Sprecher
Beschreibung
In recent research, reinforcement learning algorithms have been shown capable of solving complex control tasks, also showing potential for beam control, and in the optimization and automation of tasks in accelerator operation.
As part of the Helmholtz AI project "Machine Learning Toward Autonomous Accelerators" -- a collaboration between DESY and KIT -- reinforcement learning applications for the automatic control of an electron linear accelerators are investigated. In this contribution, we present first steps taken toward developing a framework for training reinforcement learning agents in simulation environments on specific tasks and applying these agents on an actual particle accelerator. In the future, this framework will allow for fast application of reinforcement learning to a multitude of optimization tasks on particle accelerators, eventually enabling autonomous operation to improve reproducibility and machine availability.
