視野領域を制限した追跡問題におけるProfit Sharing法に基づく学習方法の提案
視野領域を制限した追跡問題におけるProfit Sharing法に基づく学習方法の提案
カテゴリ: 論文誌(論文単位)
グループ名: 【D】産業応用部門
発行日: 2024/04/01
タイトル(英語): Development of Profit Sharing Methods for the Pursuit Problem Considering Field of View
著者名: 澤田 志門(東海大学大学院 総合理工学研究科 総合理工学専攻),奥山 淳(東海大学大学院 総合理工学研究科 総合理工学専攻)
著者名(英語): Shimon Sawada (Graduate School of Science and Technology, Tokai University), Atsushi Okuyama (Graduate School of Science and Technology, Tokai University)
キーワード: マルチエージェントシステム,強化学習,Profit Sharing,追跡問題 multi agent system,reinforcement learning,profit sharing,pursuit problem
要約(英語): To study security at facilities, particularly the capture of a suspicious person using only security robots, we investigated the pursuit problem. Multi-agent reinforcement learning has frequently been applied to this problem, in which multiple hunters catch single or multiple preys. In the pursuit problem, hunters are typically considered to have an infinite field of view (FoV) range to obtain the absolute or relative positions of the preys as the states of the hunters. However, this is inappropriate owing to “the curse of dimensionality,” and the FoV range should be restricted suitably. Moreover, strict restrictions on the FoV range prevent hunters from observing their states and continuing learning because they cannot obtain the position of a prey when it is outside their FoV. In previous studies, when a prey is out of the FoV of the hunters, they act randomly. However, in this study, we developed methods to allow hunters to continue learning when a prey is outside their FoV. In addition, numerical simulations were performed to evaluate the effectiveness of each method. The simulation results validated the effectiveness of developed methods from the viewpoints of learning performance and application of the learning results.
本誌: 電気学会論文誌D(産業応用部門誌) Vol.144 No.4 (2024) 特集:モータドライブと関連技術
本誌掲載ページ: 234-247 p
原稿種別: 論文/日本語
電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejias/144/4/144_234/_article/-char/ja/
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