Accelerator Tuning with Deep Reinforcement Learning

Particle accelerators require routine tuning during operation and when new isotope species are introduced. This is a complex process requiring many hours from experienced operators. The difficult control aspect of this problem is challenging for traditional approaches, but offers to be a promising candidate for reinforcement learning. We aim to develop an automated tuning procedure for the accelerators at TRIUMF, starting with the Off-Line Ion Source (OLIS) portion of the Isotope Separator and Accelerator (ISAC) facility. In this early stage of research, we show that the method of Recurrent Deep Deterministic Policy Gradients (RDPG) is successful in learning accelerator tuning procedures for a simple simulated environment representing the OLIS section.