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Deep reinforcement learning control of white-light continuum generation
Optica ( IF 10.4 ) Pub Date : 2021-02-10 , DOI: 10.1364/optica.414634
Carlo M. Valensise , Alessandro Giuseppi , Giulio Cerullo , Dario Polli

White-light continuum (WLC) generation in bulk media finds numerous applications in ultrafast optics and spectroscopy. Due to the complexity of the underlying spatiotemporal dynamics, WLC optimization typically follows empirical procedures. Deep reinforcement learning (RL) is a branch of machine learning dealing with the control of automated systems using deep neural networks. In this Letter, we demonstrate the capability of a deep RL agent to generate a long-term-stable WLC from a bulk medium without any previous knowledge of the system dynamics or functioning. This work demonstrates that RL can be exploited effectively to control complex nonlinear optical experiments.

中文翻译:

白光连续体生成的深度强化学习控制

散装介质中产生白光连续体(WLC)在超快光学和光谱学中有许多应用。由于底层时空动力学的复杂性,WLC优化通常遵循经验过程。深度强化学习(RL)是机器学习的一个分支,涉及使用深度神经网络控制自动化系统。在这封信中,我们演示了深度RL代理从大容量媒体生成长期稳定的WLC的能力,而无需事先了解系统动力学或功能。这项工作表明,可以有效地利用RL来控制复杂的非线性光学实验。
更新日期:2021-02-21
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