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| Paper Abstract and Keywords | ||
| Presentation | 2020-12-11 09:05 An Automated Driving Strategy for Microscopic Road Traffic Using Multi-Agent Deep Reinforcement Learning Ryota Suwa, Toshiharu Sugawara (Waseda Univ.)  AI2020-7 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This study proposes a method for interaction-aware automated driving in microscopic road traffic simulations using multi-agent deep reinforcement learning. In recent years, so many studies proposed an algorithm for automated driving using reinforcement learning, and some companies conducted experimental self-driving on public roads. However, existing reinforcement learning algorithms do not sufficiently consider interactions with other vehicles, where it is essential for safe driving on public roads. Therefore, we introduced a safety-oriented success rate and proposed a method that considers interactions between multiple agents in microscopic traffic environments. We also conducted an experimental evaluation in a driving simulation environment to see if the proposed method can improve self-driving safety. One experimental results suggest that it can achieve a higher success rate in microscopic road traffic situations, such as at an intersection and a junction, than existing methods that used single-agent reinforcement learning. Finally, we discuss the reasons for the improvement and future issues. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Multi-Agent / Deep Reinforcement Learning / Self-Driving / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 281, AI2020-7, pp. 34-38, Dec. 2020. | |
| Paper # | AI2020-7 | |
| Date of Issue | 2020-12-03 (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 | AI2020-7 |
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| Conference Information | |
| Committee | AI |
| Conference Date | 2020-12-10 - 2020-12-10 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online and HAMAMATSU ACT CITY |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Foundations and application technologies for AI systems on the new normal |
| Paper Information | |
| Registration To | AI |
| Conference Code | 2020-12-AI |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | An Automated Driving Strategy for Microscopic Road Traffic Using Multi-Agent Deep Reinforcement Learning |
| Sub Title (in English) | |
| Keyword(1) | Multi-Agent |
| Keyword(2) | Deep Reinforcement Learning |
| Keyword(3) | Self-Driving |
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| 1st Author's Name | Ryota Suwa |
| 1st Author's Affiliation | Waseda University (Waseda Univ.) |
| 2nd Author's Name | Toshiharu Sugawara |
| 2nd Author's Affiliation | Waseda University (Waseda Univ.) |
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| Speaker | Author-1 |
| Date Time | 2020-12-11 09:05:00 |
| Presentation Time | 25 minutes |
| Registration for | AI |
| Paper # | AI2020-7 |
| Volume (vol) | vol.120 |
| Number (no) | no.281 |
| Page | pp.34-38 |
| #Pages | 5 |
| Date of Issue | 2020-12-03 (AI) |
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