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High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks.
A new approach to visual servoing in robotics
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When is “nearest neighbor” meaningful?
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Iterative linear quadratic regulator design for nonlinear biological movement systems
Li, W. and Todorov, E · 2004
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Probabilistic graphical models: principles and techniques
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Lectures on stochastic programming: modeling and theory
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Estimating divergence functionals and the likelihood ratio by convex risk minimization
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Auto-encoding variational bayes
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Playing Atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
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Adam: A method for stochastic optimization
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Stochastic backpropagation and approximate inference in deep generative models
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Importance weighted autoencoders
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Variational inference with normalizing flows
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Learning structured output representation using deep conditional generative models
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Embed to control: A locally linear latent dynamics model for control from raw images
Watter, M., Springenberg, J., Boedecker, J., and Riedmiller, M · 2015
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Near-optimal representation learning for hierarchical reinforcement learning
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Representation learning with contrastive predictive coding
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Anand, A., Racah, E., Ozair, S., Bengio, Y., Côté, M.-A., and Hjelm, R. D · 2019
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Model-based reinforcement learning for atari
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On variational bounds of mutual information
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Solar: Deep structured latent representations for model-based reinforcement learning
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Prediction, consistency, curvature: Representation learning for locally-linear control
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