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Learning to flock through reinforcement.

Mihir Durve1, Fernando Peruani2, Antonio Celani3

  • 1Department of Physics, Università degli studi di Trieste, Trieste 34127, Italy and Quantitative Life Sciences Unit, The Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste 34151, Italy.

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Summary
This summary is machine-generated.

Multiagent reinforcement learning enables agents to learn flocking behavior. Agents learn to maintain group cohesion by aligning their velocity with neighbors, mimicking natural flocking strategies.

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Area of Science:

  • Collective behavior
  • Artificial intelligence
  • Computational neuroscience

Background:

  • Coordinated group motion, like bird flocking and fish schooling, emerges from individual actions.
  • Understanding the mechanisms behind collective behavior is crucial in various biological and artificial systems.

Purpose of the Study:

  • To investigate flocking behavior using multiagent reinforcement learning.
  • To determine if agents can learn flocking strategies through interaction and limited sensory input.
  • To analyze the emergent navigation strategies and their relation to known models.

Main Methods:

  • Utilized standard reinforcement learning algorithms.
  • Simulated agents with limited sensory input (neighboring velocities).
  • Trained agents against 'teacher' agents and in self-organized groups.

Main Results:

  • Learning agents successfully acquired flocking behavior, either by imitating teachers or through self-organization.
  • The emergent strategy matched the polar velocity alignment of the Vicsek model.
  • Velocity alignment was identified as an optimal strategy for maintaining group cohesion with limited sensory information.

Conclusions:

  • Velocity alignment may be an evolved adaptive behavior for minimizing neighbor loss.
  • This alignment strategy effectively promotes local polar order and group cohesion.
  • Reinforcement learning provides a framework for understanding the emergence of complex collective behaviors.