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Paper Abstract and Keywords
Presentation 2008-03-14 13:20
Active sampling based on Gaussian Process for reinforcement learning
Kazuhiro Takeda, Takeshi Mori (ＮＡＩＳＴ), Shin Ishii (Kyoto Univ.) NC2007-192
Abstract (in Japanese) (See Japanese page) 
(in English) In reinforcement learning (RL), many samples are necessary in
every policy improvement, which requires the robot actually to act many
times and hence may make the robot be broken down. One cause of the necessity of
many samples in the RL is that the agent must passively produce samples
according to it's current policy. Therefore, efficient sampling methods
such as active sampling are desired. In this report, we propose a novel
RL method with active sampling based on Gaussian process, which reduces
the number of samples necessary for policy convergence. Computer experiments show that the policy gradient
could be estimated efficiently with fewer sampling times.
Keyword (in Japanese) (See Japanese page) 
(in English) Reinforcement learning / Active sampling / Gaussian process / Policy gradient / Bayesian inference / / /  
Reference Info. IEICE Tech. Rep., vol. 107, no. 542, NC2007-192, pp. 473-478, March 2008.
Paper # NC2007-192 
Date of Issue 2008-03-05 (NC) 
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)
Download PDF NC2007-192

Conference Information
Committee NC MBE  
Conference Date 2008-03-12 - 2008-03-14 
Place (in Japanese) (See Japanese page) 
Place (in English) Tamagawa Univ 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NC 
Conference Code 2008-03-NC-MBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Active sampling based on Gaussian Process for reinforcement learning 
Sub Title (in English)  
Keyword(1) Reinforcement learning  
Keyword(2) Active sampling  
Keyword(3) Gaussian process  
Keyword(4) Policy gradient  
Keyword(5) Bayesian inference  
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1st Author's Name Kazuhiro Takeda  
1st Author's Affiliation Nara Institute of Science and Technology (ＮＡＩＳＴ)
2nd Author's Name Takeshi Mori  
2nd Author's Affiliation Nara Institute of Science and Technology (ＮＡＩＳＴ)
3rd Author's Name Shin Ishii  
3rd Author's Affiliation Kyoto University (Kyoto Univ.)
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Speaker Author-1 
Date Time 2008-03-14 13:20:00 
Presentation Time 20 minutes 
Registration for NC 
Paper # NC2007-192 
Volume (vol) vol.107 
Number (no) no.542 
Page pp.473-478 
#Pages 6 
Date of Issue 2008-03-05 (NC) 

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