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

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Deep reinforcement learning for self-tuning laser source of dissipative solitons. / Kuprikov, Evgeny ; Kokhanovskiy, Alexey; Serebrennikov, Kirill et al.

In: Scientific Reports, Vol. 12, No. 1, 7185, 12.2022.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{a0fd666175e2493d98c4bcd5c440e84c,

title = "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.",

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

note = "Funding Information: This work was supported by the Russian Science Foundation (Grant No. 17-72-30006). Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

month = dec,

doi = "10.1038/s41598-022-11274-w",

language = "English",

volume = "12",

journal = "Scientific Reports",

issn = "2045-2322",

publisher = "Nature Publishing Group",

number = "1",

}

RIS

TY - JOUR

T1 - Deep reinforcement learning for self-tuning laser source of dissipative solitons

AU - Kuprikov, Evgeny

AU - Kokhanovskiy, Alexey

AU - Serebrennikov, Kirill

AU - Turitsyn, Sergey

PY - 2022/12

Y1 - 2022/12

N2 - 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.

AB - 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.

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UR - https://www.elibrary.ru/item.asp?id=48582091

UR - https://www.mendeley.com/catalogue/c91ab8d4-7425-3224-8868-cde1802a6b3b/

U2 - 10.1038/s41598-022-11274-w

DO - 10.1038/s41598-022-11274-w

M3 - Article

C2 - 35504948

AN - SCOPUS:85129344502

VL - 12

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 7185

ER -

ID: 36059667