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Hokkaido University Collection of Scholarly and Academic Papers >
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Reinforcement Learning for Multi-Agent Systems with Temporal Logic Specifications
| Title: | Reinforcement Learning for Multi-Agent Systems with Temporal Logic Specifications |
| Authors: | Terashima, Keita Browse this author | | Kobayashi, Koichi Browse this author →KAKEN DB | | Yamashita, Yuh Browse this author →KAKEN DB |
| Keywords: | multi-agent systems | | reinforcement learning | | linear temporal logic | | aggregator | | surveillance |
| Issue Date: | 1-Jan-2024 |
| Publisher: | IEICE - Institute of the Electronics, Information and Communication Engineers |
| Journal Title: | IEICE transactions on fundamentals of electronics communications and computer sciences |
| Volume: | E107A |
| Issue: | 1 |
| Start Page: | 31 |
| End Page: | 37 |
| Publisher DOI: | 10.1587/transfun.2023KEP0016 |
| Abstract: | In a multi-agent system, it is important to consider a design method of cooperative actions in order to achieve a common goal. In this paper, we propose two novel multi-agent reinforcement learning methods, where the control specification is described by linear temporal logic formulas, which represent a common goal. First, we propose a simple solution method, which is directly extended from the single-agent case. In this method, there are some technical issues caused by the increase in the number of agents. Next, to overcome these technical issues, we propose a new method in which an aggregator is introduced. Finally, these two methods are compared by numerical simulations, with a surveillance problem as an example. |
| Rights: | copyright©2024 IEICE |
| Type: | article |
| URI: | http://hdl.handle.net/2115/92468 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 小林 孝一
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