  Planning is a powerful approach to control problems with known environment
dynamics. In unknown environments the agent needs to learn a model of the
system dynamics to make planning applicable. This is particularly challenging
when the underlying states are only indirectly observable through images. We
propose to learn a deep latent Gaussian process dynamics (DLGPD) model that
learns low-dimensional system dynamics from environment interactions with
visual observations. The method infers latent state representations from
observations using neural networks and models the system dynamics in the
learned latent space with Gaussian processes. All parts of the model can be
trained jointly by optimizing a lower bound on the likelihood of transitions in
image space. We evaluate the proposed approach on the pendulum swing-up task
while using the learned dynamics model for planning in latent space in order to
solve the control problem. We also demonstrate that our method can quickly
adapt a trained agent to changes in the system dynamics from just a few
rollouts. We compare our approach to a state-of-the-art purely deep learning
based method and demonstrate the advantages of combining Gaussian processes
with deep learning for data efficiency and transfer learning.
