Deep reinforcement learning for self-tuning laser source of dissipative solitons

Abstract

Increasing complexity of modern laser systems, mostly originated from the nonlinear dynamics of radiation, makes control of their operation more and more challenging, calling for development of new approaches in laser engineering. Machine learning methods, providing proven tools for identification, control, and data analytics of various complex systems, have been recently applied to mode-locked fiber lasers with the special focus on three key areas: self-starting, system optimization and characterization. However, the development of the machine learning algorithms for a particular laser system, while being an interesting research problem, is a demanding task requiring arduous efforts and tuning a large number of hyper-parameters in the laboratory arrangements. It is not obvious that this learning can be smoothly transferred to systems that differ from the specific laser used for the algorithm development by design or by varying environmental parameters. Here we demonstrate that a deep reinforcement learning (DRL) approach, based on trials and errors and sequential decisions, can be successfully used for control of the generation of dissipative solitons in mode-locked fiber laser system. We have shown the capability of deep Q-learning algorithm to generalize knowledge about the laser system in order to find conditions for stable pulse generation. Region of stable generation was transformed by changing the pumping power of the laser cavity, while tunable spectral filter was used as a control tool. Deep Q-learning algorithm is suited to learn the trajectory of adjusting spectral filter parameters to stable pulsed regime relying on the state of output radiation. Our results confirm the potential of deep reinforcement learning algorithm to control a nonlinear laser system with a feed-back. We also demonstrate that fiber mode-locked laser systems generating data at high speed present a fruitful photonic test-beds for various machine learning concepts based on large datasets.

Publication DOI: https://doi.org/10.1038/s41598-022-11274-w
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences
Additional Information: © 2022. The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/. Funding: This work was supported by the Russian Science Foundation (Grant No. 17-72-30006).
Uncontrolled Keywords: General
Publication ISSN: 2045-2322
Last Modified: 22 Apr 2024 07:30
Date Deposited: 04 May 2022 09:58
Full Text Link:
Related URLs: https://www.nat ... 598-022-11274-w (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2022-05-03
Accepted Date: 2022-04-11
Submitted Date: 2022-01-17
Authors: Kuprikov, Evgeny
Kokhanovskiy, Alexey
Serebrennikov, Kirill
Turitsyn, Sergey (ORCID Profile 0000-0003-0101-3834)

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