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Network Management Research Group                               M-S. Kim
Internet-Draft                                                 Y-G. Hong
Intended status: Informational                                      ETRI
Expires: May 2, 2018                                            Y-H. Han
                                                               KoreaTech
                                                        October 29, 2017


  Intelligent Management using Collaborative Reinforcement Multi-agent
                                 System
                          draft-kim-nmrg-rl-01

Abstract

   This document describes an intelligent reinforcement learning agent
   system to autonomously manage agent path-planning over a
   communication network.  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 knowledge would be involved with a large state
   information by the control action.  A reward from the global
   environment is reflected to the next optimized control action for
   autonomous path management in distributed networking nodes.  The
   reinforcement learning process (RLP) have developed and expanded to
   deep reinforcement learning (DRL) with a data-driven approach
   technique for learning process.  The trendy technique has been widely
   to attempt and apply to networking fields since DRL can be used in
   practice, since networking areas have the dynamics and heterogeneous
   environment disturbances, so that in the technique is able to be
   intelligently learned in the effective strategy.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any




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   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on May 2, 2018.

Copyright Notice

   Copyright (c) 2017 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
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   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Conventions and Terminology . . . . . . . . . . . . . . . . .   4
   3.  Motivation  . . . . . . . . . . . . . . . . . . . . . . . . .   4
     3.1.  General Motivation for Reinforcement Learning (RL)  . . .   4
     3.2.  Reinforcement Learning (RL) in networks . . . . . . . . .   4
     3.3.  Deep Reinforcement Learning (DRL) in networks . . . . . .   4
     3.4.  Motivation in our work  . . . . . . . . . . . . . . . . .   4
   4.  Related Works . . . . . . . . . . . . . . . . . . . . . . . .   5
     4.1.  Autonomous Driving System . . . . . . . . . . . . . . . .   5
     4.2.  Game Theory . . . . . . . . . . . . . . . . . . . . . . .   5
     4.3.  Wireless Sensor Network (WSN) . . . . . . . . . . . . . .   5
     4.4.  Routing Enhancement . . . . . . . . . . . . . . . . . . .   5
     4.5.  Routing Optimization  . . . . . . . . . . . . . . . . . .   6
   5.  Multi-agent Reinforcement Learning (RL) Technologies  . . . .   6
     5.1.  Reinforcement Learning (RL) . . . . . . . . . . . . . . .   6
     5.2.  Policy using Distance and Frequency . . . . . . . . . . .   6
     5.3.  Distributed Computing Node  . . . . . . . . . . . . . . .   6
     5.4.  Agent Sharing Information . . . . . . . . . . . . . . . .   7
     5.5.  Deep Learning Technique . . . . . . . . . . . . . . . . .   7
     5.6.  Sub-goal Selection  . . . . . . . . . . . . . . . . . . .   8
   6.  Proposed Architecture for Deep Reinforcement Learning (DRL) .   8
   7.  Use case of Multi-agent Reinforcement Learning (RL) . . . . .   9
     7.1.  Distributed Multi-agent Reinforcement Learning: Sharing
           Information Technique . . . . . . . . . . . . . . . . . .   9
     7.2.  Use case of Shortest Path-planning via sub-goal selection  11
   8.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  11



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   9.  Security Considerations . . . . . . . . . . . . . . . . . . .  11
   10. Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  12
   11. References  . . . . . . . . . . . . . . . . . . . . . . . . .  12
     11.1.  Normative References . . . . . . . . . . . . . . . . . .  12
     11.2.  Informative References . . . . . . . . . . . . . . . . .  12
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  14

1.  Introduction

   In large infrastructures such as transportation, health and energy
   systems, collaborative monitoring system is needed, where there are
   special needs for intelligent distributed networking systems with
   learning schemes.  Agent Reinforcement Learning (RL) for
   intelligently autonomous network management, in general, is one of
   the challengeable methods in a dynamic complex cluttered environment
   over a network.  It also needs the development of computational
   multi-agents learning systems in large distributed networking nodes,
   where the agents have limited and incomplete knowledge, and they only
   access local information in distributed networking nodes.

