Multi-agent reinforcement learning system integrating exploitation- and exploration-oriented learning

Satoshi Kurihara, Toshiharu Sugawara, Rikio Onai

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

This paper proposes and evaluates MarLee, a multi-agent reinforcement learning system that integrates both exploitation- and exploration-oriented learning. Compared with conventional reinforcement learnings, MarLee is more robust in the face of a dynamically changing environment and is able to perform exploration-oriented learning efficiently even in a large-scale environment. Thus, MarLee is well suited for autonomous systems, for example, software agents and mobile robots, that operate in dynamic, large-scale environments, like the real-world and the Internet. Spreading activation, based on the behavior-based approach, is used to explore the environment, so by manipulating the parameters of the spreading activation, it is easy to tune the learning characteristics. The fundamental effectiveness of MarLee was demonstrated by simulation.

本文言語English
ホスト出版物のタイトルMulti-Agent Systems
ホスト出版物のサブタイトルTheories, Languages, and Applications - 4th Australian Workshop on Distributed Artificial Intelligence, 1998, Selected Papers
出版社Springer Verlag
ページ45-57
ページ数13
ISBN(印刷版)3540654771, 9783540654773
出版ステータスPublished - 1998 1 1
外部発表はい
イベント4th Australian Workshop on Distributed Artificial Intelligence, DAK 1998 - Brisbane, Australia
継続期間: 1998 7 131998 7 13

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)
1544
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other4th Australian Workshop on Distributed Artificial Intelligence, DAK 1998
国/地域Australia
CityBrisbane
Period98/7/1398/7/13

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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