Intelligent Reinforcement-learning-based Network Management
draft-kim-nmrg-rl-04

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Network Management Research Group                               M-S. Kim
Internet-Draft                                                      ETRI
Intended status: Informational                                  Y-H. Han
Expires: September 12, 2019                                    KoreaTech
                                                               Y-G. Hong
                                                                    ETRI
                                                          March 11, 2019

      Intelligent Reinforcement-learning-based Network Management
                          draft-kim-nmrg-rl-04

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.

Status of This Memo

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   This Internet-Draft will expire on September 12, 2019.

Kim, et al.            Expires September 12, 2019               [Page 1]
Internet-Draft            draft-kim-nmrg-rl-04                March 2019

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Conventions and Terminology . . . . . . . . . . . . . . . . .   3
   3.  Theoretical Approaches  . . . . . . . . . . . . . . . . . . .   4
     3.1.  Reinforcement-learning  . . . . . . . . . . . . . . . . .   4
     3.2.  Deep-reinforcement-learning . . . . . . . . . . . . . . .   4
     3.3.  Advantage Actor Critic (A2C)  . . . . . . . . . . . . . .   4
     3.4.  Asynchronously Advantage Actor Critic (A3C) . . . . . . .   5
   4.  Reinforcement-learning-based process scenario . . . . . . . .   5
     4.1.  Single-agent with Single-model  . . . . . . . . . . . . .   6
     4.2.  Multi-agents Sharing Single-model . . . . . . . . . . . .   6
     4.3.  Adversarial Self-Play with Single-model . . . . . . . . .   6
     4.4.  Cooperative Multi-agents with Multiple-models . . . . . .   6
     4.5.  Competitive Multi-agents with Multiple-models . . . . . .   7
   5.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . .   7
     5.1.  Intelligent Edge-computing for Traffic Control using
           Deep-reinforcement-learning . . . . . . . . . . . . . . .   7
     5.2.  Edge computing system in a field of Construction-site
           using Reinforcement-learning  . . . . . . . . . . . . . .   7
     5.3.  Deep-reinforcement-learning-based Cyber Physical
           Management Control system over a network  . . . . . . . .   8
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   9
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .   9
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   9
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