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.

中文翻译：

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锁模光纤激光器内部强化学习算法的多稳定性操纵

光纤锁模激光器是非线性光学系统，能够以高重复率提供超短脉冲。然而，由于激光系统固有的多稳定性，调整腔参数通常是一项具有挑战性的任务。根据腔参数的调整，光学输出可能会发生显着变化，包括 Q 开关、单脉冲和多脉冲以及谐波锁模机制。在这项研究中，我们展示了 Soft Actor-Critic 算法的实验实现，用于在具有离子门控纳米管可饱和吸收器的最先进光纤激光器内生成谐波锁模状态。该算法采用重要的策略，通过有效管理激光系统的泵浦功率和纳米管吸收器的非线性传输，实现最高阶的有保证的谐波锁模状态。我们的结果证明了一种稳健且可行的基于机器学习的方法，用于调整存在多稳态现象的非线性光学系统的自动系统。