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| Presentation | 2019-03-05 14:30 Efficient Exploration by Variational Information Maximizing Exploration on Reinforcement Learning Kazuki Doi, Keigo Okawa (Gifu Univ.), Motoki Shiga (Gifu Univ./JST/RIKEN)  IBISML2018-107 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In reinforcement learning，the policy function may not be optimized properly if the observed state space is limited to local sub-space.To facilitate exploration of unseen states, Variational Information Maximizing Exploration (VIME), which evaluates entropy of the next action and state, is useful. We propose to combine VIME and Trust Region Policy Optimization (TRPO). The performance of our method was evaluated using three reinforcement learning tasks. The results shows that VIME drastically improves the acquisition reward for the task that needs a lot of steps to achieve the goal state. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Reinforcement Learning / Variational Information Maximizing Exploration / Trust Region Policy Optimization / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 472, IBISML2018-107, pp. 17-22, March 2019. | |
| Paper # | IBISML2018-107 | |
| Date of Issue | 2019-02-26 (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 | IBISML2018-107 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2019-03-05 - 2019-03-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | RIKEN AIP |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Machine learning, etc. |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2019-03-IBISML |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Efficient Exploration by Variational Information Maximizing Exploration on Reinforcement Learning |
| Sub Title (in English) | |
| Keyword(1) | Reinforcement Learning |
| Keyword(2) | Variational Information Maximizing Exploration |
| Keyword(3) | Trust Region Policy Optimization |
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| 1st Author's Name | Kazuki Doi |
| 1st Author's Affiliation | Gifu University (Gifu Univ.) |
| 2nd Author's Name | Keigo Okawa |
| 2nd Author's Affiliation | Gifu University (Gifu Univ.) |
| 3rd Author's Name | Motoki Shiga |
| 3rd Author's Affiliation | Gifu University/Japan Science and Technology Agency/RIKEN (Gifu Univ./JST/RIKEN) |
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| Speaker | 1 |
| Date Time | 2019-03-05 14:30:00 |
| Presentation Time | 30 |
| Registration for | IBISML |
| Paper # | IEICE-IBISML2018-107 |
| Volume (vol) | IEICE-118 |
| Number (no) | no.472 |
| Page | pp.17-22 |
| #Pages | IEICE-6 |
| Date of Issue | IEICE-IBISML-2019-02-26 |
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