Cooperation and Coordination Regimes by Deep Q-Learning in Multi-agent Task Executions

Yuki Miyashita*, Toshiharu Sugawara

*この研究の対応する著者

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

We investigate the coordination structures generated by deep Q-network (DQN) with various types of input by using a distributed task execution game. Although cooperation and coordination are mandatory for efficiency in multi-agent systems (MAS), they require sophisticated structures or regimes for effective behaviors. Recently, deep Q-learning has been applied to multi-agent systems to facilitate their coordinated behavior. However, the characteristics of the learned results have not yet been fully clarified. We investigate how information input to DQNs affect the resultant coordination and cooperation structures. We examine the inputs generated from local observations with and without the estimated location in the environment. Experimental results show that they form two types of coordination structures—the division of labor and the targeting of near tasks while avoiding conflicts—and that the latter is more efficient in our game. We clarify the mechanism behind and the characteristics of the generated coordination behaviors.

本文言語English
ホスト出版物のタイトルArtificial Neural Networks and Machine Learning – ICANN 2019
ホスト出版物のサブタイトルTheoretical Neural Computation - 28th International Conference on Artificial Neural Networks, 2019, Proceedings
編集者Igor V. Tetko, Pavel Karpov, Fabian Theis, Vera Kurková
出版社Springer Verlag
ページ541-554
ページ数14
ISBN(印刷版)9783030304867
DOI
出版ステータスPublished - 2019
イベント28th International Conference on Artificial Neural Networks, ICANN 2019 - Munich, Germany
継続期間: 2019 9 172019 9 19

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)
11727 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference28th International Conference on Artificial Neural Networks, ICANN 2019
国/地域Germany
CityMunich
Period19/9/1719/9/19

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)

フィンガープリント

「Cooperation and Coordination Regimes by Deep Q-Learning in Multi-agent Task Executions」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル