Journal Article

PUBDB-2023-03590

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning

Kaiser, J. (Corresponding author)DESY* ; Xu, C.Extern* ; Eichler, A.XFEL.EU*DESY* ; Santamaria Garcia, A.Extern* ; Stein, O.DESY* ; Bruendermann, E.Extern* ; Kuropka, W.Extern*DESY* ; Dinter, H.DESY* ; Mayet, F.Extern*DESY* ; Vinatier, T.DESY* ; Burkart, F.DESY* ; Schlarb, H.DESY*

2024
Macmillan Publishers Limited, part of Springer Nature [London]

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Please use a persistent id in citations: doi:10.1038/s41598-024-66263-y  doi:10.3204/PUBDB-2023-03590

Abstract: Online tuning of particle accelerators is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods like Bayesian optimisation (BO) hold great promise in improving plant performance and reducing tuning times. At the same time, reinforcement learning (RL) is a capable method of learning intelligent controllers, and recent work shows that RL can also be used to train domain-specialised optimisers in so-called reinforcement learning-trained optimisation (RLO). In parallel efforts, both algorithms have found successful adoption in particle accelerator tuning. Here we present a comparative case study, assessing the performance of both algorithms while providing a nuanced analysis of the merits and the practical challenges involved in deploying them to real-world facilities. Our results will help practitioners choose a suitable learning-based tuning algorithm for their tuning tasks, accelerating the adoption of autonomous tuning algorithms, ultimately improving the availability of particle accelerators and pushing their operational limits.

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Contributing Institute(s):

1. Strahlkontrollen (MSK)
2. Beschleunigerphysik Fachgruppe MPY1 (MPY1)
3. externe Institute im Bereich Photon Science (KIT)

Research Program(s):

1. 621 - Accelerator Research and Development (POF4-621) (POF4-621)
2. InternLabs-0011 - HIR3X - Helmholtz International Laboratory on Reliability, Repetition, Results at the most advanced X-ray Sources (2020_InternLabs-0011) (2020_InternLabs-0011)
3. ZT-I-PF-5-6 - Autonomous Accelerator (AA) (2020_ZT-I-PF-5-6) (2020_ZT-I-PF-5-6)

Experiment(s):

1. Accelerator Research Experiment at SINBAD

Appears in the scientific report 2024

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