JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
TY - UNPB AU - Su, C.G. AU - Wang, Z.J. ED - Schaa, Volker RW ED - Huang, Wenhui ED - Yan, Xueqing ED - Tang, Chuanxiang ED - Li, Lu TI - Optimisation of RFQ Transmission Efficiency Based on Reinforcement Learning Control Policy J2 - presented at SAP2023, Xichang, China, 10-12 July 2023 CY - Xichang, China T2 - Symposium on Accelerator Physics T3 - 14 LA - english AB - 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. ER -