KL Control Theory and Decision Making under Uncertainty
published: Jan. 19, 2010, recorded: December 2009, views: 7846
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !