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Regret Minimization in Partially Observable Linear Quadratic Control

by   Sahin Lale, et al.

We study the problem of regret minimization in partially observable linear quadratic control systems when the model dynamics are unknown a priori. We propose ExpCommit, an explore-then-commit algorithm that learns the model Markov parameters and then follows the principle of optimism in the face of uncertainty to design a controller. We propose a novel way to decompose the regret and provide an end-to-end sublinear regret upper bound for partially observable linear quadratic control. Finally, we provide stability guarantees and establish a regret upper bound of Õ(T^2/3) for ExpCommit, where T is the time horizon of the problem.


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