# Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network

arXiv: https://arxiv.org/abs/1705.02755 · HTML: https://mlsanity.com/paper/1705.02755/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2017.

## Built on by (most cited first, 16 of 16)

- [Safe and Psychologically Pleasant Traffic Signal Control with Reinforcement Learning using Action Masking](https://mlsanity.com/paper/2206.10122/) (2022)
- [Reinforcement Learning for Traffic Signal Control: Comparison with Commercial Systems](https://mlsanity.com/paper/2104.10455/) (2021)
- [Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial Detection](https://mlsanity.com/paper/2109.14337/) (2021)
- [Real-time Cooperative Vehicle Coordination at Unsignalized Road Intersections](https://mlsanity.com/paper/2205.01278/) (2022)
- [Traffic Light Control with Reinforcement Learning](https://mlsanity.com/paper/2308.14295/) (2023)
- [Asynchronous Parallel Reinforcement Learning for Optimizing Propulsive Performance in Fin Ray Control](https://mlsanity.com/paper/2401.11349/) (2024)
- [GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control Agents](https://mlsanity.com/paper/2407.10811/) (2024)
- [Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion](https://mlsanity.com/paper/2408.15751/) (2024)
- [SECURA: Sigmoid-Enhanced CUR Decomposition with Uninterrupted Retention and Low-Rank Adaptation in Large Language Models](https://mlsanity.com/paper/2502.18168/) (2025)
- [INSPIRE-GNN: Intelligent Sensor Placement to Improve Sparse Bicycling Network Prediction via Reinforcement Learning Boosted Graph Neural Networks](https://mlsanity.com/paper/2508.00141/) (2025)
- [Balancing Multiple Objectives in Urban Traffic Control with Reinforcement Learning from AI Feedback](https://mlsanity.com/paper/2602.20728/) (2026)
- [Deep Reinforcement Learning for Shared Autonomous Vehicles (SAV) Fleet Management](https://mlsanity.com/paper/2201.05720/) (2022)
- [MoveLight: Enhancing Traffic Signal Control through Movement-Centric Deep Reinforcement Learning](https://mlsanity.com/paper/2407.17303/) (2024)
- [Smart Traffic Signals: Comparing MARL and Fixed-Time Strategies](https://mlsanity.com/paper/2505.14544/) (2025)
- [Entropy-Constrained Strategy Optimization in Urban Floods: A Multi-Agent Framework with LLM and Knowledge Graph Integration](https://mlsanity.com/paper/2508.14654/) (2025)
- [Active Inference for Adaptive Traffic Signal Control in Noisy Nonstationary IoT Environments](https://mlsanity.com/paper/2606.13698/) (2026)

## Abstract (arXiv)

Adaptive traffic signal control, which adjusts traffic signal timing according to real-time traffic, has been shown to be an effective method to reduce traffic congestion. Available works on adaptive traffic signal control make responsive traffic signal control decisions based on human-crafted features (e.g. vehicle queue length). However, human-crafted features are abstractions of raw traffic data (e.g., position and speed of vehicles), which ignore some useful traffic information and lead to suboptimal traffic signal controls. In this paper, we propose a deep reinforcement learning algorithm that automatically extracts all useful features (machine-crafted features) from raw real-time traffic data and learns the optimal policy for adaptive traffic signal control. To improve algorithm stability, we adopt experience replay and target network mechanisms. Simulation results show that our algorithm reduces vehicle delay by up to 47% and 86% when compared to another two popular traffic signal control algorithms, longest queue first algorithm and fixed time control algorithm, respectively.