Paper Abstract and Keywords |
Presentation |
2012-06-20 11:00
On a Non-asymptotic Analysis Using Large Deviation Principles in the Multiarmed Bandit Problem Junya Honda, Akimichi Takemura (Univ. of Tokyo) IBISML2012-10 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In reinforcement learning a tradeoff between exploration and exploitation is considered.
Multiarmed bandit problems formulate this dilemma as a model of a gambler playing a slot machine with multiple arms.
In this paper we first consider a stochastic bandit such that each arm has a reward distribution supported in a known interval, e.g., [0,1].
Recently a policy, DMED, is proposed and proved to achieve the asymptotic bound for the model.
However, the derived regret bound is described in an asymptotic form and the performance in finite time has been unknown.
We inspect this policy and derive a finite-time regret bound by refining large deviation probabilities to a simple finite form.
Further, this observation reveals that the assumption on the lower-boundedness of the support is not essential and can be replaced with a weaker one, the existence of the moment generating function. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
multiarmed bandit problem / reinforcement learning / large deviation principle / moment generating function / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 112, no. 83, IBISML2012-10, pp. 65-72, June 2012. |
Paper # |
IBISML2012-10 |
Date of Issue |
2012-06-12 (IBISML) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
Notes on Review |
This article is a technical report without peer review, and its polished version will be published elsewhere. |
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IBISML2012-10 |
Conference Information |
Committee |
IBISML |
Conference Date |
2012-06-19 - 2012-06-20 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Campus plaza Kyoto |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
General topics on machine learning and its application |
Paper Information |
Registration To |
IBISML |
Conference Code |
2012-06-IBISML |
Language |
English (Japanese title is available) |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
On a Non-asymptotic Analysis Using Large Deviation Principles in the Multiarmed Bandit Problem |
Sub Title (in English) |
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Keyword(1) |
multiarmed bandit problem |
Keyword(2) |
reinforcement learning |
Keyword(3) |
large deviation principle |
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moment generating function |
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1st Author's Name |
Junya Honda |
1st Author's Affiliation |
The University of Tokyo (Univ. of Tokyo) |
2nd Author's Name |
Akimichi Takemura |
2nd Author's Affiliation |
The University of Tokyo (Univ. of Tokyo) |
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Speaker |
Author-1 |
Date Time |
2012-06-20 11:00:00 |
Presentation Time |
30 minutes |
Registration for |
IBISML |
Paper # |
IBISML2012-10 |
Volume (vol) |
vol.112 |
Number (no) |
no.83 |
Page |
pp.65-72 |
#Pages |
8 |
Date of Issue |
2012-06-12 (IBISML) |
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