Robust Reinforcement Learning Control of a Furuta Pendulum
The use of Reinforcement Learning (RL) to design controllers for safety critical systems is an important research area. On the one hand, RL can function in and adapt to complex and changing environments without requiring a model of the system. On the other hand, in such systems robustness is of high importance, as well as ways to guarantee and certify a level of robustness. This project investigat