Paper Abstract and Keywords |
Presentation |
2008-01-29 13:55
A Study on Applying Reinforcement Learning to a Safety Critical System Shinichi Fujiwara, Toshiyuki Miyamoto, Sadatoshi Kumagai (Osaka Univ.) CST2007-53 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Reinforcement Learning have been attracting many researchers attention as a framework of autonomous cooperative behavior acquisition. In this framework, actor adapts to an environment with the help of a reward obtained as a result of the trial-and-error or application of self knowledge.
Now, we discuss a possibility to apply a multi-agent reinforcement learning to already proposed damage control mechanism of Safety Critical System(SCS). In the SCS, we suppose the total system is divided into some sections and performance is secured by controlling damages under a certain level. Then we employ a reinforcement learning method for a decision-making of damage control mechanism. SCS is different from existing other testbed of multi-agent reinforcement learning because it is similar to the concept of the Quality of Service in that we control damages differently according to each required damage level. According to a simulation the Q-learning is employed as a reinforcement learning method, we regarded the reinforcement learning is possible to apply to the SCS. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Multi-agent System / Reinforcement Learning / Q-learning / Safety Critical System / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 107, Jan. 2008. |
Paper # |
|
Date of Issue |
2008-01-22 (CST) |
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) |
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CST2007-53 |
Conference Information |
Committee |
MSS |
Conference Date |
2008-01-28 - 2008-01-29 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
The University of Tokushima |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Concurrent Systems, CST Solution Competition 2007 |
Paper Information |
Registration To |
MSS |
Conference Code |
2008-01-CST |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A Study on Applying Reinforcement Learning to a Safety Critical System |
Sub Title (in English) |
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Multi-agent System |
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Reinforcement Learning |
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Q-learning |
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Safety Critical System |
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1st Author's Name |
Shinichi Fujiwara |
1st Author's Affiliation |
Osaka University (Osaka Univ.) |
2nd Author's Name |
Toshiyuki Miyamoto |
2nd Author's Affiliation |
Osaka University (Osaka Univ.) |
3rd Author's Name |
Sadatoshi Kumagai |
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Osaka University (Osaka Univ.) |
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Speaker |
Author-1 |
Date Time |
2008-01-29 13:55:00 |
Presentation Time |
25 minutes |
Registration for |
MSS |
Paper # |
CST2007-53 |
Volume (vol) |
vol.107 |
Number (no) |
no.472 |
Page |
pp.41-46 |
#Pages |
6 |
Date of Issue |
2008-01-22 (CST) |
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