Policy gradient methods for free-electron laser and terahertz source optimization and stabilization at the FERMI free-electron laser at Elettra

In this article we report on the application of a model-free reinforcement learning method to the optimization of accelerator systems. We simplify a policy gradient algorithm to accelerator control from sophisticated algorithms that have recently been demonstrated to solve complex dynamic problems....

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Bibliographic Details
Published in:Physical review. Accelerators and beams 2020-12, Vol.23 (12), p.122802, Article 122802
Main Authors: O’Shea, F. H., Bruchon, N., Gaio, G.
Format: Article
Language:English
Subjects:
Algorithms
Free electron lasers
Lasers
Optimization
Parameters
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Summary:In this article we report on the application of a model-free reinforcement learning method to the optimization of accelerator systems. We simplify a policy gradient algorithm to accelerator control from sophisticated algorithms that have recently been demonstrated to solve complex dynamic problems. After outlining a theoretical basis for the functioning of the algorithm, we explore the small hyperparameter space to develop intuition about said parameters using a simple number-guess environment. Finally, we demonstrate the algorithm optimizing both a free-electron laser and an accelerator-based terahertz source in-situ. The algorithm is applied to different accelerator control systems and optimizes the desired signals in a few hundred steps without any domain knowledge using up to five control parameters. In addition, the algorithm shows modest tolerance to accelerator fault conditions without any special preparation for such conditions.
ISSN:2469-9888
2469-9888
DOI:10.1103/PhysRevAccelBeams.23.122802