Paper Abstract and Keywords |
Presentation |
2024-01-17 14:30
Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator Ryoma Furuyama, Daiki Kuyoshi, Yamane Satoshi (Kanazawa Univ.) MSS2023-55 SS2023-34 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Imitation learning is often used in addition to reinforcement learning in environments where reward design is difficult or where the reward is sparse, but it is difficult to be able to imitate well in unknown states from a small amount of expert data and sampling data. Supervised learning methods such as Behavioral Cloning(BC) do not require sampling data, but usually suffer from distribution shift. The methods based on reinforcement learning, such as inverse reinforcement learning and Generative Adversarial imitation learning (GAIL), can learn from only a few expert data. However, they often need to interact with the environment. Soft Q imitation learning (SQIL) addressed the problems, and it was shown that it could learn efficiently by combining Behavioral Cloning and soft Q-learning with constant rewards. In order to make this algorithm more robust to distribution shift, we propose more efficient and robust algorithm by adding to this method a reward function based on adversarial inverse reinforcement learning that rewards the agent for performing actions in status similar to the demo. We call this algorithm Discriminator Soft Q Imitation Learning (DSQIL) . We evaluated it on MuJoCo environments. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Artificial Intelligence / Machine Learning / Deep Reinforcement Learning / Imitation Learning / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 123, no. 334, MSS2023-55, pp. 19-24, Jan. 2024. |
Paper # |
MSS2023-55 |
Date of Issue |
2024-01-10 (MSS, SS) |
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) |
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MSS2023-55 SS2023-34 |
Conference Information |
Committee |
SS MSS |
Conference Date |
2024-01-17 - 2024-01-18 |
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Paper Information |
Registration To |
MSS |
Conference Code |
2024-01-SS-MSS |
Language |
English |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator |
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Keyword(1) |
Artificial Intelligence |
Keyword(2) |
Machine Learning |
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Deep Reinforcement Learning |
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Imitation Learning |
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1st Author's Name |
Ryoma Furuyama |
1st Author's Affiliation |
Kanazawa University (Kanazawa Univ.) |
2nd Author's Name |
Daiki Kuyoshi |
2nd Author's Affiliation |
Kanazawa University (Kanazawa Univ.) |
3rd Author's Name |
Yamane Satoshi |
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Kanazawa University (Kanazawa Univ.) |
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Speaker |
Author-1 |
Date Time |
2024-01-17 14:30:00 |
Presentation Time |
25 minutes |
Registration for |
MSS |
Paper # |
MSS2023-55, SS2023-34 |
Volume (vol) |
vol.123 |
Number (no) |
no.334(MSS), no.335(SS) |
Page |
pp.19-24 |
#Pages |
6 |
Date of Issue |
2024-01-10 (MSS, SS) |
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