journaltitle = {Siemens AG, CT IC 4, Technical Report},
title = {The Wet Game of Chicken}
}
@article{kaiser_interpretable_2019,
abstract = {In this paper, we present a Bayesian view on model-based reinforcement learning. We use expert knowledge to impose structure on the transition model and present an efficient learning scheme based on variational inference. This scheme is applied to a heteroskedastic and bimodal benchmark problem on which we compare our results to NFQ and show how our approach yields human-interpretable insight about the underlying dynamics while also increasing data-efficiency.},
author = {Kaiser, Markus and Otte, Clemens and Runkler, Thomas and Ek, Carl Henrik},
date = {2019},
journaltitle = {Computational Intelligence and Machine Learning},
langid = {english},
pages = {6},
title = {Interpretable {{Dynamics Models}} for {{Data}}-{{Efficient Reinforcement Learning}}},