The Optical RL-Gym: an open-source toolkit for applying reinforcement learning in optical networks
Paper i proceeding, 2020

Reinforcement Learning (RL) is leading to important breakthroughs in several areas (e.g., self-driving vehicles, robotics, and network automation). Part of its success is due to the existence of toolkits (e.g., OpenAI Gym) to implement standard RL tasks. On the one hand, they allow for the quick implementation and testing of new ideas. On the other, these toolkits ensure easy reproducibility via quick and fair benchmarking. RL is also gaining traction in the optical networks research community, showing promising results while solving several use cases. However, there are many scenarios where the benefits of RL-based solutions remain still unclear. A possible reason for this is the steep learning curve required to tailor RL-based frameworks to each specific use case. This, in turn, might delay or even prevent the development of new ideas. This paper introduces the Optical Network Reinforcement-Learning-Gym (Optical RL-Gym), an open-source toolkit that can be used to apply RL to problems related to optical networks. The Optical RL-Gym follows the principles established by the OpenAI Gym, the de-facto standard for RL environments. Optical RL-Gym allows for the quick integration with existing RL agents, as well as the possibility to build upon several already available environments to implement and solve more elaborated use cases related to the optical networks research area. The capabilities and the benefits of the proposed toolkit are illustrated by using the Optical RL-Gym to solve two different service provisioning problems.

Machine learning

Reinforcement learning environments

Resource assignment

Autonomous network management

Författare

Carlos Natalino Da Silva

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Paolo Monti

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

International Conference on Transparent Optical Networks

21627339 (eISSN)

Vol. 2020-July 9203239

22nd International Conference on Transparent Optical Networks (ICTON) 2020
Bari, Italy,

Automation of Network edge Infrastructure & Applications with aRtificiAl intelligence, ANIARA

VINNOVA (2020-00763), 2020-06-15 -- 2023-10-31.

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Robotteknik och automation

Datavetenskap (datalogi)

Datorsystem

DOI

10.1109/ICTON51198.2020.9203239

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2024-01-03