79 citations · 370 across the 33 of their papers we have counts for
5 papers · 1 filter
Sensitivity Analysis for Predictive Uncertainty in Bayesian Neural Networks
Stefan Depeweg, José Miguel Hernández-Lobato, Steffen Udluft +1
We derive a novel sensitivity analysis of input variables for predictive epistemic and aleatoric uncertainty. We use Bayesian neural networks with latent variables as a model class…
Interpretable Policies for Reinforcement Learning by Genetic Programming
Daniel Hein, Steffen Udluft, Thomas A. Runkler
The search for interpretable reinforcement learning policies is of high academic and industrial interest. Especially for industrial systems, domain experts are more likely to deplo…
Bayesian Alignments of Warped Multi-Output Gaussian Processes
Markus Kaiser, Clemens Otte, Thomas Runkler +1
We propose a novel Bayesian approach to modelling nonlinear alignments of time series based on latent shared information. We apply the method to the real-world problem of finding c…
A Benchmark Environment Motivated by Industrial Control Problems
Daniel Hein, Stefan Depeweg, Michel Tokic +4
In the research area of reinforcement learning (RL), frequently novel and promising methods are developed and introduced to the RL community. However, although many researchers are…
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences
Daniel Hein, Steffen Udluft, Michel Tokic +3
The Particle Swarm Optimization Policy (PSO-P) has been recently introduced and proven to produce remarkable results on interacting with academic reinforcement learning benchmarks…