Leveraging Reinforcement Learning for Traffic Engineering in Programmable Networks: A Survey
Santiago Ríos-Guiral, Abdelkader Lahmadi, Juan Felipe Botero, Sergio Armando Gutiérrez · IEEE Communications Surveys & Tutorials · 2025
Programmable networks have transformed network management, particularly within Traffic Engineering (TE), which aims to optimize data flow across the network. By offering flexibility and efficiency, programmable networks facilitate advanced traffic and resource management capabilities. Key technologies in this area, such as Software-Defined Networking (SDN) and Programmable Data Planes (PDPs), enable real-time, dynamic routing and network control. These frameworks further allow the integration of Artificial Intelligence (AI) mechanisms to automate and refine network management processes. Specifically, Reinforcement Learning (RL) is a promising approach for TE applications due to its adaptability to evolving network conditions. We provide a survey with an in-depth review of TE solutions that leverage RL and programmable networks to improve network performance, including works from 2018 to 2025. The proposed survey includes a timely update on the state-of-the-art and presents a taxonomy that categorizes existing solutions based on RL principles and specific TE objectives. Our analysis highlights key findings and insights, contributing valuable knowledge for implementing TE mechanisms within programmable networks.