   Reinforcement Learning (RL) can become an effective technique to
   transfer and share information among agents, as it does not require a
   priori knowledge of the agent behavior or environment to accomplish
   its tasks [Megherbi].  Such a knowledge is usually acquired and
   learned automatically and autonomously by trial and error.

   Reinforcement Learning (RL) is Machine Learning techniques that will
   be adapted to the various networking environments for automatic
   networks[S.  Jiang].  Thus, this document provides motivation,
   learning technique, and use case for network machine learning.

   Deep reinforcement learning (DRL) recently proposes that the extended
   reinforcement learning (RL) algorithm could emerge as a powerful
   data-driven technique over a large state space to overcome the
   classical behavior RL process.  The DRL technique has been
   significantly shown as successful models in playing Atari games [V.
   Mnih].  The DRL provides more effective experimental system
   performance in a complex and cluttered networking environment.

   Classical reinforcement learning (RL) slightly has a limitation to be
   adopted in networking areas, since the networking environments
   consist of significantly large and complex components in fields of
   routing configuration, optimization and system management, so that
   DRL provided with much more state information for learning process is
   needed.






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2.  Conventions and Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in [RFC2119].

3.  Motivation

3.1.  General Motivation for Reinforcement Learning (RL)

   Reinforcement Learning (RL) is a system capable of autonomous
   acquirement and incorporation of knowledge.  It can continuously
   self-improve learning process with experience and attempts to
   maximize cumulative reward to manage an optimized learning knowledge
   by multi-agents-based monitoring systems[Teiralbar].  The maximized
   reward can be increasingly optimizing of learning speed for agent
   autonomous learning process.

3.2.  Reinforcement Learning (RL) in networks

   Reinforcement learning (RL) is an emerging technology in terms of
   monitoring network system to achieve fair resource allocation for
   nodes within the wire or wireless mesh setting.  Monitoring
   parameters of the network and adjusts based on the network dynamics
   can demonstrate to improve fairness in wireless environment
   Infrastructures and Resources[Nasim].

3.3.  Deep Reinforcement Learning (DRL) in networks

   Deep reinforcement learning is a large state data-driven approach on
   an intelligently learning strategy.  The intelligent technique
   represents learning models successfully to learn control policies
   directly from high-dimensional sensory input using reinforcement
   learning (RL) with Q-value function in a convolutional neural network
   [Mnih].  The model repeatedly estimates future reward to acquire more
   effective control action in following next steps.  The DRL can be
   widely-adopted in routing optimization to attempt minimizing the
   network delay [Stampa].

3.4.  Motivation in our work

   There are many different networking management issues such as
   connectivity, traffic management, fast internet without latency and
   etc.  We expect that ml-based mechanism such as reinforcement
   learning [RL] will provide network solutions with multiple cases
   against human operating capacities even if it is a challengeable area
   due to a multitude of reasons such as large state space search,
   complexity in giving reward, difficulty in agent action selection,



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   and difficulty in sharing and merging learned information among the
   agents in a distributed memory node to be transferred over a
   communication network.[Minsuk]

4.  Related Works

4.1.  Autonomous Driving System

   Autonomous vehicle is capable of self-automotive driving without
   human supervision depending on optimized trust region policy by
   reinforcement learning (RL) that enables learning of more complex and
   special network management environment.  Such a vehicle provides a
   comfortable user experience safely and reliably on interactive
   communication network [April], [Markus].

4.2.  Game Theory

   The adaptive multi-agent system, which is combined with complexities
   from interacting game player, has developed in a field of
   reinforcement learning (RL).  In the early game theory, the
   interdisciplinary work was only focused on competitive games, but
   Reinforcement Learning (RL) has developed into a general framework
   for analyzing strategic interaction and has been attracted field as
   diverse as psychology, economics and biology.[Ann] AlphaGo is also
   one of the game theories using reinforcement learning (RL), developed
   by Google DeepMind.  Even though it began as a small learning
   computational program with some simple actions, it has now trained on
   a policy and value networks of thirty million actions, states and
   rewards.

