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Adaptive Coding of Actions and Observations

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Ortega,  P
Research Group Sensorimotor Learning and Decision-Making, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Ortega, P. (2013). Adaptive Coding of Actions and Observations. Talk presented at GRASP Laboratory, University of Pennsylvania: Spring 2013 GRASP Seminar. Philadelphia, PA, USA.


Cite as: https://hdl.handle.net/21.11116/0000-0001-4F68-3
Abstract
The application of expected utility theory to construct adaptive agents is both computationally intractable and statistically questionable. To overcome these difficulties, agents need the ability to delay the choice of the optimal policy to a later stage when they have learned more about the environment. How should agents do this optimally? An information-theoretic answer to this question is given by the Bayesian control rule - the solution to the adaptive coding problem when there are not only observations but also actions. We review the central ideas behind the Bayesian control rule.