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Paper Abstract and Keywords
Presentation 2024-07-18 09:50
State-Flipped Control for Probabilistic Boolean Networks Using Reinforcement Learning
Tatsuya Hasegawa, Fuma Motoyama, Koichi Kobayashi, Yuh Yamashita (Hokkaido Univ.) RCC2024-26
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a Q-learning reward design method for state-flipped control of probabilistic Boolean networks (PBNs). A PBN is known as a mathematical model of gene regulatory networks. The state in a PBN and its time evolution are represented as a binary variable vector and a set of Boolean functions, respectively. State-flipped control is a control method where some elements of a binary variable vector representing the state are flipped. Q-learning is a simple method where an agent learns a policy that maximizes the expected discounted reward in Markov decision processes. By the proposed reward design, a controller that achieves a smaller number of flipped nodes can be derived.
Keyword (in Japanese) (See Japanese page) 
(in English) reinforcement learning / probabilistic Boolean networks / state-flipped control / / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 105, RCC2024-26, pp. 43-46, July 2024.
Paper # RCC2024-26 
Date of Issue 2024-07-10 (RCC) 
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 RCC2024-26

Conference Information
Committee RCC RCS SeMI NS SR RISING  
Conference Date 2024-07-17 - 2024-07-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Citizens Activities Promotion Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To RCC 
Conference Code 2024-07-RCC-RCS-SeMI-NS-SR-RISING 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) State-Flipped Control for Probabilistic Boolean Networks Using Reinforcement Learning 
Sub Title (in English)  
Keyword(1) reinforcement learning  
Keyword(2) probabilistic Boolean networks  
Keyword(3) state-flipped control  
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1st Author's Name Tatsuya Hasegawa  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Fuma Motoyama  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Koichi Kobayashi  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ.)
4th Author's Name Yuh Yamashita  
4th Author's Affiliation Hokkaido University (Hokkaido Univ.)
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Speaker Author-1 
Date Time 2024-07-18 09:50:00 
Presentation Time 25 minutes 
Registration for RCC 
Paper # RCC2024-26 
Volume (vol) vol.124 
Number (no) no.105 
Page pp.43-46 
#Pages 4 
Date of Issue 2024-07-10 (RCC) 

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