JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@unpublished{su:sap2023-tupb036,
% --- JACoW template Dec 2024 ---
author = {C.G. Su and Z.J. Wang},
title = {{Optimisation of RFQ Transmission Efficiency Based on Reinforcement Learning Control Policy}},
eventtitle = {14th Symp. Accel. Phys. (SAP'23)},
eventdate = {2023-07-10/2023-07-12},
language = {english},
intype = {presented at},
series = {Symposium on Accelerator Physics},
number = {14},
venue = {Xichang, China},
note = {presented at the 14th Symp. Accel. Phys. (SAP'23) in Xichang, China, unpublished},
abstract = {{The reinforcement learning (RL) algorithm is utilized to control the low-energy beam transport (LEBT) and radiofrequency quadrupole (RFQ) in linear accelerators, with the aim of improving RFQ transmission efficiency, achieving high beam intensity, reducing debugging time, and improving operational efficiency. A neural network model is established as part of the Interaction environment to partially replace the Tracewin software for RL training proceess. The SAC algorithm is a reinforcement learning algorithm used to optimize control policies for continuous action spaces. By using the SAC algorithm and interacting with the neural network model, a policy was trained to control the LEBT solenoids, optimizing the RFQ transmission efficiency to above 95\% on the simulation software Tracewin. To test the generalization ability of the strategy, we applied it to a real accelerator and successfully validated its ability to optimize the RFQ transmission efficiency. The results demonstrate that RL policy trained in simulation-based environments can be applied on real accelerator control.}},
}