Due to the ever-increasing amount of available data, the technological advances for its processing, and in the context of Industry 4.0, research and industry are focusing on creating increasingly detailed digital twins. These aspire to transfer all the capabilities and attributes of their physical counterparts into the digital world. Digital Twins enable simulations of real production and manufacturing processes to be carried out, new approaches to be tested and, in turn, innovative conclusions to be drawn without having to take the risks that costly machines entail. In parallel, approaches from the fields of machine learning, artificial intelligence and reinforcement learning are finding continuously more applications in the manufacturing and robotics domains. Especially in the latter, OpenAI researchers achieved a breakthrough, namely the construction of a neural network that was trained to solve a Rubik’s cube by a robotic hand using reinforcement learning. For the implementation, appropriate simulation environments were used, in which the agent responsible for controlling the robotic arm could train and learn for an enormous amount of times in the simulation. However, the highly heterogeneous environment in the production environment makes it difficult to integrate reinforcement learning methodologies and create the necessary simulations. Researchers must spend a severe amount of their time implementing interfaces for specific machine-tool related components rather than working on the actual problem. It is exactly this issue that this thesis addresses. The goal of this master thesis is the empirical development of a methodology for the automatic generation of reinforcement learning simulation environments for machine-tools. Within the scope of the thesis, different requirements shall be collected by interviewing domain experts as potential end users, generalized and transferred into a software concept. Furthermore, the possibility of deducing and abstracting state and action spaces for reinforcement learning environments and agents from a given machine-tool description is to be investigated within the scope of this work. In addition, the concept to be developed should be machine-tool and platform-agnostic, as well as modular, so that subsequent research can be conducted upon the presented concept.