Multistability manipulation by reinforcement learning algorithm inside mode‐locked fiber laser
Alexey Kokhanovskiy, Evgeny Kuprikov, Kirill Serebrennikov, Aram Mkrtchyan, Ayvaz Davletkhanov, Alexey Bunkov, Dmitry V. Krasnikov, М. В. Шашков, Albert G. Nasibulin, Yuriy G. Gladush
Abstract
Fiber mode-locked lasers are nonlinear optical systems that provide ultrashort pulses at high repetition rates. However, adjusting the cavity parameters is often a challenging task due to the intrinsic multistability of a laser system. Depending on the adjustment of the cavity parameters, the optical output may vary significantly, including Q-switching, single and multipulse, and harmonic mode-locked regimes. In this study, we demonstrate an experimental implementation of the Soft Actor-Critic algorithm for generating a harmonic mode-locked regime inside a state-of-the-art fiber laser with an ion-gated nanotube saturable absorber. The algorithm employs nontrivial strategies to achieve a guaranteed harmonic mode-locked regime with the highest order by effectively managing the pumping power of a laser system and the nonlinear transmission of a nanotube absorber. Our results demonstrate a robust and feasible machine-learning-based approach toward an automatic system for adjusting nonlinear optical systems with the presence of multistability phenomena.