Learning to coordinate with deep reinforcement learning in doubles pong game

Elhadji Amadou Oury Diallo, Ayumi Sugiyama, Toshiharu Sugawara

研究成果: Conference contribution

17 被引用数 (Scopus)

抄録

This paper discusses the emergence of cooperative and coordinated behaviors between joint and concurrent learning agents using deep Q-learning. Multi-agent systems (MAS) arise in a variety of domains. The collective effort is one of the main building blocks of many fundamental systems that exist in the world, and thus, sequential decision making under uncertainty for collaborative work is one of the important and challenging issues for intelligent cooperative multiple agents. However, the decisions for cooperation are highly sophisticated and complicated because agents may have a certain shared goal or individual goals to achieve and their behavior is inevitably influenced by each other. Therefore, we attempt to explore whether agents using deep Q-networks (DQN) can learn cooperative behavior. We use doubles pong game as an example and we investigate how they learn to divide their works through iterated game executions. In our approach, agents jointly learn to divide their area of responsibility and each agent uses its own DQN to modify its behavior. We also investigate how learned behavior changes according to environmental characteristics including reward schemes and learning techniques. Our experiments indicate that effective cooperative behaviors with balanced division of workload emerge. These results help us to better understand how agents behave and interact with each other in complex environments and how they coherently choose their individual actions such that the resulting joint actions are optimal.

本文言語English
ホスト出版物のタイトルProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
編集者Xuewen Chen, Bo Luo, Feng Luo, Vasile Palade, M. Arif Wani
出版社Institute of Electrical and Electronics Engineers Inc.
ページ14-19
ページ数6
ISBN(電子版)9781538614174
DOI
出版ステータスPublished - 2017
イベント16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 - Cancun, Mexico
継続期間: 2017 12月 182017 12月 21

出版物シリーズ

名前Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
2017-December

Other

Other16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
国/地域Mexico
CityCancun
Period17/12/1817/12/21

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用

フィンガープリント

「Learning to coordinate with deep reinforcement learning in doubles pong game」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