  We introduce a method for learning the dynamics of complex nonlinear systems
based on deep generative models over temporal segments of states and actions.
Unlike dynamics models that operate over individual discrete timesteps, we
learn the distribution over future state trajectories conditioned on past
state, past action, and planned future action trajectories, as well as a latent
prior over action trajectories. Our approach is based on convolutional
autoregressive models and variational autoencoders. It makes stable and
accurate predictions over long horizons for complex, stochastic systems,
effectively expressing uncertainty and modeling the effects of collisions,
sensory noise, and action delays. The learned dynamics model and action prior
can be used for end-to-end, fully differentiable trajectory optimization and
model-based policy optimization, which we use to evaluate the performance and
sample-efficiency of our method.
