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Paper Abstract and Keywords
Presentation 2026-07-02 15:00
Concise and Effective Strategy Learning via Goal-Oriented State Reduction
Kazuki Takahashi (Kogakuin Univ.), Tomoki Fukai (OIST), Yutaka Sakai (Tamagawa Univ.), Takashi Takekawa (Kogakuin Univ.) NC2026-4 IBISML2026-4
Abstract (in Japanese) (See Japanese page) 
(in English) Model-based reinforcement learning agents typically assume a Partially Observable Markov Decision Process (POMDP), predicting rewards based on hidden states inferred through learned transition and observation models. In environments with unknown state definitions, agents must self-organize representations suitable for their specific goals. Conventional observation-prediction approaches suffer from state explosion in Redundantly Observable MDP (ROMDP) environments, which disperses the "clue density" required to learn environmental dynamics efficiently. To address this, we have developed Goal-Oriented Environment Inference (GOEI), which identifies the "core", the minimal state space necessary for reward prediction. This presentation introduces a generalized GOEI framework that scales to large-scale tasks by leveraging episodic properties to mitigate the accumulation of variational inference errors. Using the game of Goofspiel as a benchmark, we demonstrate that complex Nash equilibrium strategies can be effectively approximated with remarkably few states, showcasing the scalability and potential of this goal-oriented representation learning approach.
Keyword (in Japanese) (See Japanese page) 
(in English) Goal-Oriented Environment Inference / State abstraction / Redundantly Observable MDP / Imperfect-information games / Episodic reinforcement learning / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 90, NC2026-4, pp. 17-17, July 2026.
Paper # NC2026-4 
Date of Issue 2026-06-25 (NC, 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 NC2026-4 IBISML2026-4

Conference Information
Committee NC IBISML IPSJ-BIO IPSJ-MPS  
Conference Date 2026-07-01 - 2026-07-03 
Place (in Japanese) (See Japanese page) 
Place (in English) OIST 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NC 
Conference Code 2026-07-NC-IBISML-BIO-MPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Concise and Effective Strategy Learning via Goal-Oriented State Reduction 
Sub Title (in English)  
Keyword(1) Goal-Oriented Environment Inference  
Keyword(2) State abstraction  
Keyword(3) Redundantly Observable MDP  
Keyword(4) Imperfect-information games  
Keyword(5) Episodic reinforcement learning  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Kazuki Takahashi  
1st Author's Affiliation Kogakuin University of Technology and Engineering (Kogakuin Univ.)
2nd Author's Name Tomoki Fukai  
2nd Author's Affiliation Okinawa Institute of Science and Technology (OIST)
3rd Author's Name Yutaka Sakai  
3rd Author's Affiliation Tamagawa University (Tamagawa Univ.)
4th Author's Name Takashi Takekawa  
4th Author's Affiliation Kogakuin University of Technology and Engineering (Kogakuin Univ.)
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Speaker Author-1 
Date Time 2026-07-02 15:00:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2026-4, IBISML2026-4 
Volume (vol) vol.126 
Number (no) no.90(NC), no.91(IBISML) 
Page p.17 
#Pages 1 
Date of Issue 2026-06-25 (NC, IBISML) 

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