4.3.  Wireless Sensor Network (WSN)

   Wireless sensor network (WSN) consists of a large number of sensors
   and sink nodes for monitoring systems with event parameters such as
   temperature, humidity, air conditioning, etc.  Reinforcement learning
   (RL) in WSNs has been applied in a wide range of schemes such as
   cooperative communication, routing and rate control.  The sensors and
   sink nodes are able to observe and carry out optimal actions on their
   respective operating environment for network and application
   performance enhancements.[Kok-Lim]

4.4.  Routing Enhancement

   Reinforcement Learning (RL) is used to enhance multicast routing
   protocol in wireless ad hoc networks, where each node has different
   capability.  Routers in the multicast routing protocol are determined
   to discover optimal route with a predicted reward, and then the




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   routers create the optimal path with multicast transmissions to
   reduce the overhead in Reinforcement Learning (RL).[Kok-Lim]

4.5.  Routing Optimization

   Routing optimization as traffic engineering is one of the important
   issues to control the behavior of transmitted data in order to
   maximize the performance of network [Stampa].  There are several
   attempts to be adopted with machine learning algorithms in the
   context of routing optimization.  Deep reinforcement learning (DRL)
   is recently one of solutions for unseen network states that cannot be
   achieved by traditional table-based RL agent [Stampa].  DRL can
   provide more improvement to optimal control routing configuration by
   given-agent on complex networking.

5.  Multi-agent Reinforcement Learning (RL) Technologies

5.1.  Reinforcement Learning (RL)

   Agent reinforcement Learning (RL) is ml-based unsupervised algorithms
   based on an agent learning process.  Reinforcement Learning (RL) is
   normally used with a reward from centralized node (the global
   environment), and capable of autonomous acquirement and incorporation
   of knowledge.  It is continuously self-improving and becoming more
   efficient as the learning process from an agent experience to
   optimize management performance for autonomous learning
   process.[Sutton][Madera]

5.2.  Policy using Distance and Frequency

   Distance and Frequency algorithm uses the state occurrence frequency
   in addition to the distance to goal.  It avoids deadlocks and lets
   the agent escape the Dead, and it was derived to enhance agent
   optimal learning speed.  Distance-and-Frequency is based on more
   levels of agent visibility to enhance learning algorithm by an
   additional way that uses the state occurrence frequency.[Al-Dayaa]

5.3.  Distributed Computing Node

   Autonomous multi-agent learning process for network management
   environment is related to transfer optimized knowledge between agents
   on a given local node or distributed memory nodes over a
   communication network.








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5.4.  Agent Sharing Information

   This is a technique how agents can share information for optimal
   learning process.  The quality of agent decision making often depends
   on the willingness of agents to share a given learning information
   collected by agent learning process.  Sharing Information means that
   an agent would share and communicate the knowledge learned and
   acquired with or to other agents using reinforcement learning.

   Agents normally have limited resources and incomplete knowledge
   during learning exploration.  For that reason, the agents should take
   actions and transfer the states to the global environment under
   reinforcement learning (RL), then it would share the information with
   other agents, where all agents explore to reach their goals via a
   distributed reinforcement reward-based learning method on the
   existing local distributed memory nodes.

   MPI (Message Passing Interface) is used for communication way.  Even
   if the agents do not share the capabilities and resources to monitor
   an entire given large terrain environment, they are able to share the
   needed information to manage collaborative learning process for
   optimized management in distributed networking
   nodes.[Chowdappa][Minsuk]

5.5.  Deep Learning Technique

   Recently, some of advanced techniques using RL encounter and combine
   to deep learning in neural network that has made it possible to
   extract high-level features from raw data in compute vision
   [Krizhevsky].  There are many challenges under the deep learning
   models such as Convolution Neural Network, Recurrent Neural Network
   and etc.  The benefit of the deep learning applications is that lots
   of networking models, which have problematic issue due to complex and
   cluttered networking structure, can be used with large amounts of
   labelled training data.

