From Design to Deployment of Zero-touch Deep Reinforcement Learning WLANs

TL;DR

This paper discusses the challenges and guidelines for deploying Deep Reinforcement Learning in WLAN radio resource management within zero-touch network automation, aiming to facilitate real-world application of DRL.

Contribution

It provides practical guidelines for deploying DRL in WLAN management, addressing real-world challenges and extending insights to broader network automation contexts.

Findings

Identified key challenges in deploying DRL for WLAN management

Proposed guidelines to overcome deployment hurdles

Validated guidelines through case studies or experiments

Abstract

Machine learning (ML) is increasingly used to automate networking tasks, in a paradigm known as zero-touch network and service management (ZSM). In particular, Deep Reinforcement Learning (DRL) techniques have recently gathered much attention for their ability to learn taking complex decisions in different fields. In the ZSM context, DRL is an appealing candidate for tasks such as dynamic resource allocation, that is generally formulated as hard optimization problems. At the same time, successful training and deployment of DRL agents in real-world scenarios faces a number of challenges that we outline and address in this paper. Tackling the case of Wireless Local Area Network (WLAN) radio resource management, we report guidelines that extend to other usecases and more general contexts.