rickstaa/stable-learning-control
A framework for training theoretically stable (and robust) Reinforcement Learning control algorithms.
This framework helps robotics engineers and control system designers develop robust and stable control policies for their systems using reinforcement learning. It takes environmental data, such as sensor readings or system states, and outputs control algorithms that guarantee stable and predictable behavior. This is ideal for those working on automated systems where safety and reliability are paramount.
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Use this if you need to train reinforcement learning agents for control systems and require a guarantee that your control policies will maintain stability and robustness in real-world applications.
Not ideal if you are working with environments that do not have a positive definite cost function or if your primary concern is not system stability.
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Language
Python
License
MIT
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Last pushed
Sep 04, 2024
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