Deep Reinforcement Learning for Router Selection in Network With Heavy Traffic

Deep Reinforcement Learning for Router Selection in Network With Heavy Traffic

The rapid development of wireless communications brings a tremendous increase in the amount number of data streams and poses significant challenges to the traditional routing protocols. In this paper, we leverage deep reinforcement learning (DRL) for router selection in the network with heavy traffi...

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Bibliographic Details
Main Authors: Ruijin Ding, Yadong Xu, Feifei Gao, Xuemin Shen, Wen Wu
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Deep reinforcement learning
routing
network congestion
network throughput
deep Q network
Online Access:https://ieeexplore.ieee.org/document/8673947/

Internet

https://ieeexplore.ieee.org/document/8673947/

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