Fraunhofer-IIS/fmugym
Interface to connect Reinforcement Learning libraries to Functional Mock-up Units for control under uncertainties
This tool helps engineers and researchers design and test control systems for physical models. You provide a functional mock-up unit (FMU), which is a standardized simulation model of a component or system, and the tool connects it to reinforcement learning algorithms. The output is an optimized control strategy that can handle real-world uncertainties, making your systems more robust. It's ideal for control engineers, systems integrators, and researchers working with complex cyber-physical systems.
No commits in the last 6 months. Available on PyPI.
Use this if you need to develop robust, intelligent control systems for simulated physical models (FMUs) that can adapt to uncertainties.
Not ideal if you are looking for a tool to simulate FMUs without integrating advanced reinforcement learning for control optimization.
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29
Forks
7
Language
Python
License
MIT
Category
Last pushed
Sep 03, 2025
Commits (30d)
0
Dependencies
4
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