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| Presentation | 2009-03-12 14:50 Adaptive Importance Sampling with Automatic Model Selection in Reward Weighted Regression Hirotaka Hachiya (Tokyo Inst. of Tech.), Jan Peters (Max Planck Inst. of Tech.), Masashi Sugiyama (Tokyo Inst. of Tech.)  NC2008-145 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Direct policy search is a useful framework of reinforcement learning in particular in continuous systems such as robot control. Policy search often requires a large number of samples for obtaining a stable estimator due to its high flexibility, but this is prohibitive when the sampling cost is expensive. In this paper, we extend the state-of-the-art policy search method called reward weighted regression so that previously collected samples can be efficiently reused. The usefulness of the proposed method, called Reward-weighted Regression with sample Reuse (R3), is demonstrated through a toy example. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Reinforcement learning / Policy search / Expectation-Maximization algorithm / Adaptive importance sampling / Importance weighted cross validation / sample reuse / Covariate shift / robot control | |
| Reference Info. | IEICE Tech. Rep., vol. 108, no. 480, NC2008-145, pp. 249-254, March 2009. | |
| Paper # | NC2008-145 | |
| 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 | NC2008-145 |
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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 | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Adaptive Importance Sampling with Automatic Model Selection in Reward Weighted Regression |
| Sub Title (in English) | |
| Keyword(1) | Reinforcement learning |
| Keyword(2) | Policy search |
| Keyword(3) | Expectation-Maximization algorithm |
| Keyword(4) | Adaptive importance sampling |
| Keyword(5) | Importance weighted cross validation |
| Keyword(6) | sample reuse |
| Keyword(7) | Covariate shift |
| Keyword(8) | robot control |
| 1st Author's Name | Hirotaka Hachiya |
| 1st Author's Affiliation | Tokyo Institute of Technology (Tokyo Inst. of Tech.) |
| 2nd Author's Name | Jan Peters |
| 2nd Author's Affiliation | Max Planck Institute (Max Planck Inst. of Tech.) |
| 3rd Author's Name | Masashi Sugiyama |
| 3rd Author's Affiliation | Tokyo Institute of Technology (Tokyo Inst. of Tech.) |
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| Speaker | Author-1 |
| Date Time | 2009-03-12 14:50:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | NC2008-145 |
| Volume (vol) | vol.108 |
| Number (no) | no.480 |
| Page | pp.249-254 |
| #Pages | 6 |
| Date of Issue | 2009-03-04 (NC) |
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