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This study introduces an intelligent hitchhiking particle (IHP) that uses reinforcement learning to navigate efficiently by attaching to active Brownian particles (ABPs). The IHP demonstrates superior persistent motion compared to individual ABPs, optimizing resource use.

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Area of Science:

  • Physics
  • Biophysics
  • Artificial Intelligence

Background:

  • Organisms can achieve goals efficiently by utilizing external information and resources.
  • Active Brownian particles (ABPs) exhibit complex motion patterns in fluid environments.

Purpose of the Study:

  • To develop a minimal model for a motorless particle performing a navigational task.
  • To enable a particle to navigate persistently by hitchhiking on ABPs.

Main Methods:

  • Development of a minimal model for an intelligent hitchhiking particle (IHP).
  • Application of a reinforcement learning algorithm to train the IHP.
  • Analysis using an analytic model to calculate mean-squared displacement.

Main Results:

  • The IHP successfully navigates persistently by identifying and attaching to suitable ABPs.
  • The IHP can anticipate and react to the motion patterns of host particles, including chiral ABPs.
  • The persistent motion of the IHP outperforms that of individual bath particles in long-time ballistic motion.

Conclusions:

  • Intelligent hitchhiking is an effective strategy for resource-efficient navigation.
  • Reinforcement learning enables simple agents to exhibit complex adaptive behaviors.
  • The IHP model provides insights into collective motion and navigation in active matter systems.