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We propose a novel reformulation of the stochastic optimal control problem asan approximate inference problem, demonstrating, that such a interpretationleads to new practical methods for the original problem. In particular wecharacterise a novel class of iterative solutions to the stochastic optimalcontrol problem based on a natural relaxation of the exact dual formulation.These theoretical insights are applied to the Reinforcement Learning problemwhere they lead to new model free, off policy methods for discrete andcontinuous problems.

Approximate Inference and Stochastic Optimal Control