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

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A Deep Reinforcement Learning Algorithm for Smart Control of Hysteresis Phenomena in a Mode-Locked Fiber Laser. / Kokhanovskiy, Alexey; Shevelev, Alexey; Serebrennikov, Kirill и др.

в: Photonics, Том 9, № 12, 921, 01.12.2022.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

BibTeX

@article{957b23d28d4b4dbab51e9fbc1d13ed1c,

title = "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.",

keywords = "fiber mode-locked lasers, hysteresis phenomena, reinforcement learning",

author = "Alexey Kokhanovskiy and Alexey Shevelev and Kirill Serebrennikov and Evgeny Kuprikov and Sergey Turitsyn",

note = "Funding: This work was supported by the Russian Science Foundation (Grant No. 17-72-30006-P).",

year = "2022",

month = dec,

day = "1",

doi = "10.3390/photonics9120921",

language = "English",

volume = "9",

journal = "Photonics",

issn = "2304-6732",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

number = "12",

}

RIS

TY - JOUR

T1 - A Deep Reinforcement Learning Algorithm for Smart Control of Hysteresis Phenomena in a Mode-Locked Fiber Laser

AU - Kokhanovskiy, Alexey

AU - Shevelev, Alexey

AU - Serebrennikov, Kirill

AU - Kuprikov, Evgeny

AU - Turitsyn, Sergey

N1 - Funding: This work was supported by the Russian Science Foundation (Grant No. 17-72-30006-P).

PY - 2022/12/1

Y1 - 2022/12/1

N2 - 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.

AB - 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.

KW - fiber mode-locked lasers

KW - hysteresis phenomena

KW - reinforcement learning

UR - https://www.scopus.com/inward/record.url?eid=2-s2.0-85144675885&partnerID=40&md5=6c1645228290de7a7368a37e6ccc55af

UR - https://www.mendeley.com/catalogue/34dc78fb-7254-3560-a419-e88a3fb583dc/

U2 - 10.3390/photonics9120921

DO - 10.3390/photonics9120921

M3 - Article

VL - 9

JO - Photonics

JF - Photonics

SN - 2304-6732

IS - 12

M1 - 921

ER -

ID: 44675204