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Paper Abstract and Keywords
Presentation 2010-03-09 14:10
Introducing a New Function to Save the Trouble of Parameter Tuning of Softmax Action-Selection
Kenji Ono, Kazunori Iwata, Akira Hayashi, Nobuo Suematsu (Hiroshima City Univ.) NC2009-106
Abstract (in Japanese) (See Japanese page) 
(in English) Markov decision processes are one of the most popular frameworks for reinforcement learning. The entropy of probability density functions of Markv decision processes is referred to as the stochastic complexity. The stochastic complexity is helpful for tuning the parameters of action-selection strategies to alleviate the exploration-exploitation dilemma which is well-known in reinforcement learning. In this paper, we improve the softmax action-selection which is a frequently used strategy, to save the trouble of its paramete-tuning by utilizing the stochastic complexity.
Keyword (in Japanese) (See Japanese page) 
(in English) Reinforcement Learning / Softmax Method / Markov Decision Process / / / / /  
Reference Info. IEICE Tech. Rep., vol. 109, no. 461, NC2009-106, pp. 107-112, March 2010.
Paper # NC2009-106 
Date of Issue 2010-03-02 (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 NC2009-106

Conference Information
Committee NC MBE  
Conference Date 2010-03-09 - 2010-03-11 
Place (in Japanese) (See Japanese page) 
Place (in English) Tamagawa University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To NC 
Conference Code 2010-03-NC-MBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Introducing a New Function to Save the Trouble of Parameter Tuning of Softmax Action-Selection 
Sub Title (in English)  
Keyword(1) Reinforcement Learning  
Keyword(2) Softmax Method  
Keyword(3) Markov Decision Process  
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1st Author's Name Kenji Ono  
1st Author's Affiliation Hiroshima City University (Hiroshima City Univ.)
2nd Author's Name Kazunori Iwata  
2nd Author's Affiliation Hiroshima City University (Hiroshima City Univ.)
3rd Author's Name Akira Hayashi  
3rd Author's Affiliation Hiroshima City University (Hiroshima City Univ.)
4th Author's Name Nobuo Suematsu  
4th Author's Affiliation Hiroshima City University (Hiroshima City Univ.)
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Speaker Author-1 
Date Time 2010-03-09 14:10:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2009-106 
Volume (vol) vol.109 
Number (no) no.461 
Page pp.107-112 
#Pages 6 
Date of Issue 2010-03-02 (NC) 

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