Towards Cognitive Routing based on Deep Reinforcement Learning

2020/03/19 by Jiawei Wu, Wu, Jiawei, Jianxue Li +5
Computer Science · Engineering · #Advanced Memory and Neural Computing #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Network Security and Intrusion Detection #Networking and Internet Architecture (cs.NI) #Software-Defined Networks and 5G

paper · pdf · doi:10.48550/arxiv.2003.12439

openalex publication_date 2020/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Routing is one of the key functions for stable operation of network infrastructure. Nowadays, the rapid growth of network traffic volume and changing of service requirements call for more intelligent routing methods than before. Towards this end, we propose a definition of cognitive routing and an implementation approach based on Deep Reinforcement Learning (DRL). To facilitate the research of DRL-based cognitive routing, we introduce a simulator named RL4Net for DRL-based routing algorithm development and simulation. Then, we design and implement a DDPG-based routing algorithm. The simulation results on an example network topology show that the DDPG-based routing algorithm achieves better performance than OSPF and random weight algorithms. It demonstrate the preliminary feasibility and potential advantage of cognitive routing for future network.

Citations

• On the Theory of the Brownian Motion
• Continuous control with deep reinforcement learning
• Experience-driven Networking: A Deep Reinforcement Learning based Approach

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