Your Search Results

Use this resource - and many more! - in your textbook!

AcademicPub holds over eight million pieces of educational content for you to mix-and-match your way.

Experience the freedom of customizing your course pack with AcademicPub!
Not an educator but still interested in using this content? No problem! Visit our provider's page to contact the publisher and get permission directly.

Adaptive learning of uncontrolled restless bandits with logarithmic regret

By: Mingyan Liu; Tekin, C.;

2011 / IEEE / 978-1-4577-1818-2

Description

This item was taken from the IEEE Conference ' Adaptive learning of uncontrolled restless bandits with logarithmic regret ' In this paper we consider the problem of learning the optimal policy for the uncontrolled restless bandit problem. In this problem only the state of the selected arm can be observed, the state transitions are independent of control and the transition law is unknown. We propose a learning algorithm which gives logarithmic regret uniformly over time with respect to the optimal finite horizon policy with known transition law under some assumptions on the transition probabilities of the arms and the structure of the optimal stationary policy for the infinite horizon average reward problem.