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Paper Abstract and Keywords
Presentation 2023-03-17 16:25
Investigation on improving diversity of options in option-critic reinforcement learning
Aya Nakagawa, Hidehiro Nakano (Tokyo City Univ.) MSS2022-109 NLP2022-154
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, reinforcement learning has been attracting attention in various fields such as automatic control and game AI. One of the methods of hierarchical reinforcement learning is Option-Critic (OC), which improves search performance by partitioning the target task, thereby enhancing learning performance in large-scale environments. OC learns by probabilistically generating Options corresponding to segmented subtasks, but it has a problem that similar Options are generated due to the Option degeneracy problem, which reduces the diversity of Options. In this study, we propose a method to improve the diversity of Options by generating Options with different characteristics from those generated by conventional methods using on-policy updating formulas, and combining them with conventional methods.
Keyword (in Japanese) (See Japanese page) 
(in English) Reinforcement Learning / Hierarchical Reinforcement Learning / Option-Critic / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 436, NLP2022-154, pp. 225-230, March 2023.
Paper # NLP2022-154 
Date of Issue 2023-03-08 (MSS, NLP) 
ISSN 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 MSS2022-109 NLP2022-154

Conference Information
Committee NLP MSS  
Conference Date 2023-03-15 - 2023-03-17 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NLP 
Conference Code 2023-03-NLP-MSS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Investigation on improving diversity of options in option-critic reinforcement learning 
Sub Title (in English)  
Keyword(1) Reinforcement Learning  
Keyword(2) Hierarchical Reinforcement Learning  
Keyword(3) Option-Critic  
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1st Author's Name Aya Nakagawa  
1st Author's Affiliation Tokyo City University (Tokyo City Univ.)
2nd Author's Name Hidehiro Nakano  
2nd Author's Affiliation Tokyo City University (Tokyo City Univ.)
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Speaker Author-1 
Date Time 2023-03-17 16:25:00 
Presentation Time 20 minutes 
Registration for NLP 
Paper # MSS2022-109, NLP2022-154 
Volume (vol) vol.122 
Number (no) no.435(MSS), no.436(NLP) 
Page pp.225-230 
#Pages 6 
Date of Issue 2023-03-08 (MSS, NLP) 

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