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Paper Abstract and Keywords
Presentation 2024-01-25 14:40
Efficient exploration with intrinsic motivation considering state transitions in deep reinforcement learning
Kaito Ohshika, Hidenori Itaya, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu Univ.) PRMU2023-42
Abstract (in Japanese) (See Japanese page) 
(in English) In deep reinforcement learning, learning data is collected through the interaction between the agent and the environment, so efficient exploration of the environment leads to the acquisition of exhaustive learning data. To solve this problem, a method to improve the efficiency of exploration with intrinsic motivation of the agent has been proposed. Efficient search is achieved by evaluating the novelty of observed information and encouraging exploration into unknown state spaces. However, conventional intrinsic motivation focuses only on the current state and does not consider time series information of the environment. We propose an intrinsic motivation system that focuses on state transitions of the environment, and show the effectiveness of considering state transitions by analyzing agent performance in evaluation experiments using the Atari2600.
Keyword (in Japanese) (See Japanese page) 
(in English) reinforcement learning / intrinsic motivation / state transitions / / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 358, PRMU2023-42, pp. 14-19, Jan. 2024.
Paper # PRMU2023-42 
Date of Issue 2024-01-18 (PRMU) 
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 PRMU2023-42

Conference Information
Committee PRMU MVE VRSJ-SIG-MR IPSJ-CVIM  
Conference Date 2024-01-25 - 2024-01-26 
Place (in Japanese) (See Japanese page) 
Place (in English) Keio Univ. (Hiyoshi Campus) 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2024-01-PRMU-MVE-SIG-MR-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Efficient exploration with intrinsic motivation considering state transitions in deep reinforcement learning 
Sub Title (in English)  
Keyword(1) reinforcement learning  
Keyword(2) intrinsic motivation  
Keyword(3) state transitions  
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1st Author's Name Kaito Ohshika  
1st Author's Affiliation Chubu University (Chubu Univ.)
2nd Author's Name Hidenori Itaya  
2nd Author's Affiliation Chubu University (Chubu Univ.)
3rd Author's Name Tsubasa Hirakawa  
3rd Author's Affiliation Chubu University (Chubu Univ.)
4th Author's Name Takayoshi Yamashita  
4th Author's Affiliation Chubu University (Chubu Univ.)
5th Author's Name Hironobu Fujiyoshi  
5th Author's Affiliation Chubu University (Chubu Univ.)
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Speaker Author-1 
Date Time 2024-01-25 14:40:00 
Presentation Time 12 minutes 
Registration for PRMU 
Paper # PRMU2023-42 
Volume (vol) vol.123 
Number (no) no.358 
Page pp.14-19 
#Pages 6 
Date of Issue 2024-01-18 (PRMU) 

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