Learning to school in dense configurations with multi-agent deep reinforcement learning.

Zhu, Yi; Pang, Jian-Hua; Gao, Tong; Tian, Fang-Bao

Zhu, Yi; Pang, Jian-Hua; Gao, Tong; Tian, Fang-Bao.

Afiliación

Zhu Y; Ocean Intelligence Technology Center, Shenzhen Institute of Guangdong Ocean University, Shenzhen, Guangdong 518055, People's Republic of China.
Pang JH; Ocean Intelligence Technology Center, Shenzhen Institute of Guangdong Ocean University, Shenzhen, Guangdong 518055, People's Republic of China.
Gao T; College of Ocean Engineering, Guangdong Ocean University, Zhanjiang, Guangdong 524088, People's Republic of China.
Tian FB; Department of Mechanical Engineering, Michigan State University, East Lansing, MI 48864, United States of America.

Bioinspir Biomim ; 18(1)2022 11 16.

Article en En | MEDLINE | ID: mdl-36322983

Asunto(s)

Refuerzo en Psicología; Natación; Animales; Hidrodinámica; Peces; Fenómenos Biomecánicos; Instituciones Académicas

Palabras clave

collective motion; fish schooling; immersed boundary-lattice Boltzmann method; multi-agent deep reinforcement learning; side-by-side swimming; staggered swimming

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Refuerzo en Psicología / Natación Límite: Animals Idioma: En Revista: Bioinspir Biomim Asunto de la revista: BIOLOGIA / ENGENHARIA BIOMEDICA Año: 2022 Tipo del documento: Article Pais de publicación: Reino Unido

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