Interpretable Dynamics Models for Data-Efficient Reinforcement Learning

Markus Kaiser; Clemens Otte; Thomas Runkler; Carl Henrik Ek

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arXiv · 1907.04902

Interpretable Dynamics Models for Data-Efficient Reinforcement Learning

Markus Kaiser·Clemens Otte·Thomas Runkler·Carl Henrik Ek

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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.

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Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek. 2019-07-10. Interpretable Dynamics Models for Data-Efficient Reinforcement Learning. https://arxiv.org/abs/1907.04902

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