Martinez Marin, J.L. Blomberg, B.R. Bunnell, K.J. Dunn, G.M. Letcher, E. Mustapha, B. Stanton, D. JACoW Publishing Geneva, Switzerland 2673-5547 978-3-95450-240-0 10.18429/JACoW-HIAT2022-TH1I2 English 130-135 controls simulation quadrupole rfq experiment Contribution to a conference proceedings 2022 2022-08 https://doi.org/10.18429/JACoW-HIAT2022-TH1I2 https://jacow.org/hiat2022/papers/th1i2.pdf The use of artificial intelligence can significantly reduce the time needed to tune an accelerator system such as the Argonne Tandem Linear Accelerator System (ATLAS) where a new beam is tuned once or twice a week. After establishing automatic data collection procedures and having analysed the data, machine learning models were developed and tested to tune subsections of the linac. Models based on Reinforcement Learning (RL) and Bayesian Optimization (BO) were developed, their respec-tive results are discussed and compared. RL and BO are well known AI techniques, often used for control systems. The results were obtained for a subsection of ATLAS that contains complex elements such as the radio-frequency quadrupole (RFQ). The models will be later generalized to the whole ATLAS linac, and similar models can be devel-oped for any accelerator with a modern control system.