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@InProceedings{bruchon:icalepcs2019-wepha021,
author = {N. Bruchon and G. Fenu and G. Gaio and M. Lonza and F.A. Pellegrino and E. Salvato},
title = {{Free-Electron Laser Optimization with Reinforcement Learning}},
booktitle = {Proc. ICALEPCS'19},
pages = {1122--1126},
paper = {WEPHA021},
language = {english},
keywords = {laser, FEL, electron, controls, free-electron-laser},
venue = {New York, NY, USA},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {17},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2020},
issn = {2226-0358},
isbn = {978-3-95450-209-7},
doi = {10.18429/JACoW-ICALEPCS2019-WEPHA021},
url = {https://jacow.org/icalepcs2019/papers/wepha021.pdf},
note = {https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA021},
abstract = {Reinforcement Learning (RL) is one of the most promising techniques in Machine Learning because of its modest computational requirements with respect to other algorithms. RL uses an agent that takes actions within its environment to maximize a reward related to the goal it is designed to achieve. We have recently used RL as a model-free approach to improve the performance of the FERMI Free Electron Laser. A number of machine parameters are adjusted to find the optimum FEL output in terms of intensity and spectral quality. In particular we focus on the problem of the alignment of the seed laser with the electron beam, initially using a simplified model and then applying the developed algorithm on the real machine. This paper reports the results obtained and discusses pros and cons of this approach with plans for future applications.},
}