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| Presentation | 2016-11-16 15:00 Policy search based on sample clustering with Gaussian mixture model Taiki Yano, Shinichi Maeda (Kyoto Univ.)  IBISML2016-46 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | EM-based Policy Hyper Parameter Exploration (EPHE)(Wang et al., 2016) is a method that kills two birds with one stone; it has both advantages of Policy Gradient Parameter Exploration (PGPE)(Sehnke et al., 2010) that can conduct stochastic exploration based on deterministic policy, and an EM-type algorithm that is free of meticulous tuning for stepsize selection. However, EPHE are often forced to introduce rather heuristic rules to create samples to be used for the policy update. We used mixture of Gaussian distribution to justify this heuristic approach. This modification enabled automatic informative selection of the sample set that is free of heuristic post-processing. The results of computer-simulated experiments revealed that EM-based policy search with Gaussian mixture model sometimes fails to find better strategy due to the poor exploration arisen from the shrink of the variance of the cluster during learning. To remedy this failure, we introduced the covariance matrix adaptation mechanism and found that our new algorithm achieved comparable or better performance. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Reinforcement Learning / EM-based policy search / Gaussian mixture model / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 116, no. 300, IBISML2016-46, pp. 9-15, Nov. 2016. | |
| Paper # | IBISML2016-46 | |
| Date of Issue | 2016-11-09 (IBISML) | |
| 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 | IBISML2016-46 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2016-11-16 - 2016-11-18 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kyoto Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Information-Based Induction Science Workshop (IBIS2016) |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2016-11-IBISML |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Policy search based on sample clustering with Gaussian mixture model |
| Sub Title (in English) | |
| Keyword(1) | Reinforcement Learning |
| Keyword(2) | EM-based policy search |
| Keyword(3) | Gaussian mixture model |
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| 1st Author's Name | Taiki Yano |
| 1st Author's Affiliation | Kyoto University (Kyoto Univ.) |
| 2nd Author's Name | Shinichi Maeda |
| 2nd Author's Affiliation | Kyoto University (Kyoto Univ.) |
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| Speaker | Author-1 |
| Date Time | 2016-11-16 15:00:00 |
| Presentation Time | 180 minutes |
| Registration for | IBISML |
| Paper # | IBISML2016-46 |
| Volume (vol) | vol.116 |
| Number (no) | no.300 |
| Page | pp.9-15 |
| #Pages | 7 |
| Date of Issue | 2016-11-09 (IBISML) |
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