Subject: I-D Action: draft-kim-nmrg-rl-02.txt

From: [email-masked]
Date: Mon, 05 Mar 2018 01:27:26 -0800

A New Internet-Draft is available from the on-line Internet-Drafts directories.

        Title           : Intelligent Management using Collaborative Reinforcement Multi-agent System
        Authors         : Min-Suk Kim
                          Yong-Geun Hong
                          Tae-Jin Ahn
                          Kwi-Hoon Kim
                          Youn-Hee Han
	Filename        : draft-kim-nmrg-rl-02.txt
	Pages           : 17
	Date            : 2018-03-05

Abstract:
   This document describes intelligent network management system to
   autonomously manage and monitor using machine learning techniques.
   Reinforcement learning is one of the machine learning techniques that
   can provide autonomously management with multi-agent path-planning
   over a communication network.  According to intelligent distributed
   multi-agent system, the main centralized node called by the global
   environment should not only manage all agents workflow in a hybrid
   peer-to-peer networking architecture and, but transfer and share
   information in distributed nodes.  All agents in distributed nodes
   are able to be provided with a cumulative reward for each action that
   a given agent takes with respect to an optimized knowledge based on a
   to-be-learned policy over the learning process.  The optimized and
   trained knowledge would be involved with a large state information by
   the control action over a network.  A reward from the global
   environment is reflected to the next optimized control action
   autonomously for network management in distributed networking nodes.
   The Reinforcement Learning Process (RLP) have developed and expanded
   to Deep Reinforcement Learning (DRL) with model-driven or data-driven
   technical approaches for learning process.  The trendy technique has
   been widely to attempt and apply to networking fields since DRL can
   be used in practical networking areas beyond dynamics and
   heterogeneous environment disturbances, so that in the technique can
   be intelligently learned in the effective strategy.

The IETF datatracker status page for this draft is:
https://datatracker.ietf.org/doc/draft-kim-nmrg-rl/

There are also htmlized versions available at:
https://tools.ietf.org/html/draft-kim-nmrg-rl-02
https://datatracker.ietf.org/doc/html/draft-kim-nmrg-rl-02

A diff from the previous version is available at:
https://www.ietf.org/rfcdiff?url2=draft-kim-nmrg-rl-02

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