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Title:Analysis of Bayesian control law for adaptive control
Author(s):Chu, Yifeng
Contributor(s):Raginsky, Maxim
Subject(s):adaptive control
minimum relative entropy
Abstract:This thesis analyzes the Bayesian control law for adaptive control proposed by Ortega and Braun. The problem of concern is as follows: Assume the agent is put into an unknown environment that is sampled from certain distribution. If the agent is given priors of environment distribution, dynamics of each environment, and optimal controller for various environments, then under Bayesian control law, the agent's behavior would be as optimal as the true controller tailored to that unknown environment in an asymptotic sense. Furthermore, the results in the original paper are rewritten in a more understandable way and several ambiguities are clarifed.
Issue Date:2018-05
Date Available in IDEALS:2018-05-22

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