A Deep Reinforcement Learning Algorithm for Smart Control of Hysteresis Phenomena in a Mode-Locked Fiber Laser

Abstract

We experimentally demonstrate the application of a double deep Q-learning network algorithm (DDQN) for design of a self-starting fiber mode-locked laser. In contrast to the static optimization of a system design, the DDQN reinforcement algorithm is capable of learning the strategy of dynamic adjustment of the cavity parameters. Here, we apply the DDQN algorithm for stable soliton generation in a fiber laser cavity exploiting a nonlinear polarization evolution mechanism. The algorithm learns the hysteresis phenomena that manifest themselves as different pumping-power thresholds for mode-locked regimes for diverse trajectories of adjusting optical pumping.

Publication DOI: https://doi.org/10.3390/photonics9120921
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences
Aston University (General)
Funding Information: This work was supported by the Russian Science Foundation (Grant No. 17-72-30006-P).
Additional Information: Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Uncontrolled Keywords: fibre mode-locked lasers,reinforcement learning,hysteresis phenomena
Publication ISSN: 2304-6732
Data Access Statement: Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
Last Modified: 27 Nov 2024 08:20
Date Deposited: 20 Aug 2024 08:43
Full Text Link:
Related URLs: https://www.mdp ... 4-6732/9/12/921 (Publisher URL)
PURE Output Type: Article
Published Date: 2022-12
Published Online Date: 2022-11-30
Accepted Date: 2022-11-22
Authors: Kokhanovskiy, Alexey
Shevelev, Alexey
Serebrennikov, Kirill
Kuprikov, Evgeny
Turitsyn, Sergei K. (ORCID Profile 0000-0003-0101-3834)

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