Bounded regret in stochastic multi-armed bandits

author: Sébastien Bubeck, Department of Operations Research and Financial Engineering, Princeton University
published: Aug. 9, 2013,   recorded: June 2013,   views: 181
Categories

Slides

Related content

Report a problem or upload files

If 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.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography

Description

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(⋆).

See Also:

Download slides icon Download slides: colt2013_bubeck_regret_01.pdf (748.9 KB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: