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There are over 15 distinct communities that work in the general area ofsequential decisions and information, often referred to as decisions underuncertainty or stochastic optimization. We focus on two of the most importantfields: stochastic optimal control, with its roots in deterministic optimalcontrol, and reinforcement learning, with its roots in Markov decisionprocesses. Building on prior work, we describe a unified framework that coversall 15 different communities, and note the strong parallels with the modelingframework of stochastic optimal control. By contrast, we make the case that themodeling framework of reinforcement learning, inherited from discrete Markovdecision processes, is quite limited. Our framework (and that of stochasticcontrol) is based on the core problem of optimizing over policies. We describefour classes of policies that we claim are universal, and show that each ofthese two fields have, in their own way, evolved to include examples of each ofthese four classes.

From Reinforcement Learning to Optimal Control: A unified framework for sequential decisions