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Paper Abstract and Keywords
Presentation 2024-12-20 09:40
Q-Learning with Prior Knowledge
Takahisa Imagawa, Shuichi Enokida (KIT) IBISML2024-34
Abstract (in Japanese) (See Japanese page) 
(in English) Reinforcement learning (RL) is a method for learning actions which are highly rewarding and one of the representative methods of RL is Q-learning.
Regret of Q-learning have been analyzed, however most of them assume that learning takes place from scratch.
If the agent has acquired prior knowledge about the learning domain, its learning will be more efficient as suggested in transfer learning research.
Therefore, we propose a Q-learning method, Biased Exploration Q-learning (BEQ), which assumes that the agent can acquire domain knowledge in advance.
We analyze regret of BEQ and show that its upper bound is $mathcal{O}(hat{w}_{max}sqrt{H^2S'A'Tiota} + HS'A'Delta_Q)$.
BEQ and existing methods differ in terms of whether the agent can obtain the domain knowledge or not, but this bound is smaller than those of existing methods.
Also experiments show that BEQ outperforms Potential Based Reward Shaping (PBRS), which is a common method for introducing domain knowledge.
Keyword (in Japanese) (See Japanese page) 
(in English) Reinforcement Learning / Q-Learning / Transfer Learning / Prior Knowledge / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 321, IBISML2024-34, pp. 14-27, Dec. 2024.
Paper # IBISML2024-34 
Date of Issue 2024-12-13 (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 IBISML2024-34

Conference Information
Committee IBISML  
Conference Date 2024-12-20 - 2024-12-21 
Place (in Japanese) (See Japanese page) 
Place (in English) Lecture room 1 (D101), Graduate School of Environmental Science 
Topics (in Japanese) (See Japanese page) 
Topics (in English) generative AI, machine learning 
Paper Information
Registration To IBISML 
Conference Code 2024-12-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Q-Learning with Prior Knowledge 
Sub Title (in English)  
Keyword(1) Reinforcement Learning  
Keyword(2) Q-Learning  
Keyword(3) Transfer Learning  
Keyword(4) Prior Knowledge  
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1st Author's Name Takahisa Imagawa  
1st Author's Affiliation Kyushu Institute of Technology (KIT)
2nd Author's Name Shuichi Enokida  
2nd Author's Affiliation Kyushu Institute of Technology (KIT)
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Speaker Author-1 
Date Time 2024-12-20 09:40:00 
Presentation Time 20 minutes 
Registration for IBISML 
Paper # IBISML2024-34 
Volume (vol) vol.124 
Number (no) no.321 
Page pp.14-27 
#Pages 14 
Date of Issue 2024-12-13 (IBISML) 

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