連続空間での深層強化学習による羊の群れのハーディングに関する基礎検討

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2022/02/01

タイトル(英語): A Basic Study on Herding a Flock of Sheep through Deep Reinforcement Learning in a Continuous Space

著者名: 飯村　伊智郎（熊本県立大学），鈴木　俊亮（熊本県立大学），中山　茂（鹿児島大学）

著者名(英語): Ichiro Iimura (Prefectural University of Kumamoto), Shunsuke Suzuki (Prefectural University of Kumamoto), Shigeru Nakayama (Kagoshima University)

キーワード: ハーディング，羊，群れ，牧羊犬，深層強化学習，連続空間　　herding，sheep，flock，sheepdog，deep reinforcement learning (deep RL)，continuous space

要約(英語): Strombom et al. elucidated an algorithm in which a sheepdog can skillfully control a flock of sheep to guide them to a destination. This is called the Herding Algorithm, and it models the behavior of a sheepdog in two ways: “driving”, which guides a flock of sheep to a destination, and “collecting”, which brings the sheep together into one flock. In this model, Go et al. showed that an agent (sheepdog) could herd a flock of sheep with an inference model generated by reinforcement learning (RL). However, in their previous study, RL learned only the movement behavior to the positions at which the agent performs “driving” and “collecting” in the discretized environmental state and behavioral space. In this study, we have assumed a continuous environmental state and behavioral space. We have confirmed that even if the agent's herding behavior is the learning target, the proposed inference model generated by deep RL can herd sheep.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.142 No.2 （2022） 特集：確率的最適化手法・機械学習技術を用いたシステム知能化の最新動向

本誌掲載ページ: 149-150 p

原稿種別: 研究開発レター／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/142/2/142_149/_article/-char/ja/