Coordination in Collaborative Work by Deep Reinforcement Learning with Various State Descriptions

Yuki Miyashita*, Toshiharu Sugawara

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Cooperation and coordination are sophisticated behaviors and are still major issues in studies on multi-agent systems because how to cooperate and coordinate depends on not only environmental characteristics but also the behaviors/strategies that closely affect each other. On the other hand, recently using the multi-agent deep reinforcement learning (MADRL) has received much attention because of the possibility of learning and facilitating their coordinated behaviors. However, the characteristics of socially learned coordination structures have been not sufficiently clarified. In this paper, by focusing on the MADRL in which each agent has its own deep Q-networks (DQNs), we show that the different types of input to the network lead to various coordination structures, using the pickup and floor laying problem, which is an abstract form related to our target problem. We also indicate that the generated coordination structures affect the entire performance of multi-agent systems.

本文言語English
ホスト出版物のタイトルPRIMA 2019
ホスト出版物のサブタイトルPrinciples and Practice of Multi-Agent Systems - 22nd International Conference, Proceedings
編集者Matteo Baldoni, Mehdi Dastani, Beishui Liao, Yuko Sakurai, Rym Zalila Wenkstern
出版社Springer
ページ550-558
ページ数9
ISBN(印刷版)9783030337919
DOI
出版ステータスPublished - 2019
イベント22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2019 - Turin, Italy
継続期間: 2019 10 282019 10 31

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11873 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2019
国/地域Italy
CityTurin
Period19/10/2819/10/31

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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