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Paper Abstract and Keywords
Presentation 2024-03-03 17:00
Multi-agent reinforcement learning based control method for large-scale crowd movement on Mojiko Fireworks Festival dataset
Kazuya Miyazaki, Masato Kiyama, Motoki Amagasaki, Toshiaki Okamoto (Kumamoto Univ.) IBISML2023-45
Abstract (in Japanese) (See Japanese page) 
(in English) The importance of human flow guidance is increasing in response to accidents at events. In recent years, some research has applied reinforcement learning to simulation. Reinforcement learning agents receive the current state from the simulator and learn behaviours that maximise rewards through repeated failures and successes. In this study, we apply MA-DDPG, a method of multi-agent reinforcement learning, and propose a method of learning an action value function for an actor in MA-DDPG. Then, we perform congestion reduction on the Mojiko Fireworks Festival dataset. In the evaluation, the congestion of the previous research model and the proposed model are compared.Comparison of the methods shows that the proposed method succeeds in reducing congestion compared to the existing methods. The proposed method was found to be more successful than the other methods in reducing congestion through cooperation between agents.
Keyword (in Japanese) (See Japanese page) 
(in English) Pedestrian Simulation / Pedestrian Guidance / Multi-Agent Reinforcement Learning / Deep Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 410, IBISML2023-45, pp. 36-43, March 2024.
Paper # IBISML2023-45 
Date of Issue 2024-02-25 (IBISML) 
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 IBISML2023-45

Conference Information
Committee PRMU IBISML IPSJ-CVIM  
Conference Date 2024-03-03 - 2024-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Hiroshima Univ. Higashi-Hiroshima campus 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IBISML 
Conference Code 2024-03-PRMU-IBISML-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Multi-agent reinforcement learning based control method for large-scale crowd movement on Mojiko Fireworks Festival dataset 
Sub Title (in English)  
Keyword(1) Pedestrian Simulation  
Keyword(2) Pedestrian Guidance  
Keyword(3) Multi-Agent Reinforcement Learning  
Keyword(4) Deep Learning  
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1st Author's Name Kazuya Miyazaki  
1st Author's Affiliation Kumamoto University (Kumamoto Univ.)
2nd Author's Name Masato Kiyama  
2nd Author's Affiliation Kumamoto University (Kumamoto Univ.)
3rd Author's Name Motoki Amagasaki  
3rd Author's Affiliation Kumamoto University (Kumamoto Univ.)
4th Author's Name Toshiaki Okamoto  
4th Author's Affiliation Kumamoto University (Kumamoto Univ.)
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Speaker Author-1 
Date Time 2024-03-03 17:00:00 
Presentation Time 15 minutes 
Registration for IBISML 
Paper # IBISML2023-45 
Volume (vol) vol.123 
Number (no) no.410 
Page pp.36-43 
#Pages 8 
Date of Issue 2024-02-25 (IBISML) 

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