Day 3 and 4 of NIPS main meeting (part 1, part 2). More amazing deep learning results.

**Embed to control: a locally linear latent dynamics model for control from raw images**

Manuel Watter, Jost Springenberg, Joschka Boedecker, Martin Riedmiller

To implement optimal control in the latent state space, they used iterative Linear-Quadratic-Gaussian control applied directly to video. A gaussian latent state space was decoded from images through a deep variational latent variable model. One step prediction of latent dynamics was modeled to be locally linear where the dynamics matrices were parameterized by a neural network that depends on the current state. A variant of a variational cost that minimizes instantaneous reconstruction, and also KL divergence between predicted latent and the reconstructed latent. Deconvolution network was used, and as can be seen in the [video online], the generated images are a bit blurry, but iLQG control works well.

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