Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems

Such learning problems are formulated as latent or generative model learning assuming that observations were emerged from the low-dimensional latent states, which includes an intractable posterior inference of latent states for given input data.