Open AccessResearch on Routing Algorithm Based on Reinforcement Learning in SDN
Author(s) -
Chuntao Fang,
Cheng Cheng,
Zhongyun Tang,
Chuanhuang Li
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1284/1/012053
Subject(s) - reinforcement learning , computer science , q learning , routing (electronic design automation) , artificial intelligence , routing algorithm , unsupervised learning , reinforcement , artificial neural network , wake sleep algorithm , competitive learning , value (mathematics) , machine learning , routing table , routing protocol , computer network , engineering , structural engineering , generalization error
Reinforcement learning is an unsupervised learning method which has been used in many fields. Actually, the essence of Reinforcement learning is a decision-making problem. It constantly tries to interact with the environment. Each interaction process will get a different feedback value, and then it adjusts each trial strategy through feedback. In this paper, we apply the Reinforcement learning technology to software defined network routing algorithm, and propose the routing algorithm based on Q-learning. Through the combination of Reinforcement learning and neural network, which means the Q-table in Q-learning is replaced by neural network, we present routing algorithm based on Deep Q-learning.