Dynamics with Inverse Autoregressive Flows

Through this work, we propose the incorporation of Inverse Autoregressive Flows for determining the state space (latents) in a dynamical system model. This reduces the number of samples that need to be obtained in order to approximate the posterior distribution (and thus the underlying states/latents for a set of observations and controls) from one per time step to one per sequence of observations. Our experiments with pendulum-v01, an environment from openai gym confirmed that the accuracy with which the observations are generated are close to the state of the art for sequence models.