Using inference to solve reinforcement learning and control problems
By Matt Hoffman
A number of techniques have been proposed recently to transform the problems of reinforcement learning and control into problems of maximum-likelihood and inference. This reformulation allows us to use a greater set of tools (such as EM, MCMC, etc.) to solve these problems and should provide additional insight into the behavior of some of the more classical algorithms. In this talk I will present an overview of this methodology and detail more specifically some of the work I've done in this area.
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