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| Presentation | 2021-03-04 16:25 Hierarchical Feature Extraction for Dynamic Q-Network Taishi Komatsu, Yukari Yamauchi (Nihon Univ.)  NC2020-62 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Recently, Convolutional Neural Networks (CNN), which have been successful in the field of image recognition, use a hierarchical convolutional structure to represent the simple and complex cells of the brain, enabling the recognition of complex shapes. Dynamic Q-Network is an algorithm that dynamically learns feature extraction by adapting Self-Organizing Neural Network (SOINN) as the feature extraction part of Deep Q-Network. In this research, we propose a method of placing a Self-Organizing Map (SOM) below the SOINN layer in the feature extraction part of Dynamic Q-Network, and dividing the feature extraction part into two layers of SOM-SOINN. By performing feature extraction in a hierarchical manner, we aim to improve the learning accuracy of the Q-Network by distinguishing between simple and complex firings of the input information, thereby enabling feature extraction of higher dimensions. The effectiveness of the proposed method is shown based on the processing time and the win rate in the experimental environment of Reversi. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Reinforcement Learning / Convolutional Neural Network / Self-Organizing Incremental Neural Network / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 403, NC2020-62, pp. 112-116, March 2021. | |
| Paper # | NC2020-62 | |
| Date of Issue | 2021-02-24 (NC) | |
| ISSN | 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 | NC2020-62 |
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| Conference Information | |
| Committee | NC MBE |
| Conference Date | 2021-03-03 - 2021-03-05 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Neuro Computing, Medical Engineering, etc. |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2021-03-NC-MBE |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Hierarchical Feature Extraction for Dynamic Q-Network |
| Sub Title (in English) | |
| Keyword(1) | Reinforcement Learning |
| Keyword(2) | Convolutional Neural Network |
| Keyword(3) | Self-Organizing Incremental Neural Network |
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| 1st Author's Name | Taishi Komatsu |
| 1st Author's Affiliation | Nihon University (Nihon Univ.) |
| 2nd Author's Name | Yukari Yamauchi |
| 2nd Author's Affiliation | Nihon University (Nihon Univ.) |
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| Speaker | Author-1 |
| Date Time | 2021-03-04 16:25:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | NC2020-62 |
| Volume (vol) | vol.120 |
| Number (no) | no.403 |
| Page | pp.112-116 |
| #Pages | 5 |
| Date of Issue | 2021-02-24 (NC) |
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