I'll first give a general introduction on formalizing planning (more precisely Stochastic Optimal Control problems) in terms of probabilistic inference (more precisely: KL divergence minimization), providing a unifying perspective of previous approaches. I'll then focus on novel algorithms that can be derived from this formulation, including an efficient model-free RL algorithm that I find very interesting.
