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Paper Abstract and Keywords
Presentation 2012-11-07 15:30
Stochastic policy gradient method for a stochastic policy using a Gaussian process regression
Yutaka Nakamura, Hiroshi Ishiguro (Osaka Univ.) IBISML2012-52
Abstract (in Japanese) (See Japanese page) 
(in English) Reinforcement learning (RL) methods using Gaussian process regression (GP) for approximating the value function have been studied [1]. Thanks to the use of Bayesian reasoning with GPs, the variance of the output can be calculated, but there is no direct benefit by using the variance of the value estimate. In this research, we propose a policy gradient method for a GP based stochastic policy, where the output variance is utilized as the confidence in the action selection. We apply our method to a control task of the swinging up a pendulum, and simulation results show a good controller can be obtained by our method.
Keyword (in Japanese) (See Japanese page) 
(in English) Reinforcement learning / Gaussian process regression / policy gradient method / adaptive control / / / /  
Reference Info. IEICE Tech. Rep., vol. 112, no. 279, IBISML2012-52, pp. 129-133, Nov. 2012.
Paper # IBISML2012-52 
Date of Issue 2012-10-31 (IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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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)
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Conference Information
Committee IBISML  
Conference Date 2012-11-07 - 2012-11-09 
Place (in Japanese) (See Japanese page) 
Place (in English) Bunkyo School Building, Tokyo Campus, Tsukuba Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) the 15th Information-Based Induction Sciences Workshop 
Paper Information
Registration To IBISML 
Conference Code 2012-11-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Stochastic policy gradient method for a stochastic policy using a Gaussian process regression 
Sub Title (in English)  
Keyword(1) Reinforcement learning  
Keyword(2) Gaussian process regression  
Keyword(3) policy gradient method  
Keyword(4) adaptive control  
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1st Author's Name Yutaka Nakamura  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Hiroshi Ishiguro  
2nd Author's Affiliation Osaka University (Osaka Univ.)
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Speaker Author-1 
Date Time 2012-11-07 15:30:00 
Presentation Time 150 minutes 
Registration for IBISML 
Paper # IBISML2012-52 
Volume (vol) vol.112 
Number (no) no.279 
Page pp.129-133 
#Pages
Date of Issue 2012-10-31 (IBISML) 


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