## AbstractStochastic optimal control theory concerns the problem of how to act optimally when reward is only obtained at a later time. The stochastic optimal control problem is central to modeling intelligent behaviour in animals or machines. Examples are control of multi-joint robot arms, navigation of vehicles, coordination of multi-agent systems. In addition, control theory plays an important role in financial applications. Currently, the dominant approach to the above problems within the Machine learning community is Reinforcement Learning or (Partially Observable) Markov Decision Processes and often uses discounted reward. One can view these approaches as special cases of stochastic control theory. The tutorial is introductory and aimed at the 'average' machine learning researcher. No background in control theory and/or reinforcement learning is assumed. A basic understanding of Bayesian networks and statistical inference is assumed.
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