Basic Reinforcement Learning Techniques to Control the Intensity of a Seeded Free-Electron Laser

Basic Reinforcement Learning Techniques to Control the Intensity of a Seeded Free-Electron Laser

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...

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Bibliographic Details
Main Authors: Niky Bruchon, Gianfranco Fenu, Giulio Gaio, Marco Lonza, Finn Henry O’Shea, Felice Andrea Pellegrino, Erica Salvato
Format: Article
Language:English
Published: MDPI AG 2020-05-01
Series:Electronics
Subjects:
reinforcement learning
free-electron laser
optimization
control-system
Online Access:https://www.mdpi.com/2079-9292/9/5/781

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