Annuth, RobertRobertAnnuthNußbaum, Finn OleFinn OleNußbaumBecker, ChristianChristianBecker2025-06-042025-06-042024-02-01Technische Universität Hamburg: (2025)https://hdl.handle.net/11420/45456Today's innovative systems rely heavily on complex electronic circuitry and control. This complexity increases development, testing, and manufacturing efforts. Consequently, building the first hardware prototype is often uneconomical, leading to a preference for simulation-based approaches. MATLAB, a commercial software, facilitates rapid prototyping, mathematical analysis, and simulation through its Simulink toolbox. Additionally, MATLAB offers a reinforcement learning (RL) toolbox for integration with Simulink. However, this RL toolbox is still maturing and lacks many recent RL innovations, partly because MATLAB is not alywas the first choice for machine learning in academia and industry. Despite this, many companies and researchers leverage Simulink's powerful simulation capabilities, often developing custom simulation libraries. To bridge the gap between MATLAB/Simulink and the more widely used Python ecosystem for RL, StableRLS was developed. StableRLS enables the use of existing Simulink models for RL in Python by converting these models and offering a Python user interface compatible with the Gymnasium library.enhttps://creativecommons.org/licenses/by-nc-sa/4.0/reinforcement learningFMUMATLAB SimulinkPythonComputer Science, Information and General Works::005: Computer Programming, Programs, Data and Security::005.1: ProgrammingComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial IntelligenceResearch Reporthttps://doi.org/10.15480/882.914310.15480/882.9143Research Report