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Bayesian optimization of distributed neurodynamical controller models for spatial navigation.

Armin Hadzic1, Grace M Hwang1,2, Kechen Zhang3

  • 1The Johns Hopkins University Applied Physics Laboratory, Laurel, 20723, MD, USA.

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|October 10, 2022
PubMed
Summary
This summary is machine-generated.

We developed a Bayesian optimization framework to efficiently tune complex multi-agent swarm controllers, inspired by neural networks. This method accelerates the application of neuroscientific theories to real-world autonomous systems.

Keywords:
Bayesian optimizationDynamical systems modelsMulti-agent controlSpatial navigationSwarmingUMAP

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Area of Science:

  • Robotics
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Dynamical systems models advance decentralized navigation in multi-agent swarms.
  • The NeuroSwarms controller, analogous to hippocampal place-cell circuits, presents complex dynamics.
  • Linear analyses are insufficient for these complex swarm models, and traditional tuning methods are inadequate.

Purpose of the Study:

  • To present a Bayesian optimization framework for tuning dynamical controller models in autonomous multi-agent systems.
  • To enable adaptive and efficient exploration of complex parameter spaces.
  • To accelerate the translation of neuroscientific theory into applied domains.

Main Methods:

  • Utilized Bayesian optimization with Gaussian process surrogate models.
  • Defined a task-dependent objective function for cooperative localization and reward capture.
  • Evaluated performance across multiple maze environments with varying geometries.

Main Results:

  • The framework efficiently explores the parameter space of dynamical swarm controllers.
  • Demonstrated successful cooperative localization and reward capture under time constraints.
  • Validated search performance through high-dimensional clustering and trajectory visualization.

Conclusions:

  • Bayesian optimization offers a sample-efficient method for tuning complex dynamical swarm controllers.
  • This approach facilitates the application of neuroscientific principles to autonomous systems.
  • Adaptive evaluation accelerates the development of self-organizing behavioral capacities in complex systems.