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

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