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| Paper Abstract and Keywords | ||
| Presentation | 2021-02-22 14:40 A Study on the Application of Curriculum Learning in Deep Reinforcement Learning -- action acquisition in shooting game AI as an example -- Ikumi Kodaka, Fumiaki Saito (CIT)  AI2020-47 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Deep reinforcement learning is attracting attention because it can be applied to higher-dimensional environments compared to conventional reinforcement learning. However, an important issue is to increase the number of trials required for action acquisition, particularly in high-dimensional and sparsely rewarded tasks. Therefore, in this study, we applied curriculum learning, which improves learning performance by gradually changing the difficulty level of tasks, in the action acquisition in a shooting game AI. Through experimental evaluation, we verified the speeding up of action acquisition and considered the transition of difficulty and its efficiency. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Deep Reinforcement Learning / Curriculum Learning / Deep Q-Network / Game AI / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 379, AI2020-47, pp. 47-52, Feb. 2021. | |
| Paper # | AI2020-47 | |
| Date of Issue | 2021-02-15 (AI) | |
| 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 | AI2020-47 |
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| Conference Information | |
| Committee | AI |
| Conference Date | 2021-02-22 - 2021-02-22 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Web/IoT Intelligence, etc. |
| Paper Information | |
| Registration To | AI |
| Conference Code | 2021-02-AI |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study on the Application of Curriculum Learning in Deep Reinforcement Learning |
| Sub Title (in English) | action acquisition in shooting game AI as an example |
| Keyword(1) | Deep Reinforcement Learning |
| Keyword(2) | Curriculum Learning |
| Keyword(3) | Deep Q-Network |
| Keyword(4) | Game AI |
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| 1st Author's Name | Ikumi Kodaka |
| 1st Author's Affiliation | Chiba Institute of Technology (CIT) |
| 2nd Author's Name | Fumiaki Saito |
| 2nd Author's Affiliation | Chiba Institute of Technology (CIT) |
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| Speaker | Author-1 |
| Date Time | 2021-02-22 14:40:00 |
| Presentation Time | 20 minutes |
| Registration for | AI |
| Paper # | AI2020-47 |
| Volume (vol) | vol.120 |
| Number (no) | no.379 |
| Page | pp.47-52 |
| #Pages | 6 |
| Date of Issue | 2021-02-15 (AI) |
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