114 citations · 408 across the 9 of their papers we have counts for
3 papers · 1 filter
On the Design of LQR Kernels for Efficient Controller Learning
Alonso Marco, Philipp Hennig, Stefan Schaal +1
Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct cont…
Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
Andreas Doerr, Duy Nguyen-Tuong, Alonso Marco +2
PID control architectures are widely used in industrial applications. Despite their low number of open parameters, tuning multiple, coupled PID controllers can become tedious in pr…
Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization
Alonso Marco, Felix Berkenkamp, Philipp Hennig +4
In practice, the parameters of control policies are often tuned manually. This is time-consuming and frustrating. Reinforcement learning is a promising alternative that aims to aut…