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Related Concept Videos

Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Reinforcement Schedules01:24

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Multi-input and Multi-variable systems01:22

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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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
Summary

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.

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

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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:

  • 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.