| Paper |
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| TUPOPT013 |
Twin Delayed Deep Deterministic Policy Gradient for Free-electron Laser Online Optimization |
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- M. Cai, C. Feng, L. Tu, Z.T. Zhao, Z.H. Zhu
SINAP, Shanghai, People’s Republic of China
- C. Feng, K.Q. Zhang, Z.T. Zhao
SSRF, Shanghai, People’s Republic of China
- D. Gu
SARI-CAS, Pudong, Shanghai, People’s Republic of China
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X-ray free-electron lasers (FEL) have contributed to many frontier applications of nanoscale science which benefit from its extraordinary properties. During FEL commissioning, the beam status optimization especially orbit correction is particularly significant for FEL amplification. For example, the deviation between beam orbit and the magnetic center of undulator can affect the interaction between the electron beam and the FEL pulse. Usually, FEL commissioning requires a lot of effort for multi-dimensional parameters optimization in a time-varying system. Therefore, advanced algorithms are needed to facilitate the commissioning procedure. In this paper, we propose an online method to optimize the FEL power and transverse coherence by using a twin delayed deep deterministic policy gradient (TD3) algorithm. The algorithm exhibits more stable learning convergence and improves learning performance because the overestimation bias of policy gradient methods is suppressed.
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| DOI • |
reference for this paper
※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOPT013
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| About • |
Received ※ 17 May 2022 — Revised ※ 14 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 22 June 2022 |
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