Kokhanovskiy, Alexey, Shevelev, Alexey, Serebrennikov, Kirill, Kuprikov, Evgeny and Turitsyn, Sergei K. (2022). A Deep Reinforcement Learning Algorithm for Smart Control of Hysteresis Phenomena in a Mode-Locked Fiber Laser. Photonics, 9 (12),
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 |
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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. ( 0000-0003-0101-3834) |