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Paper Abstract and Keywords
Presentation 2017-03-13 10:25
Estimation of the change of agent's behavior strategy using state-action history
Shihori Uchida, Shigeyuki Oba, Shin Ishii (Kyoto Univ.) NC2016-65
Abstract (in Japanese) (See Japanese page) 
(in English) Reinforcement learning (RL) is a model of learning process of animals and intelligent agents to obtain the optimal behavioral policy based on interactions with unknown environments.
Inverse reinforcement learning (IRL) is its opposite, in which the characteristics like reward function of the RL agent are estimated based on the history of the agent's behaviors.
In the uncertain environment, the RL agent needs to balance between the currently good behavioral policy (exploitation) and an exploration policy for resolving the uncertainty of the environment (exploration).
The existing IRL methods were not appropriate to identify the RL agent's characteristics when it is taking a mixed strategy performing exploitation and exploration depending on its situation.
In this study, we proposed a new IRL method that enabled dissociation of different behavioral policies but with the common reward function.
Our computer simulation showed that, our method successfully identifies not only the timing of the policy change, but also the other RL parameters like behavioral randomness and the common reward function, only from the agent's behaviors.
Keyword (in Japanese) (See Japanese page) 
(in English) Reinforcement learning / Inverse reinforcement learning / Behavior strategy / / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 521, NC2016-65, pp. 7-12, March 2017.
Paper # NC2016-65 
Date of Issue 2017-03-06 (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 NC2016-65

Conference Information
Committee MBE NC  
Conference Date 2017-03-13 - 2017-03-14 
Place (in Japanese) (See Japanese page) 
Place (in English) Kikai-Shinko-Kaikan Bldg. 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NC 
Conference Code 2017-03-MBE-NC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Estimation of the change of agent's behavior strategy using state-action history 
Sub Title (in English)  
Keyword(1) Reinforcement learning  
Keyword(2) Inverse reinforcement learning  
Keyword(3) Behavior strategy  
1st Author's Name Shihori Uchida  
1st Author's Affiliation Kyoto University (Kyoto Univ.)
2nd Author's Name Shigeyuki Oba  
2nd Author's Affiliation Kyoto University (Kyoto Univ.)
3rd Author's Name Shin Ishii  
3rd Author's Affiliation Kyoto University (Kyoto Univ.)
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Date Time 2017-03-13 10:25:00 
Presentation Time 25 
Registration for NC 
Paper # IEICE-NC2016-65 
Volume (vol) IEICE-116 
Number (no) no.521 
Page pp.7-12 
#Pages IEICE-6 
Date of Issue IEICE-NC-2017-03-06 

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