Faculté des Sciences appliquées
Faculté des Sciences appliquées

Master thesis : Reinforcement Learning for Network Control

Doloris, Samy ULiège
Promotor(s) : Mathy, Laurent ULiège ; Goloubew, Dimitry
Date of defense : 26-Jun-2019/27-Jun-2019 • Permalink :
Title : Master thesis : Reinforcement Learning for Network Control
Author : Doloris, Samy ULiège
Date of defense  : 26-Jun-2019/27-Jun-2019
Advisor(s) : Mathy, Laurent ULiège
Goloubew, Dimitry 
Committee's member(s) : Fonteneau, Raphaël ULiège
Donnet, Benoît ULiège
Language : English
Number of pages : 63
Discipline(s) : Engineering, computing & technology > Computer science
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems"
Faculty: Master thesis of the Faculté des Sciences appliquées


[fr] As computer networks become more dynamic, complex and sophisticated, they naturally become harder to manage and maintain.

More specifically, networking issues are not always well detected and remediated by existing networking control planes: those issues often requires human involvement to be properly taken care of.

The aim of this work is to consider computer networking problems in a more automatic or programmatic way. One approach to tackle this problem is to use Reinforcement Learning.

In this thesis, a monitoring pipeline and problem injection module are built on a test network, in order to train an intelligent agent using Reinforcement Learning techniques, able to properly detect and remediate some predefined networking issues.

The test network built in this study, is a physical one with which the agent and modules communicate using SSH.

Several experiments of increasing complexity are implemented and several Reinforcement Learning agents are trained and evaluated.

The overall goal of this project was to open up the way to implement Artificial Intelligence techniques in computer networking, a field where such techniques are rarely used, and the approach of Reinforcement Learning was shown successful in this work, under some assumptions.



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  • Doloris, Samy ULiège Université de Liège > Master ingé. civ. info., à fin.


Committee's member(s)

  • Fonteneau, Raphaël ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Dép. d'électric., électron. et informat. (Inst.Montefiore)
    ORBi View his publications on ORBi
  • Donnet, Benoît ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorithmique des grands systèmes
    ORBi View his publications on ORBi
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  • Total number of downloads 15

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