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| Paper Abstract and Keywords | ||
| Presentation | 2019-06-18 13:30 Hybrid Reinforcement and Imitation Learning for Human-Like Agents Rousslan Fernand Julien Dossa, Xinyu Lian (Kobe Uni), Hirokazu Nomoto (EQUOS RESEARCH), Takashi Matsubara, Kuniaki Uehara (Kobe Uni)  NC2019-16 IBISML2019-14 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Reinforcement learning methods achieve performance superior to humans in a wide range of complex tasks and uncertain environments. However, high performance is not the sole metric for practical use, namely when used as a game AI or autonomous driving agent, since highly efficient agent tends to perform greedily and selfishly, therefore inconveniencing the users. Consequently, there is a need for more human-like agents. Imitation learning, on the other hand, aims at reproducing the behavior of a human expert and can be used to train a human-like agent, the caveat being that its performance is generally limited by the expert's skill. In the study, we propose a training scheme to construct a human-like and efficient agent through a hybrid of reinforcement and imitation learning, and apply it to a racing car simulator. The proposed hybrid agent achieves a higher performance than a strictly imitation learning agent while exhibits more human-like behavior, which is measured via a human sensitivity test. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Autonomous Driving / Game AI / Human-Like Behavior / Imitation Learning / Reinforcement Learning / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 88, NC2019-16, pp. 69-74, June 2019. | |
| Paper # | NC2019-16 | |
| Date of Issue | 2019-06-10 (NC, IBISML) | |
| 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 | NC2019-16 IBISML2019-14 |
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| Conference Information | |
| Committee | NC IBISML IPSJ-MPS IPSJ-BIO |
| Conference Date | 2019-06-17 - 2019-06-19 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Okinawa Institute of Science and Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Neurocomputing, Machine Learning Approach to Biodata Mining, and General |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2019-06-NC-IBISML-MPS-BIO |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Hybrid Reinforcement and Imitation Learning for Human-Like Agents |
| Sub Title (in English) | |
| Keyword(1) | Autonomous Driving |
| Keyword(2) | Game AI |
| Keyword(3) | Human-Like Behavior |
| Keyword(4) | Imitation Learning |
| Keyword(5) | Reinforcement Learning |
| Keyword(6) | |
| Keyword(7) | |
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| 1st Author's Name | Rousslan Fernand Julien Dossa |
| 1st Author's Affiliation | Kobe University (Kobe Uni) |
| 2nd Author's Name | Xinyu Lian |
| 2nd Author's Affiliation | Kobe University (Kobe Uni) |
| 3rd Author's Name | Hirokazu Nomoto |
| 3rd Author's Affiliation | EQUOS RESEARCH Co., Ltd. (EQUOS RESEARCH) |
| 4th Author's Name | Takashi Matsubara |
| 4th Author's Affiliation | Kobe University (Kobe Uni) |
| 5th Author's Name | Kuniaki Uehara |
| 5th Author's Affiliation | Kobe University (Kobe Uni) |
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| Speaker | Author-1 |
| Date Time | 2019-06-18 13:30:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | NC2019-16, IBISML2019-14 |
| Volume (vol) | vol.119 |
| Number (no) | no.88(NC), no.89(IBISML) |
| Page | pp.69-74(NC), pp.91-96(IBISML) |
| #Pages | 6 |
| Date of Issue | 2019-06-10 (NC, IBISML) |
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