Photonics Research, Volume. 10, Issue 6, 1491(2022)
Deep reinforcement with spectrum series learning control for a mode-locked fiber laser
Fig. 1. GNLSE simulation result from the NPE-based mode-locking laser system. (a) Spectral evolution when EPC is in
Fig. 4. MDRL environment layout. LD, laser diode; WDM, 980/1060 nm wavelength division multiplexer; YDF, ytterbium-doped fiber; C, coupler; SMF, single-mode fiber; P, polarizer; I, isolator; EPC, electrical polarization controller; SF, optical spectrum filter; D, diagnostic optical spectrum analyzer.
Fig. 5. Spectrum and time-wave evolution during MDRL search. (a) Spectrum evolution data from the spectrum analyzer. (b) Time-wave evolution data from the high-speed photodetector and oscilloscope. (c) Obtained reward at each search step. (d) Direct autocorrelation output (blue line) and autocorrelation output after dispersion compensation (orange square, purple line).
Fig. 6. Mode-locked state switch by MSP. (a) Mode-locked state switch by minimizing the difference between
Fig. 7. Algorithm performance. (a) Total search step from 100 random initial states to the mode-locked state using MDRL (purple solid circle), DDPG (orange solid square), and genetic algorithm (green solid triangle). (b) Search stability test at different temperatures with MDRL (purple), DDPG (orange), and genetic algorithm (green).
Fig. 8. Search stability test at different temperatures with MDRL (purple), DDPG (orange), and genetic algorithm (green).
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Zhan Li, Shuaishuai Yang, Qi Xiao, Tianyu Zhang, Yong Li, Lu Han, Dean Liu, Xiaoping Ouyang, Jianqiang Zhu, "Deep reinforcement with spectrum series learning control for a mode-locked fiber laser," Photonics Res. 10, 1491 (2022)
Category: Lasers and Laser Optics
Received: Feb. 4, 2022
Accepted: Apr. 29, 2022
Published Online: May. 25, 2022
The Author Email: Dean Liu (liudean@siom.ac.cn), Xiaoping Ouyang (oyxp@siom.ac.cn)