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 RL terminology, this problem can be stated as follows: An agent is assigned the task to solve a given problem in multiple sequential steps. It does this by observing states from the environment and generating actions in response to these observations. The sequence of steps required to solve the problem is called an episode. The selection of actions in each step is defined by a policy, i.e., a mapping from observations to actions, which is learned through interactions with the environment such that the cumulative reward received for the actions is maximized. The agent can hence be considered as a learning algorithm that optimizes a policy for solving the respective task, which is encoded by its environment. In this work [1], 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, considering both the direct optimization approach and the incremental optimization approach [2]. These differ in how the agent utilizes its actions to modify the degrees of freedom (DOFs) of the geometry parameterization. With incremental shape optimization, only a single DOF is changed per step. In the implementation used in our RL-based shape optimization package releso, the DOFs is either incremented or decremented by a predefined amount. In contrast, in the direct optimization approach, all DOF are adapted at once in every step, and can, at least in our implementation, be set to any value within a specified interval. This results in the following behaviour: A fully trained agent, following the incremental method, iteratively approaches the optimal geometry in multiple small steps. Contrasting, an agent following the direct strategy tries to find the optimal geometry in a single step. We are able to show that RL-algorithms are very suitable for repeated optimization tasks of similar structure. Imagine for example the wings for different types of airplanes.