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| Presentation | 2009-07-13 10:30 Composition of Feature Space and State Space Dynamics Models for Model-based Reinforcement Learning Akihiko Yamaguchi, Jun Takamatsu, Tsukasa Ogasawara (NAIST)  NLP2009-15 NC2009-8 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Learning a dynamics model and a reward model during reinforcement learning is a useful way, since the agent can also update its value function by using the models. In this paper, we propose a general dynamics model that is a composition of the feature space dynamics model and the state space dynamics model. This way enables to obtain a good generalization from a small number of samples because of the linearity of the state space dynamics, while it does not lose the accuracy. We demonstrate the simulation comparison of some dynamics models used together with a Dyna algorithm. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | reinforcement learning / model-based reinforcement learning / Dyna-style planning / prioritized sweeping / dynamics model / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 109, no. 125, NC2009-8, pp. 7-12, July 2009. | |
| Paper # | NC2009-8 | |
| Date of Issue | 2009-07-06 (NLP, 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 | NLP2009-15 NC2009-8 |
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| Conference Information | |
| Committee | NC NLP |
| Conference Date | 2009-07-13 - 2009-07-14 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | NAIST |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2009-07-NC-NLP |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Composition of Feature Space and State Space Dynamics Models for Model-based Reinforcement Learning |
| Sub Title (in English) | |
| Keyword(1) | reinforcement learning |
| Keyword(2) | model-based reinforcement learning |
| Keyword(3) | Dyna-style planning |
| Keyword(4) | prioritized sweeping |
| Keyword(5) | dynamics model |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Akihiko Yamaguchi |
| 1st Author's Affiliation | Nara Institute of Science and Technology (NAIST) |
| 2nd Author's Name | Jun Takamatsu |
| 2nd Author's Affiliation | Nara Institute of Science and Technology (NAIST) |
| 3rd Author's Name | Tsukasa Ogasawara |
| 3rd Author's Affiliation | Nara Institute of Science and Technology (NAIST) |
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| Speaker | Author-1 |
| Date Time | 2009-07-13 10:30:00 |
| Presentation Time | 30 minutes |
| Registration for | NC |
| Paper # | NLP2009-15, NC2009-8 |
| Volume (vol) | vol.109 |
| Number (no) | no.124(NLP), no.125(NC) |
| Page | pp.7-12 |
| #Pages | 6 |
| Date of Issue | 2009-07-06 (NLP, NC) |
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