Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Reliable and Accurate Autonomous Flow Operation based on Off-line Trained Reinforcement Learning

Not Accessible

Your library or personal account may give you access

Abstract

A RL agent trained offline for reliability and able to refine its policies during online operation is proposed. Results for three illustrative flow automation use cases show remarkable performance with extraordinary adaptability to changes.

© 2021 The Author(s)

PDF Article
More Like This
Combining Long-Short Term Memory and Reinforcement Learning for Improved Autonomous Network Operation

Fatemehsadat Tabatabaeimehr, Sima Barzegar, Marc Ruiz, and Luis Velasco
F2G.4 Optical Fiber Communication Conference (OFC) 2021

How IBN can be Embedded within Optical Transport Networks

Luis Velasco
F1C.4 Optical Fiber Communication Conference (OFC) 2021

A Subcarrier-Slot Autonomous Partition Scheme Based on Deep-Reinforcement-Learning in Elastic Optical Networks

Xin Wang, Yue-Cai Huang, Jie Liu, and Siyuan Yu
M4A.211 Asia Communications and Photonics Conference (ACP) 2019

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.