%% You should probably cite draft-kim-nmrg-rl-05 instead of this revision.
@techreport{kim-nmrg-rl-04,
    number =    {draft-kim-nmrg-rl-04},
    type =      {Internet-Draft},
    institution =   {Internet Engineering Task Force},
    publisher = {Internet Engineering Task Force},
    note =      {Work in Progress},
    url =       {https://datatracker.ietf.org/doc/draft-kim-nmrg-rl/04/},
    author =    {Min-Suk Kim and Youn-Hee Han and Yong-Geun Hong},
    title =     {{Intelligent Reinforcement-learning-based Network Management}},
    pagetotal = 12,
    year =      2019,
    month =     mar,
    day =       11,
    abstract =  {This document presents intelligent network management scenarios based on reinforcement-learning approaches. Nowadays, a heterogeneous network should usually provide real-time connectivity, the type of network management with the quality of real-time data, and transmission services generated by the operating system for an application service. With that reason intelligent management system is needed to support real-time connection and protection through efficient management of interfering network traffic for high-quality network data transmission in the both cloud and IoE network systems. Reinforcement-learning is one of the machine learning algorithms that can intelligently and autonomously provide to management systems over a communication network. Reinforcement-learning has developed and expanded with deep learning technique based on model-driven or data- driven technical approaches so that these trendy techniques have been widely to intelligently attempt an adaptive networking models with effective strategies in environmental disturbances over variety of networking areas.},
}