Junjie Ye, Zhengyin Du, Xuesong Yao, Weijian Lin, Yufei Xu, Zehui Chen, Zaiyuan Wang, Sining Zhu, Zhiheng Xi, Siyu Yuan, Tao Gui, Qi Zhang, Xuanjing Huang, Jiecao Chen. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Particle accelerators are composed of various components, and their properties are finely tuned to optimize certain particle beam qualities as they accelerate. In particular, particle colliders like the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Lab (BNL) are interested in maximizing luminosity, a measure of the collision rate primarily determined by the beam intensity (number of particles) and its beam size. However, finding and maintaining optimum settings is a time-consuming expert operator activity. This work proposes the use of the Recurrent Proximal Policy Optimization (RPPO), a reinforcement learning algorithm, to find parameters of quadrupole magnet strengths optimizing the beam qualities in the Booster to AGS (Alternating Gradient Synchrotron) section of the RHIC complex.