Kuprikov Evgeny, Kokhanovskiy Alexey, Serebrennikov Kirill, Turitsyn Sergey
Novosibirsk State University, Pirogova str., 2, Novosibirsk, 630090, Russia.
Aston Institute of Photonic Technologies, Aston University, Birmingham, B4 7ET, UK.
Sci Rep. 2022 May 3;12(1):7185. doi: 10.1038/s41598-022-11274-w.
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.
现代激光系统的复杂性日益增加，这主要源于辐射的非线性动力学，使得对其操作的控制变得越来越具有挑战性，这就需要在激光工程中开发新的方法。机器学习方法为各种复杂系统的识别、控制和数据分析提供了经过验证的工具，最近已应用于锁模光纤激光器，特别关注三个关键领域：自启动、系统优化和表征。然而，为特定激光系统开发机器学习算法虽然是一个有趣的研究问题，但却是一项艰巨的任务，需要付出艰巨的努力并在实验室设置中调整大量超参数。尚不清楚这种学习能否顺利转移到与用于算法开发的特定激光器在设计上或通过改变环境参数而不同的系统。在这里，我们证明了一种基于试错和顺序决策的深度强化学习（DRL）方法可以成功用于控制锁模光纤激光系统中耗散孤子的产生。我们已经展示了深度Q学习算法能够概括有关激光系统的知识，以便找到稳定脉冲产生的条件。通过改变激光腔的泵浦功率来改变稳定产生区域，同时使用可调谐光谱滤波器作为控制工具。深度Q学习算法适合根据输出辐射的状态学习将光谱滤波器参数调整到稳定脉冲状态的轨迹。我们的结果证实了深度强化学习算法通过反馈控制非线性激光系统的潜力。我们还证明，基于高速生成数据的光纤锁模激光系统为基于大型数据集的各种机器学习概念提供了富有成效的光子测试平台。
