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Paper Abstract and Keywords
Presentation 2009-07-13 10:30
Composition of Feature Space and State Space Dynamics Models for Model-based Reinforcement Learning
Akihiko Yamaguchi, Jun Takamatsu, Tsukasa Ogasawara (NAIST) NLP2009-15 NC2009-8
Abstract (in Japanese) (See Japanese page) 
(in English) Learning a dynamics model and a reward model during reinforcement learning is a useful way, since the agent can also update its value function by using the models. In this paper, we propose a general dynamics model that is a composition of the feature space dynamics model and the state space dynamics model. This way enables to obtain a good generalization from a small number of samples because of the linearity of the state space dynamics, while it does not lose the accuracy. We demonstrate the simulation comparison of some dynamics models used together with a Dyna algorithm.
Keyword (in Japanese) (See Japanese page) 
(in English) reinforcement learning / model-based reinforcement learning / Dyna-style planning / prioritized sweeping / dynamics model / / /  
Reference Info. IEICE Tech. Rep., vol. 109, no. 125, NC2009-8, pp. 7-12, July 2009.
Paper # NC2009-8 
Date of Issue 2009-07-06 (NLP, NC) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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 NLP2009-15 NC2009-8

Conference Information
Committee NC NLP  
Conference Date 2009-07-13 - 2009-07-14 
Place (in Japanese) (See Japanese page) 
Place (in English) NAIST 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NC 
Conference Code 2009-07-NC-NLP 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Composition of Feature Space and State Space Dynamics Models for Model-based Reinforcement Learning 
Sub Title (in English)  
Keyword(1) reinforcement learning  
Keyword(2) model-based reinforcement learning  
Keyword(3) Dyna-style planning  
Keyword(4) prioritized sweeping  
Keyword(5) dynamics model  
1st Author's Name Akihiko Yamaguchi  
1st Author's Affiliation Nara Institute of Science and Technology (NAIST)
2nd Author's Name Jun Takamatsu  
2nd Author's Affiliation Nara Institute of Science and Technology (NAIST)
3rd Author's Name Tsukasa Ogasawara  
3rd Author's Affiliation Nara Institute of Science and Technology (NAIST)
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Speaker Author-1 
Date Time 2009-07-13 10:30:00 
Presentation Time 30 minutes 
Registration for NC 
Paper # NLP2009-15, NC2009-8 
Volume (vol) vol.109 
Number (no) no.124(NLP), no.125(NC) 
Page pp.7-12 
Date of Issue 2009-07-06 (NLP, NC) 

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