Toward the Application of Reinforcement Learning to the Intensity Control of a Seeded Free-Electron Laser

Author(s):  
Niky Bruchon ◽  
Gianfranco Fenu ◽  
Giulio Gaio ◽  
Marco Lonza ◽  
Felice Andrea Pellegrino ◽  
...  
Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 781 ◽  
Author(s):  
Niky Bruchon ◽  
Gianfranco Fenu ◽  
Giulio Gaio ◽  
Marco Lonza ◽  
Finn Henry O’Shea ◽  
...  

Optimal tuning of particle accelerators is a challenging task. Many different approaches have been proposed in the past to solve two main problems—attainment of an optimal working point and performance recovery after machine drifts. The most classical model-free techniques (e.g., Gradient Ascent or Extremum Seeking algorithms) have some intrinsic limitations. To overcome those limitations, Machine Learning tools, in particular Reinforcement Learning (RL), are attracting more and more attention in the particle accelerator community. We investigate the feasibility of RL model-free approaches to align the seed laser, as well as other service lasers, at FERMI, the free-electron laser facility at Elettra Sincrotrone Trieste. We apply two different techniques—the first, based on the episodic Q-learning with linear function approximation, for performance optimization; the second, based on the continuous Natural Policy Gradient REINFORCE algorithm, for performance recovery. Despite the simplicity of these approaches, we report satisfactory preliminary results, that represent the first step toward a new fully automatic procedure for the alignment of the seed laser to the electron beam. Such an alignment is, at present, performed manually.


1983 ◽  
Vol 44 (C1) ◽  
pp. C1-385-C1-385
Author(s):  
E. D. Shaw ◽  
R. M. Emanuelson ◽  
G. A. Herbster

1983 ◽  
Vol 44 (C1) ◽  
pp. C1-367-C1-367
Author(s):  
W. Becker ◽  
J. K. McIver

2010 ◽  
Vol 130 (2) ◽  
pp. 209-212 ◽  
Author(s):  
Daisuke Ishizuka ◽  
Keijiro Sakai ◽  
Nobuyuki Iwata ◽  
Hirofumi Yajima ◽  
Hiroshi Yamamoto

2011 ◽  
Vol 131 (2) ◽  
pp. 68-71
Author(s):  
Etsuo FUJIWARA ◽  
Eiichi ANAYAMA ◽  
Yuichiro KATSUTA ◽  
Toshiki IZUTANI ◽  
Daichi OKUHARA ◽  
...  

2014 ◽  
Vol 134 (12) ◽  
pp. 836-839
Author(s):  
Junichi INOUE ◽  
Yuji TANAKA ◽  
Yuki MATSUMOTO ◽  
Kensuke KANDA

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