Self-starting fiber mode-locked laser assisted with DDQN algorithm

Standard

Self-starting fiber mode-locked laser assisted with DDQN algorithm. / Kokhanovskiy, A.; Serebrennikov, K.; Shevelev, A. et al.

2022 International Conference Laser Optics, ICLO 2022 - Proceedingss. Institute of Electrical and Electronics Engineers Inc., 2022. 9839918 (2022 International Conference Laser Optics, ICLO 2022 - Proceedingss).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Kokhanovskiy, A, Serebrennikov, K, Shevelev, A & Kuprikov, E 2022, Self-starting fiber mode-locked laser assisted with DDQN algorithm. in 2022 International Conference Laser Optics, ICLO 2022 - Proceedingss., 9839918, 2022 International Conference Laser Optics, ICLO 2022 - Proceedingss, Institute of Electrical and Electronics Engineers Inc., 2022 International Conference Laser Optics, ICLO 2022, St. Petersburg, Russian Federation, 20.06.2022. https://doi.org/10.1109/ICLO54117.2022.9839918

APA

Kokhanovskiy, A., Serebrennikov, K., Shevelev, A., & Kuprikov, E. (2022). Self-starting fiber mode-locked laser assisted with DDQN algorithm. In 2022 International Conference Laser Optics, ICLO 2022 - Proceedingss [9839918] (2022 International Conference Laser Optics, ICLO 2022 - Proceedingss). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICLO54117.2022.9839918

Vancouver

Kokhanovskiy A, Serebrennikov K, Shevelev A, Kuprikov E. Self-starting fiber mode-locked laser assisted with DDQN algorithm. In 2022 International Conference Laser Optics, ICLO 2022 - Proceedingss. Institute of Electrical and Electronics Engineers Inc. 2022. 9839918. (2022 International Conference Laser Optics, ICLO 2022 - Proceedingss). doi: 10.1109/ICLO54117.2022.9839918

Author

Kokhanovskiy, A. ; Serebrennikov, K. ; Shevelev, A. et al. / Self-starting fiber mode-locked laser assisted with DDQN algorithm. 2022 International Conference Laser Optics, ICLO 2022 - Proceedingss. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 International Conference Laser Optics, ICLO 2022 - Proceedingss).

BibTeX

@inproceedings{89f840c50c704682bc4ff9bac9f3e12a,

title = "Self-starting fiber mode-locked laser assisted with DDQN algorithm",

abstract = "Here, we investigate the performance of Double-Deep Q-learning (DDQL) algorithm for designing self-starting fiber laser. Fiber mode-locked laser based on nonlinear polarization effect was chosen as an experimental platform. We show that the proposed algorithm is capable to learn non-trivial hysteresis dynamics inside the laser in order to achieve stable soliton generation. ",

keywords = "machine learning algorithms, mode-locked fiber laser, reinforcement learning algorithms, solitons",

author = "A. Kokhanovskiy and K. Serebrennikov and A. Shevelev and E. Kuprikov",

note = "Funding Information: This work was supported by Russian Science Foundation (17-72-30006). Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Conference Laser Optics, ICLO 2022 ; Conference date: 20-06-2022 Through 24-06-2022",

year = "2022",

doi = "10.1109/ICLO54117.2022.9839918",

language = "English",

isbn = "9781665466646",

series = "2022 International Conference Laser Optics, ICLO 2022 - Proceedingss",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2022 International Conference Laser Optics, ICLO 2022 - Proceedingss",

address = "United States",

}

RIS

TY - GEN

T1 - Self-starting fiber mode-locked laser assisted with DDQN algorithm

AU - Kokhanovskiy, A.

AU - Serebrennikov, K.

AU - Shevelev, A.

AU - Kuprikov, E.

PY - 2022

Y1 - 2022

N2 - Here, we investigate the performance of Double-Deep Q-learning (DDQL) algorithm for designing self-starting fiber laser. Fiber mode-locked laser based on nonlinear polarization effect was chosen as an experimental platform. We show that the proposed algorithm is capable to learn non-trivial hysteresis dynamics inside the laser in order to achieve stable soliton generation.

AB - Here, we investigate the performance of Double-Deep Q-learning (DDQL) algorithm for designing self-starting fiber laser. Fiber mode-locked laser based on nonlinear polarization effect was chosen as an experimental platform. We show that the proposed algorithm is capable to learn non-trivial hysteresis dynamics inside the laser in order to achieve stable soliton generation.

KW - machine learning algorithms

KW - mode-locked fiber laser

KW - reinforcement learning algorithms

KW - solitons

UR - http://www.scopus.com/inward/record.url?scp=85136373214&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/9a778a2e-5056-3d17-9d0c-783dd93ac05d/

U2 - 10.1109/ICLO54117.2022.9839918

DO - 10.1109/ICLO54117.2022.9839918

M3 - Conference contribution

AN - SCOPUS:85136373214

SN - 9781665466646

T3 - 2022 International Conference Laser Optics, ICLO 2022 - Proceedingss

BT - 2022 International Conference Laser Optics, ICLO 2022 - Proceedingss

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 International Conference Laser Optics, ICLO 2022

Y2 - 20 June 2022 through 24 June 2022

ER -

ID: 37039346