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Paper Abstract and Keywords
Presentation 2022-12-16 15:00
Learning of train control measures by means of Deep Q-Network -- Preliminary study with a single train control --
Shogo Igarashi, Takumi Fukuda, Sei Takahashi, Hideo Nakamura (Nihon Univ), Tetsuya Takata (Kyosan Electric Manufacturing) DC2022-77
Abstract (in Japanese) (See Japanese page) 
(in English) Although the predictive fuzzy control technique has been put to practical use as a train control strategy for automatic train operation systems, it is difficult to control trains considering the speed limit and gradient of all running sections due to the complexity of the model. In this paper, we propose a method of learning single train control strategies for automatic train operation systems using Deep Q-Network, which learns control strategies based on the experience of simulation in advance, and confirm that the control strategies obtained by this method provide good control in terms of on-time performance, energy saving, and good ride quality. The control strategy obtained by this method is confirmed to provide good control in terms of punctuality, energy efficiency, and good ride quality.
Keyword (in Japanese) (See Japanese page) 
(in English) Auto Train Control / Train Control / Machine Learning / Reinforcement Learning / Deep Q-Network / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 318, DC2022-77, pp. 26-29, Dec. 2022.
Paper # DC2022-77 
Date of Issue 2022-12-09 (DC) 
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)
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Conference Information
Committee DC  
Conference Date 2022-12-16 - 2022-12-16 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Safety, etc. 
Paper Information
Registration To DC 
Conference Code 2022-12-DC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Learning of train control measures by means of Deep Q-Network 
Sub Title (in English) Preliminary study with a single train control 
Keyword(1) Auto Train Control  
Keyword(2) Train Control  
Keyword(3) Machine Learning  
Keyword(4) Reinforcement Learning  
Keyword(5) Deep Q-Network  
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1st Author's Name Shogo Igarashi  
1st Author's Affiliation Department of Computer Science, Graduate School, Nihon University (Nihon Univ)
2nd Author's Name Takumi Fukuda  
2nd Author's Affiliation Department of Computer Engineering, College of Science and Technology, Nihon University (Nihon Univ)
3rd Author's Name Sei Takahashi  
3rd Author's Affiliation Department of Computer Engineering, College of Science and Technology, Nihon University (Nihon Univ)
4th Author's Name Hideo Nakamura  
4th Author's Affiliation Professor Emeritus of Nihon University (Nihon Univ)
5th Author's Name Tetsuya Takata  
5th Author's Affiliation Kyosan Electric Manufacturing Co.,Ltd. (Kyosan Electric Manufacturing)
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Speaker Author-1 
Date Time 2022-12-16 15:00:00 
Presentation Time 20 minutes 
Registration for DC 
Paper # DC2022-77 
Volume (vol) vol.122 
Number (no) no.318 
Page pp.26-29 
#Pages
Date of Issue 2022-12-09 (DC) 


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