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Paper Abstract and Keywords
Presentation 2026-07-10 10:30
Enhancing Exploration through Inter-Agent Interactions in Multi-Agent Reinforcement Learning
Ryoei Maeda, Takeshi Kano, Yuichi Katori (FUN) NLP2026-36
Abstract (in Japanese) (See Japanese page) 
(in English) In multi-agent reinforcement learning, exploration of the state space and the learning process can depend on the policies of other agents, which can make efficient exploration difficult. This study developed a simplified fighting game environment and constructed a reinforcement learning model based on echo state network based function approximation, followed by learning experiments. Focusing on the acquisition of environmental knowledge during learning, three training settings were compared: learning against a random opponent, mutual learning, and role-swapping mutual learning, in which agent roles are periodically exchanged. Results show that role-swapping mutual learning achieved higher performance than learning against random opponents, particularly in evaluations against rule-based agents, suggesting that interaction among agents may facilitate the acquisition of environmental and strategic knowledge. However, large variance across trials was observed, indicating that challenges remain in the reproducibility of the learning effects.
Keyword (in Japanese) (See Japanese page) 
(in English) Reinforcement Learning / Multi-agent / Echo State Network / / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 94, NLP2026-36, pp. 51-56, July 2026.
Paper # NLP2026-36 
Date of Issue 2026-07-02 (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 NLP2026-36

Conference Information
Committee NLP  
Conference Date 2026-07-09 - 2026-07-10 
Place (in Japanese) (See Japanese page) 
Place (in English) Toki Koryu Kaikan (Sado) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Nonlinear problems, etc. 
Paper Information
Registration To NLP 
Conference Code 2026-07-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Enhancing Exploration through Inter-Agent Interactions in Multi-Agent Reinforcement Learning 
Sub Title (in English)  
Keyword(1) Reinforcement Learning  
Keyword(2) Multi-agent  
Keyword(3) Echo State Network  
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1st Author's Name Ryoei Maeda  
1st Author's Affiliation Future university Hakodate (FUN)
2nd Author's Name Takeshi Kano  
2nd Author's Affiliation Future university Hakodate (FUN)
3rd Author's Name Yuichi Katori  
3rd Author's Affiliation Future university Hakodate (FUN)
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Speaker Author-1 
Date Time 2026-07-10 10:30:00 
Presentation Time 25 minutes 
Registration for NLP 
Paper # NLP2026-36 
Volume (vol) vol.126 
Number (no) no.94 
Page pp.51-56 
#Pages 6 
Date of Issue 2026-07-02 (NLP) 

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