   DRL can provide more extended and powerful scenarios to build
   networking models with optimized action controls, huge system states
   and real-time-based reward function.  Moreover, DRL has a significant
   advantage to set highly sequential data in a large model state space.
   In particular, the data distribution in RL is able to change as
   learning behaviors, that is a problem for deep learning approaches
   assumed by a fixed underlying distribution [Mnih].








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5.6.  Sub-goal Selection

   A new technical method for agent sub-goal selection in distributed
   nodes is introduced to reduce the agent initial random exploration
   with a given selected sub-goal.

   [TBD]

6.  Proposed Architecture for Deep Reinforcement Learning (DRL)

   The architecture using Reinforcement Learning (RL) describes a
   collaborative multi-agent-based system in distributed environments as
   shown in figure 1, where the architecture is combined with a hybrid
   architecture making use of both a master and slave architecture and a
   peer-to-peer.  The centralized node(global environment), assigns each
   slave computing node a portion of the distributed terrain and an
   initial number of agents.



         +-------------+
         |             |                       +-----------------+
         |             |<...... node 1 .......>|    terrain 1    |
         |             |                       +-----------------+
         | Global env. |                           +        |
         |  (node 0)   |                           |        |
         |             |                           |        +
         |             |                       +-----------------+
         |             |<...... node 2 .......>|    terrain 2    |
         |             |                       +-----------------+
         +-------------+

        Figure 1: Hybrid P2P and Master/Slave Architecture Overview

   Reinforcement Learning (RL) actions involve interacting with a given
   environment, so the environment provides an agent learning process
   with the elements as followings:

   o  Agent control actions, large states and cumulative rewards

   o  Initial data-set in memory

   o  Random or learning process in a given node

   o  Next, optimamization in neural network under reinforcement
      learning (RL)

   Additionally, agent actions with states toward its goal as below:



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   o  Agent continuously control actions to earn next optimized state
      based on its policy with reward

   o  After an agent reaches its goal, it can repeatedly collect the
      information collected by the random or learning process to next
      learning process for optimal management

   o  Agent learning process is optimized in the following phase and
      exploratory learning trials

   As shown in Figure2, we illustrate the fundamental architecture for
   relationship of a control action, large states space and optimized
   reward.  The agent does an action that leads to a reward from
   achieving an optimal path toward its goal.  Our works will be
   extended depending on the architecture.


                                   DRL Network
                                   +----------------------------------+
                                   |Q-Value1|                         |
                                   |--------+    +-------+    +------+|
       ...........Action...........|Q-Value2|----|Network|----|States||<......
       .                           |--------+    +-------+    +------+|      .
       .                           |Q-Value3|                         |      .
       .                           +----------------------------------+      .
       .                                                                     .
+---------+----------+                                                       .
| Global Environment |                                                       .
+---------+----------+                                                       .
       .                                                                     .
       .                                                                     .
       .                +-------------------+                          +----------+
       ................>+ Large State Space +............States.......>+ D-Memory +
                        +-------------------+                          +----------+


                     Figure 2: DRL work-flow Overview

7.  Use case of Multi-agent Reinforcement Learning (RL)

7.1.  Distributed Multi-agent Reinforcement Learning: Sharing
      Information Technique

   In this section, we deal with case of a collaborative distributed
   multi-agent, where each agent has same or different individual goals
   in a distributed environment.  Since sharing information scheme among
   the agents is problematic one, we need to expand on the work
   described by solving the challenging cases.



