Bounded regret in stochastic multi-armed bandits
published: Aug. 9, 2013, recorded: June 2013, views: 279
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.
We study the stochastic multi-armed bandit problem when one knows the value μ(⋆) of an optimal arm, as a well as a positive lower bound on the smallest positive gap Δ. We propose a new randomized policy that attains a regret uniformly bounded over time in this setting. We also prove several lower bounds, which show in particular that bounded regret is not possible if one only knows Δ, and bounded regret of order 1/Δ is not possible if one only knows μ(⋆).
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !