  Optimal control under uncertainty is a prevailing challenge for many reasons.
One of the critical difficulties lies in producing tractable solutions for the
underlying stochastic optimization problem. We show how advanced approximate
inference techniques can be used to handle the statistical approximations
principled and practically by framing the control problem as a problem of input
estimation. Analyzing the Gaussian setting, we present an inference-based
solver that is effective in stochastic and deterministic settings and was found
to be superior to popular baselines on nonlinear simulated tasks. We draw
connections that relate this inference formulation to previous approaches for
stochastic optimal control and outline several advantages that this inference
view brings due to its statistical nature.