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   Basically, the main proposed algorithm is presented by distributed
   multi-agent reinforcement learning as below:

   +-------------------------------------------------------------------+
   | Proposed Algorithm                                                |
   +-------------------------------------------------------------------+
   | (1) Let Ni denote the number of node (i= 1, 2, 3 ...)             |
   |                                                                   |
   | (2) Let Aj denote the number of agent                             |
   |                                                                   |
   | (3) Let Dk denote the number of goals                             |
   |                                                                   |
   | (4) Place initial number of agents Aj, in random position (Xm,    |
   | Yn)                                                               |
   |                                                                   |
   | (5) Initialization of data-set memory for neural network          |
   |                                                                   |
   | (6) Copy neutal network Q and store as the data-set memory        |
   |                                                                   |
   | (7) Every Aj in Ni                                                |
   |                                                                   |
   | -----> (a) Do initial exploration (random) to corresponding Dk    |
   |                                                                   |
   | -----> (b) Do exploration (using RL) for Tx denote the number of  |
   | trial                                                             |
   +-------------------------------------------------------------------+

                        Table 1: Proposed Algorithm

   +-------------------------------------------------------------------+
   | Random Trial                                                      |
   +-------------------------------------------------------------------+
   | (1) Let Si denote the the current state                           |
   |                                                                   |
   | (2) Relinquish Si so that the other agent can occupy the position |
   |                                                                   |
   | (3) Assign the agent new position                                 |
   |                                                                   |
   | (4) Update the current state Si -> Si+1                           |
   +-------------------------------------------------------------------+

                           Table 2: Random Trial









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   +-------------------------------------------------------------------+
   | Optimal Trial                                                     |
   +-------------------------------------------------------------------+
   | (1) Let Si denote the the current state                           |
   |                                                                   |
   | (2) Let ACj denote a contorl action                               |
   |                                                                   |
   | (3) Let DRm denote discount reward                                |
   |                                                                   |
   | (4) Choose ACj <- Policy(Si, ACj) in neural network               |
   |                                                                   |
   | (5) Update and copy the network for learning process in the       |
   | global environment                                                |
   |                                                                   |
   | (6) Update the current state Si < Si+1-                           |
   |                                                                   |
   | (7) Repeat a available network control action                     |
   +-------------------------------------------------------------------+

                          Table 3: Optimal Trial

   Multi-agent reinforcement learning (RL) in distributed nodes can
   improve the overall system performance to transfer or share
   information from one node to another node in following cases;
   expanded complexity in RL technique with various experimental factors
   and conditions, analyzing multi-agent sharing information for agent
   learning process.

7.2.  Use case of Shortest Path-planning via sub-goal selection

   Sub-goal selection is a scheme of a distributed multi-agent RL
   technique based on selected intermediary agent sub-goal(s) with the
   aim of reducing the initial random trial.  The scheme is to improve
   the multi-agent system performance with asynchronously triggered
   exploratory phase(s) with selected agent sub-goal(s) for autonomous
   network management.

   [TBD]

8.  IANA Considerations

   There are no IANA considerations related to this document.

9.  Security Considerations

   [TBD]





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10.  Acknowledgements

   Carles Gomez has been funded in part by the Spanish Government
   (Ministerio de Educacion, Cultura y Deporte) through the Jose
   Castillejo grant CAS15/00336.  His contribution to this work has been
   carried out during his stay as a visiting scholar at the Computer
   Laboratory of the University of Cambridge.

11.  References

11.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

11.2.  Informative References

   [I-D.jiang-nmlrg-network-machine-learning]
              Jiang, S., "Network Machine Learning", ID draft-jiang-
              nmlrg-network-machine-learning-02, October 2016.

   [Megherbi]
              "Megherbi, D. B., Kim, Minsuk, Madera, Manual., A Study of
              Collaborative Distributed Multi-Goal and Multi-agent based
              Systems for Large Critical Key Infrastructures and
              Resources (CKIR) Dynamic Monitoring and Surveillance, IEEE
              International Conference on Technologies for Homeland
              Security", 2013.

   [Teiralbar]
              "Megherbi, D. B., Teiralbar, A. Boulenouar, J., A Time-
              varying Environment Machine Learning Technique for
              Autonomous Agent Shortest Path Planning, Proceedings of
              SPIE International Conference on Signal and Image
              Processing, Orlando, Florida", 2001.

