IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2014-03-18 14:00
A profit sharing reinforcement learning method using hierarchical reward propagation function based on action history
Zhenhua Gong, Hidehiro Nakano, Arata Miyauchi (Tokyo City Univ.) NC2013-139
Abstract (in Japanese) (See Japanese page) 
(in English) A Profit Sharing Reinforcement Learing (PSRL) method can realize robust learing not only in Markov Decision Process (MDP) environments but also in non-MDP environments such as Partially Observable MDP (POMDP) environments. The learing efficiency of the PSRL is significantly improved if a reinforcement function used in distributing rewards can be appropriately designed. In this paper, a PSRL method used hierarchical reward propagation function which is based on action history of learning agents is proposed. Using this method, efficient learning is possible for POMDP environments. Through numerical experiments, effectiveness of the proposed method can be verified.
Keyword (in Japanese) (See Japanese page) 
(in English) Reinforcement Learing / Profit Sharing / Reinforcement Function / / / / /  
Reference Info. IEICE Tech. Rep., vol. 113, no. 500, NC2013-139, pp. 293-298, March 2014.
Paper # NC2013-139 
Date of Issue 2014-03-10 (NC) 
ISSN Print edition: ISSN 0913-5685    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 NC2013-139

Conference Information
Committee NC MBE  
Conference Date 2014-03-17 - 2014-03-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Tamagawa University 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NC 
Conference Code 2014-03-NC-MBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A profit sharing reinforcement learning method using hierarchical reward propagation function based on action history 
Sub Title (in English)  
Keyword(1) Reinforcement Learing  
Keyword(2) Profit Sharing  
Keyword(3) Reinforcement Function  
Keyword(4)  
Keyword(5)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Zhenhua Gong  
1st Author's Affiliation Tokyo City University (Tokyo City Univ.)
2nd Author's Name Hidehiro Nakano  
2nd Author's Affiliation Tokyo City University (Tokyo City Univ.)
3rd Author's Name Arata Miyauchi  
3rd Author's Affiliation Tokyo City University (Tokyo City Univ.)
4th Author's Name  
4th Author's Affiliation ()
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
Speaker Author-1 
Date Time 2014-03-18 14:00:00 
Presentation Time 20 minutes 
Registration for NC 
Paper # NC2013-139 
Volume (vol) vol.113 
Number (no) no.500 
Page pp.293-298 
#Pages
Date of Issue 2014-03-10 (NC) 


[Return to Top Page]

[Return to IEICE Web Page]


The Institute of Electronics, Information and Communication Engineers (IEICE), Japan