Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Nicolás Jara 1 ; Hermann Pempelfort 1 ; Erick Viera 1 ; Juan Sanchez 1 ; Gabriel España 2 and Danilo Borquez-Paredes 2

Affiliations: 1 Department of Electronics Engineering, Universidad Tecnica Federico Santa Maria, Valparaíso, Chile ; 2 Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Viña del Mar, Chile

Keyword(s): Optical Networks, Framework, Deep Reinforcement Learning, Simulation Technique.

Abstract: A novel open-source toolkit for a straightforward implementation of deep reinforcement learning (DRL) techniques to address any resource allocation problem in current and future optical network architectures is presented. The tool follows OpenAI GYMNASIUM guidelines, presenting a versatile framework adaptable to any optical network architecture. Our tool is compatible with the Stable Baseline library, allowing the use of any agent available in the literature or created by the software user. For the training and testing process, we adapted the Flex Net Sim Simulator to be compatible with our toolkit. Using three agents from the Stable Baselines library, we exemplify our framework performance to demonstrate the tool’s overall architecture and assess its functionality. Results demonstrate how easily and consistently our tool can solve optical network resource allocation challenges using just a few lines of code applying Deep Reinforcement Learning techniques and ad-hoc heuristics algori thms. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 70.40.220.129

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Jara, N.; Pempelfort, H.; Viera, E.; Sanchez, J.; España, G. and Borquez-Paredes, D. (2024). DREAM-ON GYM: A Deep Reinforcement Learning Environment for Next-Gen Optical Networks. In Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH; ISBN 978-989-758-708-5; ISSN 2184-2841, SciTePress, pages 215-222. DOI: 10.5220/0012715900003758

@conference{simultech24,
author={Nicolás Jara. and Hermann Pempelfort. and Erick Viera. and Juan Sanchez. and Gabriel España. and Danilo Borquez{-}Paredes.},
title={DREAM-ON GYM: A Deep Reinforcement Learning Environment for Next-Gen Optical Networks},
booktitle={Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH},
year={2024},
pages={215-222},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012715900003758},
isbn={978-989-758-708-5},
issn={2184-2841},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH
TI - DREAM-ON GYM: A Deep Reinforcement Learning Environment for Next-Gen Optical Networks
SN - 978-989-758-708-5
IS - 2184-2841
AU - Jara, N.
AU - Pempelfort, H.
AU - Viera, E.
AU - Sanchez, J.
AU - España, G.
AU - Borquez-Paredes, D.
PY - 2024
SP - 215
EP - 222
DO - 10.5220/0012715900003758
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic