逃离掠食者诱导的流动:智能猎物策略与方向和游泳行动

Bocheng Li1, Jingran Qiu1,2, Lihao Zhao1,3

  • 1AML, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, P. R. China. zhaolihao@tsinghua.edu.cn.

Soft matter
|February 25, 2025
PubMed
概括

浮游生物使用强化学习 (RL) 来开发逃离捕食者的逃生策略. 将方向盘和速度控制相结合,提供了最有效的避开方式,性能优于单个操作.

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