In this thesis a deep reinforcement learnng agent is trained in an environment made with the Unity game engine. The new ML Agents APl allows communication with a Python backend, which enables research with familiar tools. The simulation features a huge variety of different adjustable parameters and can be run in parallel. The agent successfully learned to follow the track and is robust to various environmental changes. Furthermore, the future deployment of Neural Networks on the AutoMiny car is prepared. The AutoMiny cars are developed by the FU Berlin (Institute of Computer Science). The model car features the NvidiaJetson Nano, which allows the hardware accelerated inference via Deep Neural Networks.