The superconducting linear accelerator is a highly flexiable facility for
modern scientific discoveries, necessitating weekly reconfiguration and tuning.
Accordingly, minimizing setup time proves essential in affording users with
ample experimental time. We propose a trend-based soft actor-critic(TBSAC) beam
control method with strong robustness, allowing the agents to be trained in a
simulated environment and applied to the real accelerator directly with
zero-shot. To validate the effectiveness of our method, two different typical
beam control tasks were performed on China Accelerator Facility for Superheavy
Elements (CAFe II) and a light particle injector(LPI) respectively. The orbit
correction tasks were performed in three cryomodules in CAFe II seperately, the
time required for tuning has been reduced to one-tenth of that needed by human
experts, and the RMS values of the corrected orbit were all less than 1mm. The
other transmission efficiency optimization task was conducted in the LPI, our
agent successfully optimized the transmission efficiency of radio-frequency
quadrupole(RFQ) to over $85\%$ within 2 minutes. The outcomes of these two
experiments offer substantiation that our proposed TBSAC approach can
efficiently and effectively accomplish beam commissioning tasks while upholding
the same standard as skilled human experts. As such, our method exhibits
potential for future applications in other accelerator commissioning fields.

该论文提出了一种基于趋势的软演员 - 评论家 (TBSAC) 束流控制方法，该方法在模拟环境下训练代理人，并直接应用于实际的超导线性加速器中，以实现优化束流效率的任务。通过两种不同的典型任务的实验验证，该方法显示出与熟练的人类专家相同的标准，从而表明其在未来应用于其他加速器调试领域是有潜力的。