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| Presentation | 2009-03-13 09:20 Modular Reinforcement Learning based on Adaptive Model Complexity Yu Hiei (Nara Inst. of Sci and Tech.), Takeshi Mori (Kyoto Univ.), Shin Ishii (Kyoto Univ./Nara Institute of Science and Technology)  NC2008-149 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In real-world problems such as robot control, the environment surrounding a controlled system is nonstationary, and the optimal control may change with time. It is difficult to learn such a nonstationary control by using reinforcement learning (RL) which usually assumes stationary Markov decision processes. To deal with such problems, modular RL methods have been proposed, in which the nonstationary environment and the control are approximated by means of multiple modules. However, the deficiency or excess of the modules can cause the inefficient controller or make the learning slower. To overcome these difficulties, we propose a novel modular RLalgorithm, in which the optimal number of modules is seeked based on a variational Bayes method during the policy optimization. Computer simulations show that our method achieves faster and more stable learning than the previous one. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Nonstationary environment / Modular reinforcement learning / Variational Bayes method / Optimal model search / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 108, no. 480, NC2008-149, pp. 273-278, March 2009. | |
| Paper # | NC2008-149 | |
| Date of Issue | 2009-03-04 (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 | NC2008-149 |
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| Conference Information | |
| Committee | NC MBE |
| Conference Date | 2009-03-11 - 2009-03-13 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tamagawa Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2009-03-NC-MBE |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Modular Reinforcement Learning based on Adaptive Model Complexity |
| Sub Title (in English) | |
| Keyword(1) | Nonstationary environment |
| Keyword(2) | Modular reinforcement learning |
| Keyword(3) | Variational Bayes method |
| Keyword(4) | Optimal model search |
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| 1st Author's Name | Yu Hiei |
| 1st Author's Affiliation | Nara Institute of Science and Technology (Nara Inst. of Sci and Tech.) |
| 2nd Author's Name | Takeshi Mori |
| 2nd Author's Affiliation | Kyoto University (Kyoto Univ.) |
| 3rd Author's Name | Shin Ishii |
| 3rd Author's Affiliation | Kyoto University/Nara Institute of Science and Technology (Kyoto Univ./Nara Institute of Science and Technology) |
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| Speaker | Author-1 |
| Date Time | 2009-03-13 09:20:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | NC2008-149 |
| Volume (vol) | vol.108 |
| Number (no) | no.480 |
| Page | pp.273-278 |
| #Pages | 6 |
| Date of Issue | 2009-03-04 (NC) |
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