Reinforcement learning on continuous and deterministic systems

Hans Harder
Tristan Kenneweg, Thorben Markmann, Michiel Straat
Research Theme
R2 Prosilience & Robustness
Reinforcement Learning

My research focuses on data-efficient methods in machine learning and reinforcement-learning, in particular for continuous and deterministic dynamical systems. Under investigation are different ways to achieve data sparsity: For example, by exploiting symmetries (such as translation invariance); learning surrogate models (in order to reduce the number of interactions with the real system); or by exploiting the deterministic nature of such systems.

I’m currently looking at variance bounds in the context of reinforcement learning for “near deterministic” dynamical systems. I’m also investigating optimal (i.e. variance minimizing) step-sizes in policy evaluation procedures