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

Yi Zhu1, Jian-Hua Pang1,2, Tong Gao3

  • 1Ocean Intelligence Technology Center, Shenzhen Institute of Guangdong Ocean University, Shenzhen, Guangdong 518055, People's Republic of China.

Bioinspiration & Biomimetics
|November 2, 2022
PubMed
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This summary is machine-generated.

Fish schooling formations are maintained through active control policies and tail-beat flapping, enabling stable swimming. Followers exhibit reduced energy expenditure, optimizing swimming efficiency in dense schools.

Area of Science:

  • Fluid dynamics
  • Biophysics
  • Robotics

Background:

  • Fish schooling is a complex behavior with unclear mechanisms for maintaining stable formations.
  • Understanding these dynamics is crucial for fields ranging from marine biology to bio-inspired robotics.

Purpose of the Study:

  • To numerically investigate the mechanisms behind stable fish schooling formations.
  • To explore how individual fish maintain position and control movement within a dense school.

Main Methods:

  • A hybrid approach combining multi-agent deep reinforcement learning and the immersed boundary-lattice Boltzmann method.
  • Development of active control policies for leader and follower swimmers to maintain proximity and desired paths.

Main Results:

Keywords:
collective motionfish schoolingimmersed boundary-lattice Boltzmann methodmulti-agent deep reinforcement learningside-by-side swimmingstaggered swimming

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  • Swimmers successfully maintained stable formations against hydrodynamic forces using only tail-beat flapping.
  • Irregular and asymmetrical tail movements were observed, indicating kinematic adjustments for force balance.
  • Follower fish demonstrated reduced amplitude and cost of transport, indicating energy efficiency.
  • Side-by-side formations were hydrodynamically stable but energetically less efficient; staggered formations were more efficient overall.

Conclusions:

  • Fish schooling involves sophisticated active control and kinematic adjustments to balance hydrodynamic forces.
  • Different schooling configurations offer trade-offs between stability and energetic efficiency.
  • This study provides insights into collective animal behavior and informs the design of bio-inspired robotic systems.