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Predictive Coding for Locally-Linear Control
High-dimensional observations and unknown dynamics are major challenges ...
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Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control
Many real-world sequential decision-making problems can be formulated as...
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DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Many reinforcement learning (RL) tasks provide the agent with high-dimen...
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Gaussian-Dirichlet Random Fields for Inference over High Dimensional Categorical Observations
We propose a generative model for the spatio-temporal distribution of hi...
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Heteroscedastic Uncertainty for Robust Generative Latent Dynamics
Learning or identifying dynamics from a sequence of high-dimensional obs...
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Learning the Linear Quadratic Regulator from Nonlinear Observations
We introduce a new problem setting for continuous control called the LQR...
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Controlled Gaussian Process Dynamical Models with Application to Robotic Cloth Manipulation
Over the last years, robotic cloth manipulation has gained relevance wit...
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Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations
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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