The Quest for Safe Deep Reinforcement Learning-driven Network Slicing: Progress, Pitfalls and Potential - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Other Publications Year : 2023

The Quest for Safe Deep Reinforcement Learning-driven Network Slicing: Progress, Pitfalls and Potential

Abstract

The quest for the efficient and autonomous placement of network services is crucial to progressing towards a fully automated network, commonly referred to as a “zero-touch network”. In this presentation, we focus on the results of our research into exploiting the potential of deep reinforcement learning strategies in the field of network slicing. Our aim is not only to elucidate the remarkable advantages these techniques offer over conventional methods but also to address their inherent limitations. In addition, we will highlight new avenues that we are currently exploring to enhance the reliability aspects of network slicing when deploying techniques based on deep reinforcement learning. During this presentation, we will outline some of the solutions and tangible results we have obtained in the pursuit of this critical mission.
Fichier principal
Vignette du fichier
safe01k-hadjadj-aoulSC.pdf (198.72 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04368656 , version 1 (01-01-2024)

Licence

Attribution

Identifiers

Cite

Yassine Hadjadj-Aoul. The Quest for Safe Deep Reinforcement Learning-driven Network Slicing: Progress, Pitfalls and Potential. 2023, pp.1-1. ⟨10.1145/3630050.3630262⟩. ⟨hal-04368656⟩
8 View
4 Download

Altmetric

Share

Gmail Facebook X LinkedIn More