A B S T R AC T Industrial computed tomography requires extensive expert knowledge. This paper presents a concept to automate the parameterization of measurements by reinforcement learning. Within a simulation, agents are supposed to learn measurement strategies such as the design of time-mini-mal measurements. Even though current virtual CTs allow realistic measurements, their runtime currently still prevents their use as an efficient training environment.