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Paper Abstract and Keywords
Presentation 2026-03-20 16:15
Evaluation of Training Environments and Generalization Performance of RL-based Exploration Policies for On-the-fly Controller Synthesis
Yusei Ishimizu (Science Tokyo), Toshihide Ubukata (Waseda Univ.), Enhong Mu (SWU), Jialong Li (Waseda Univ.), Kenji Tei (Science Tokyo) KBSE2025-71
Abstract (in Japanese) (See Japanese page) 
(in English) Controller synthesis for Discrete Event Systems (DES) ensures system safety and non-blocking properties but suffers from the state explosion problem. To mitigate this, On-the-fly Directed Controller Synthesis (OTF-DCS) combined with Reinforcement Learning (RL)-based heuristics has been proposed. However, the impact of training environment parameters (e.g., number of components, state depth) on the generalization performance and robustness of the learned policies against unknown test instances remains unclear. This paper evaluates the solvability and exploration efficiency of RL-based exploration policies trained under different parameter settings by zero-shot transfer to a wide range of problem instances using benchmarks such as Travel Agency and Bidding Workflow. The experimental results reveal that the optimal training strategy differs fundamentally depending on whether the target domain requires "global consensus formation" or is dominated by "local resource contention.
Keyword (in Japanese) (See Japanese page) 
(in English) Discrete Event Systems / Controller Synthesis / On-the-fly Exploration / Reinforcement Learning / Generalization Performance / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 423, KBSE2025-71, pp. 130-134, March 2026.
Paper # KBSE2025-71 
Date of Issue 2026-03-12 (KBSE) 
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 KBSE2025-71

Conference Information
Committee KBSE  
Conference Date 2026-03-19 - 2026-03-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Prefectural General Welfare Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To KBSE 
Conference Code 2026-03-KBSE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Evaluation of Training Environments and Generalization Performance of RL-based Exploration Policies for On-the-fly Controller Synthesis 
Sub Title (in English)  
Keyword(1) Discrete Event Systems  
Keyword(2) Controller Synthesis  
Keyword(3) On-the-fly Exploration  
Keyword(4) Reinforcement Learning  
Keyword(5) Generalization Performance  
Keyword(6)  
Keyword(7)  
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1st Author's Name Yusei Ishimizu  
1st Author's Affiliation Institute of Science Tokyto (Science Tokyo)
2nd Author's Name Toshihide Ubukata  
2nd Author's Affiliation Waseda University (Waseda Univ.)
3rd Author's Name Enhong Mu  
3rd Author's Affiliation Southwest University (SWU)
4th Author's Name Jialong Li  
4th Author's Affiliation Waseda University (Waseda Univ.)
5th Author's Name Kenji Tei  
5th Author's Affiliation Institute of Science Tokyto (Science Tokyo)
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Speaker Author-1 
Date Time 2026-03-20 16:15:00 
Presentation Time 30 minutes 
Registration for KBSE 
Paper # KBSE2025-71 
Volume (vol) vol.125 
Number (no) no.423 
Page pp.130-134 
#Pages 5 
Date of Issue 2026-03-12 (KBSE) 

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