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KL control theory and decision making under uncertainty

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If you have a question about this talk, please contact Zoubin Ghahramani.

KL control theory consists of a class of control problems for which the control computation can be solved as a graphical model inference problem. In this talk, we show how to apply this theory in the context of a delayed choice task and for collaborating agents. We first introduce the KL control framework. Then we show that in a delayed reward task when the future is uncertain it is optimal to delay the timing of your decision. We show preliminary results on human subjects that confirm this prediction. Subsequently, we discuss two player games, such as the stag-hunt game, where collaboration can improve or worsten as a result of recursive reasoning about the opponents actions. The Nash equilibria appear as local minima of the optimal cost to go, but may disappear when monetary gain decreases. This behaviour is in agreement with experimental findings in humans.

This talk is part of the Machine Learning @ CUED series.

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