Stability in Reinforcement Learning
The aim of the research is to develop control algorithms that balance optimal performance with stability guarantees for stochastic dynamical systems modelled as Markov Decision Processes. Extend these methods to the Reinforcement Learning setting to improve the robustness, reliability, and safety of data-driven control design.
Back to Current Students