Reinforcement Learning (RL) is a subfield of Machine Learning that aims to mimic the way how humans learn to accomplish a new task. This process is mostly guided by experience, i.e., by trying out how to act in a specific situation in order to achieve a certain goal. Once enough experience has been gathered, it can be utilized to make more educated decisions to accomplish this task efficiently. In this work, we apply RL as a shape optimization driver. Based on a variety of basic examples lent from production engineering, we were able to show that an RL agent can indeed learn to optimize a geometry. In particular, we compare a variety of algorithms from the two general categories direct optimization approach and incremental optimization approach.