Interpretable Dynamics Models for Data-Efficient Reinforcement Learning

2019-07-10 · Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek

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.

📄 PDF Abstract BibTeX arXiv:1907.04902

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Model-based Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Variational Inference

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