Deep reinforcement learning control of white-light continuum generation. Valensise, C. M., Giuseppi, A., Cerullo, G., & Polli, D. Optica, 8(2):239–242, OSA, Feb, 2021. ![Deep reinforcement learning control of white-light continuum generation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@ARTICLE{Valensise21,
author = {Carlo M. Valensise and Alessandro Giuseppi and Giulio Cerullo and Dario Polli},
journal = {Optica},
keywords = {Beam splitters; Neural networks; Nonlinear spectroscopy; Optical amplifiers; Parametric down conversion; White light},
number = {2},
pages = {239--242},
publisher = {OSA},
title = {Deep reinforcement learning control of white-light continuum generation},
volume = {8},
month = {Feb},
year = {2021},
document_type={Article},
url = {http://www.osapublishing.org/optica/abstract.cfm?URI=optica-8-2-239},
doi = {10.1364/OPTICA.414634},
abstract = {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.},
}
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