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Paper Abstract and Keywords
Presentation 2025-12-23 11:50
Control of Refrigerant Circuits Using Reinforcement Learning
Tsuchida Hayamoto, Bisser Raytchev (Hiroshima Univ), Takumi Uemura, Mikimasa Kawaguchi (Mazda), Yanlei Gu, Toru Higaki (Hiroshima Univ) IBISML2025-35
Abstract (in Japanese) (See Japanese page) 
(in English) This study, conducted as a collaborative project with Mazda Corporation, aims to compare the performance of reinforcement learning methods for a control task. Soft Actor-Critic (SAC) was used as the benchmark method, and three additional approaches―Koopman-Inspired Proximal Policy Optimization (KIPPO), Q-weighted Variational Policy Optimization (QVPO), and DIffusion POlicy (DIPO)―were evaluated. These methods were applied to the same refrigerant circuit control task, and their respective characteristics and control performance were assessed.
Keyword (in Japanese) (See Japanese page) 
(in English) Reinforcement Learning / Control / Policy Optimization / Diffusion model / Koopman Theory / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 308, IBISML2025-35, pp. 88-94, Dec. 2025.
Paper # IBISML2025-35 
Date of Issue 2025-12-15 (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 IBISML2025-35

Conference Information
Committee IBISML  
Conference Date 2025-12-22 - 2025-12-23 
Place (in Japanese) (See Japanese page) 
Place (in English) Nagoya Institute of Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) machine learning, etc. 
Paper Information
Registration To IBISML 
Conference Code 2025-12-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Control of Refrigerant Circuits Using Reinforcement Learning 
Sub Title (in English)  
Keyword(1) Reinforcement Learning  
Keyword(2) Control  
Keyword(3) Policy Optimization  
Keyword(4) Diffusion model  
Keyword(5) Koopman Theory  
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Keyword(7)  
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1st Author's Name Tsuchida Hayamoto  
1st Author's Affiliation Hiroshima University (Hiroshima Univ)
2nd Author's Name Bisser Raytchev  
2nd Author's Affiliation Hiroshima University (Hiroshima Univ)
3rd Author's Name Takumi Uemura  
3rd Author's Affiliation Mazda Motor Corporation (Mazda)
4th Author's Name Mikimasa Kawaguchi  
4th Author's Affiliation Mazda Motor Corporation (Mazda)
5th Author's Name Yanlei Gu  
5th Author's Affiliation Hiroshima University (Hiroshima Univ)
6th Author's Name Toru Higaki  
6th Author's Affiliation Hiroshima University (Hiroshima Univ)
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Speaker Author-1 
Date Time 2025-12-23 11:50:00 
Presentation Time 20 minutes 
Registration for IBISML 
Paper # IBISML2025-35 
Volume (vol) vol.125 
Number (no) no.308 
Page pp.88-94 
#Pages 7 
Date of Issue 2025-12-15 (IBISML) 

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