An Asymptotically Optimal Policy for Finite Support Models in the Multiarmed Bandit Problem

Details An Asymptotically Optimal Policy for Finite Support Models in the Multiarmed Bandit Problem An Asymptotically Optimal Policy for Finite Support Models in the Multiarmed Bandit Problem

2009-05-17

arxiv.org/abs/0905.2776v3

Machine Learning 85 (2011) 361--391

Mathematics

Statistics Theory

Scientific paper

We propose minimum empirical divergence (MED) policy for the multiarmed bandit problem. We prove asymptotic optimality of the proposed policy for the case of finite support models. In our setting, Burnetas and Katehakis has already proposed an asymptotically optimal policy. For choosing an arm our policy uses a criterion which is dual to the quantity used in Burnetas and Katehakis. Our criterion is easily computed by a convex optimization technique and has an advantage in practical implementation. We confirm by simulations that MED policy demonstrates good performance in finite time in comparison to other currently popular policies.

Affiliated with

Also associated with

No associations

Rating

An Asymptotically Optimal Policy for Finite Support Models in the Multiarmed Bandit Problem does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with An Asymptotically Optimal Policy for Finite Support Models in the Multiarmed Bandit Problem, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and An Asymptotically Optimal Policy for Finite Support Models in the Multiarmed Bandit Problem will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-608954