   [Nasim]    "Nasim ArianpooEmail, Victor C.M. Leung, How network
              monitoring and reinforcement learning can improve tcp
              fairness in wireless multi-hop networks, EURASIP Journal
              on Wireless Communications and Networking", 2016.

   [Minsuk]   "Dalila B. Megherbi and Minsuk Kim, A Hybrid P2P and
              Master-Slave Cooperative Distributed Multi-Agent
              Reinforcement Learning System with Asynchronously
              Triggered Exploratory Trials and Clutter-index-based
              Selected Sub goals, IEEE CIG Conference", 2016.



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   [April]    "April Yu, Raphael Palefsky-Smith, Rishi Bedi, Deep
              Reinforcement Learning for Simulated Autonomous Vehicle
              Control, Stanford University", 2016.

   [Markus]   "Markus Kuderer, Shilpa Gulati, Wolfram Burgard, Learning
              Driving Styles for Autonomous Vehicles from Demonstration,
              Robotics and Automation (ICRA)", 2015.

   [Ann]      "Ann Nowe, Peter Vrancx, Yann De Hauwere, Game Theory and
              Multi-agent Reinforcement Learning, In book: Reinforcement
              Learning: State of the Art, Edition: Adaptation, Learning,
              and Optimization Volume 12", 2012.

   [Kok-Lim]  "Kok-Lim Alvin Yau, Hock Guan Goh, David Chieng, Kae
              Hsiang Kwong, Application of reinforcement learning to
              wireless sensor networks: models and algorithms, Published
              in Journal Computing archive Volume 97 Issue 11, Pages
              1045-1075", November 2015.

   [Sutton]   "Sutton, R. S., Barto, A. G., Reinforcement Learning: an
              Introduction, MIT Press", 1998.

   [Madera]   "Madera, M., Megherbi, D. B., An Interconnected Dynamical
              System Composed of Dynamics-based Reinforcement Learning
              Agents in a Distributed Environment: A Case Study,
              Proceedings IEEE International Conference on Computational
              Intelligence for Measurement Systems and Applications,
              Italy", 2012.

   [Al-Dayaa]
              "Al-Dayaa, H. S., Megherbi, D. B., Towards A Multiple-
              Lookahead-Levels Reinforcement-Learning Technique and Its
              Implementation in Integrated Circuits, Journal of
              Artificial Intelligence, Journal of Supercomputing. Vol.
              62, issue 1, pp. 588-61", 2012.

   [Chowdappa]
              "Chowdappa, Aswini., Skjellum, Anthony., Doss, Nathan,
              Thread-Safe Message Passing with P4 and MPI, Technical
              Report TR-CS-941025, Computer Science Department and NSF
              Engineering Research Center, Mississippi State
              University", 1994.

   [Mnih]     "V.Mnih and et al., Human-level Control Through Deep
              Reinforcement Learning, Nature 518.7540", 2015.






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   [Stampa]   "G Stamp, M Arias, etc., A Deep-reinforcement Learning
              Approach for Software-defined Networking Routing
              Optimization, cs.NI", 2017.

   [Krizhevsky]
              "A Krizhevsky, I Sutskever, and G Hinton, Imagenet
              classification with deep con- volutional neural networks,
              In Advances in Neural Information Processing Systems,
              1106-1114", 2012.

Authors' Addresses

   Min-Suk Kim
   Etri
   161 Gajeong-Dong Yuseung-Gu
   Daejeon  305-700
   Korea

   Phone: +82 42 860 5930
   Email: mskim16@etri.re.kr


   Yong-Geun Hong
   ETRI
   161 Gajeong-Dong Yuseung-Gu
   Daejeon  305-700
   Korea

   Phone: +82 42 860 6557
   Email: yghong@etri.re.kr


   Youn-Hee Han
   KoreaTech
   Byeongcheon-myeon Gajeon-ri, Dongnam-gu
   Choenan-si, Chungcheongnam-do
   330-708
   Korea

   Phone: +82 41 560 1486
   Email: yhhan@koreatech.ac.kr










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