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.
@article{arxiv.1907.04902,
title = {Interpretable Dynamics Models for Data-Efficient Reinforcement Learning},
author = {Markus Kaiser and Clemens Otte and Thomas Runkler and Carl Henrik Ek},
journal= {arXiv preprint arXiv:1907.04902},
year = {2019}
}
Comments
ESANN 2019 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 24-26 April 2019, i6doc.com publ., ISBN 978-287-587-065-0