaptive urban traffic management.