Reinforcement Learning for Network Control

Abstract

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.