no code implementations • NeurIPS 2018 • Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek
We apply the method to the real-world problem of finding common structure in the sensor data of wind turbines introduced by the underlying latent and turbulent wind field.
no code implementations • 16 Oct 2018 • Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek
The data association problem is concerned with separating data coming from different generating processes, for example when data come from different data sources, contain significant noise, or exhibit multimodality.
no code implementations • 10 Jul 2019 • Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek
In this paper, we present a Bayesian view on model-based reinforcement learning.
Model-based Reinforcement Learning reinforcement-learning +2
1 code implementation • 28 Jan 2022 • Philipp Scholl, Felix Dietrich, Clemens Otte, Steffen Udluft
Safe Policy Improvement (SPI) aims at provable guarantees that a learned policy is at least approximately as good as a given baseline policy.
1 code implementation • 1 Aug 2022 • Philipp Scholl, Felix Dietrich, Clemens Otte, Steffen Udluft
Based on this finding, we develop adaptations, the Adv-Soft-SPIBB algorithms, and show that they are provably safe.