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Paper Abstract and Keywords
Presentation 2016-11-16 15:00
Policy search based on sample clustering with Gaussian mixture model
Taiki Yano, Shinichi Maeda (Kyoto Univ.) IBISML2016-46
Abstract (in Japanese) (See Japanese page) 
(in English) EM-based Policy Hyper Parameter Exploration (EPHE)(Wang et al., 2016) is a method that kills two birds with one stone; it has both advantages of Policy Gradient Parameter Exploration (PGPE)(Sehnke et al., 2010) that can conduct stochastic exploration based on deterministic policy, and an EM-type algorithm that is free of meticulous tuning for stepsize selection. However, EPHE are often forced to introduce rather heuristic rules to create samples to be used for the policy update. We used mixture of Gaussian distribution to justify this heuristic approach. This modification enabled automatic informative selection of the sample set that is free of heuristic post-processing. The results of computer-simulated experiments revealed that EM-based policy search with Gaussian mixture model sometimes fails to find better strategy due to the poor exploration arisen from the shrink of the variance of the cluster during learning. To remedy this failure, we introduced the covariance matrix adaptation mechanism and found that our new algorithm achieved comparable or better performance.
Keyword (in Japanese) (See Japanese page) 
(in English) Reinforcement Learning / EM-based policy search / Gaussian mixture model / / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 300, IBISML2016-46, pp. 9-15, Nov. 2016.
Paper # IBISML2016-46 
Date of Issue 2016-11-09 (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)
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Conference Information
Committee IBISML  
Conference Date 2016-11-16 - 2016-11-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyoto Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Information-Based Induction Science Workshop (IBIS2016) 
Paper Information
Registration To IBISML 
Conference Code 2016-11-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Policy search based on sample clustering with Gaussian mixture model 
Sub Title (in English)  
Keyword(1) Reinforcement Learning  
Keyword(2) EM-based policy search  
Keyword(3) Gaussian mixture model  
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1st Author's Name Taiki Yano  
1st Author's Affiliation Kyoto University (Kyoto Univ.)
2nd Author's Name Shinichi Maeda  
2nd Author's Affiliation Kyoto University (Kyoto Univ.)
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Speaker Author-1 
Date Time 2016-11-16 15:00:00 
Presentation Time 180 minutes 
Registration for IBISML 
Paper # IBISML2016-46 
Volume (vol) vol.116 
Number (no) no.300 
Page pp.9-15 
#Pages 7 
Date of Issue 2016-11-09 (IBISML) 

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