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arXiv · 1911.00756

Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations

Neha Das·Maximilian Karl·Philip Becker-Ehmck·Patrick van der Smagt
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Abstract

Learning a model of dynamics from high-dimensional images can be a core ingredient for success in many applications across different domains, especially in sequential decision making. However, currently prevailing methods based on latent-variable models are limited to working with low resolution images only. In this work, we show that some of the issues with using high-dimensional observations arise from the discrepancy between the dimensionality of the latent and observable space, and propose solutions to overcome them.

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Neha Das, Maximilian Karl, Philip Becker-Ehmck, Patrick van der Smagt. 2019-11-02. Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations. https://arxiv.org/abs/1911.00756

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