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Paper Abstract and Keywords
Presentation 2015-03-16 14:45
Reinforcement Learning based on Internal-Dynamics-Derived Exploration Using a Chaotic Neural Network
Katsunari Shibata, Yuta Sakashita (Oita Univ.) MBE2014-166 NC2014-117
Abstract (in Japanese) (See Japanese page) 
(in English) As a basic concept for emergence of intelligence through autonomous learning, exploration that is es- sential in reinforcement learning is considered as one aspect of the learner’s internal dynamics, which is expected to develop through learning towards purposive and dynamic higher functions such as ‘thinking’. A chaotic neural network is used to generate motions that include exploratory factors based on the chaotic dynamics without adding external random noises. Effective exploration is expected based on the dynamics such as “chaotic itinerancy”, which is also the key to learn dynamic higher functions more easily that needs both stable and transitive dynamics. Fur- thermore, a reinforcement learning method without external random noises by focusing on the correlation between the TD (Temporal Difference) error of the state value and the output change of each neuron is newly proposed. It was confirmed that an agent learned goal-directed behaviors in a simple task.
Keyword (in Japanese) (See Japanese page) 
(in English) chaotic neural network / exploration / reinforcement learning / dynamics / thinking / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 515, NC2014-117, pp. 277-282, March 2015.
Paper # NC2014-117 
Date of Issue 2015-03-09 (MBE, NC) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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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 MBE2014-166 NC2014-117

Conference Information
Committee NC MBE  
Conference Date 2015-03-16 - 2015-03-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Tamagawa University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) ME, etc 
Paper Information
Registration To NC 
Conference Code 2015-03-NC-MBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Reinforcement Learning based on Internal-Dynamics-Derived Exploration Using a Chaotic Neural Network 
Sub Title (in English)  
Keyword(1) chaotic neural network  
Keyword(2) exploration  
Keyword(3) reinforcement learning  
Keyword(4) dynamics  
Keyword(5) thinking  
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1st Author's Name Katsunari Shibata  
1st Author's Affiliation Oita University (Oita Univ.)
2nd Author's Name Yuta Sakashita  
2nd Author's Affiliation Oita University (Oita Univ.)
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Speaker Author-1 
Date Time 2015-03-16 14:45:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # MBE2014-166, NC2014-117 
Volume (vol) vol.114 
Number (no) no.514(MBE), no.515(NC) 
Page pp.277-282 
#Pages
Date of Issue 2015-03-09 (MBE, NC) 


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