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Optimal control under uncertainty is a prevailing challenge for many reasons.One of the critical difficulties lies in producing tractable solutions for theunderlying stochastic optimization problem. We show how advanced approximateinference techniques can be used to handle the statistical approximationsprincipled and practically by framing the control problem as a problem of inputestimation. Analyzing the Gaussian setting, we present an inference-basedsolver that is effective in stochastic and deterministic settings and was foundto be superior to popular baselines on nonlinear simulated tasks. We drawconnections that relate this inference formulation to previous approaches forstochastic optimal control and outline several advantages that this inferenceview brings due to its statistical nature.

Efficient Stochastic Optimal Control through Approximate Bayesian Input Inference