72 citations · 322 across the 23 of their papers we have counts for
5 papers · 1 filter
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning
Sebastian Weichwald, Søren Wengel Mogensen, Tabitha Edith Lee +6
Questions in causality, control, and reinforcement learning go beyond the classical machine learning task of prediction under i.i.d. observations. Instead, these fields consider th…
GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems
Bhavya Sukhija, Matteo Turchetta, David Lindner +3
Learning optimal control policies directly on physical systems is challenging since even a single failure can lead to costly hardware damage. Most existing model-free learning meth…
Identifying Causal Structure in Dynamical Systems
Dominik Baumann, Friedrich Solowjow, Karl H. Johansson +1
Mathematical models are fundamental building blocks in the design of dynamical control systems. As control systems are becoming increasingly complex and networked, approaches for o…
Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures
Alonso Marco, Alexander von Rohr, Dominik Baumann +2
When learning to ride a bike, a child falls down a number of times before achieving the first success. As falling down usually has only mild consequences, it can be seen as a toler…
Classified Regression for Bayesian Optimization: Robot Learning with Unknown Penalties
Alonso Marco, Dominik Baumann, Philipp Hennig +1
Learning robot controllers by minimizing a black-box objective cost using Bayesian optimization (BO) can be time-consuming and challenging. It is very often the case that some roll…