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| Presentation | 2021-08-27 10:25 Combining Multiagent Reinforcement Learning and Discrete Event Modeling for Pathfinding on a Non-Grid Graph Shiyao Ding (Kyoto Univ.), Hideki Aoyama (Panasonic), Donghui Lin (Kyoto Univ.)  SWIM2021-15 SC2021-13 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this report, we study a new multiagent path finding (MAPF) problem where multiple agents move on a non-grid graph with the goal of minimizing the traveling time summation of all agents. Multiagent reinforcement learning (MARL), which is effective to solve the traditional MAPF problems on a grid graph, can be applied in this new problem. However, considering the following two issues brought by the non-grid feature: 1) the action space is large where the agent actions are the nodes it can arrive rather than only four directions (up, down, right, left); 2) the state space is large where the agent can stay at edges rather than only nodes, MARL cannot learn optimal paths for all agents effectively. As for solving this problem, we propose a novel MARL algorithm by importing a discrete event model to MARL. Specifically, one part of agents’ pathfinding are solved by the predefined rules. Then, based on those pathfinding results, the other part of agents are trained by MARL further, which can accelerate the learning process. Finally, the experiment results show the effectiveness of our proposed method than some existing algorithms. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Multi-agent Pathfinding / Multi-agent reinforcement learning / Drone delivery / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 157, SC2021-13, pp. 13-17, Aug. 2021. | |
| Paper # | SC2021-13 | |
| Date of Issue | 2021-08-20 (SWIM, SC) | |
| 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 | SWIM2021-15 SC2021-13 |
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| Conference Information | |
| Committee | SWIM SC |
| Conference Date | 2021-08-27 - 2021-08-27 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | SC |
| Conference Code | 2021-08-SWIM-SC |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Combining Multiagent Reinforcement Learning and Discrete Event Modeling for Pathfinding on a Non-Grid Graph |
| Sub Title (in English) | |
| Keyword(1) | Multi-agent Pathfinding |
| Keyword(2) | Multi-agent reinforcement learning |
| Keyword(3) | Drone delivery |
| Keyword(4) | |
| Keyword(5) | |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Shiyao Ding |
| 1st Author's Affiliation | Kyoto University (Kyoto Univ.) |
| 2nd Author's Name | Hideki Aoyama |
| 2nd Author's Affiliation | Panasonic Corporation (Panasonic) |
| 3rd Author's Name | Donghui Lin |
| 3rd Author's Affiliation | Kyoto University (Kyoto Univ.) |
| Speaker | Author-1 |
| Date Time | 2021-08-27 10:25:00 |
| Presentation Time | 25 minutes |
| Registration for | SC |
| Paper # | SWIM2021-15, SC2021-13 |
| Volume (vol) | vol.121 |
| Number (no) | no.156(SWIM), no.157(SC) |
| Page | pp.13-17 |
| #Pages | 5 |
| Date of Issue | 2021-08-20 (SWIM, SC) |
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