Optimal active particle navigation meets machine learning

TL;DR

This paper reviews recent advances in optimal navigation strategies for active agents, emphasizing machine learning approaches that enable efficient navigation in complex, high-dimensional, or unknown environments.

Contribution

It provides an overview of developments from micro- to macroscale and highlights the role of machine learning in solving complex navigation problems.

Findings

Machine learning uncovers highly efficient navigation strategies.

Learning-based methods handle chaotic and high-dimensional environments.

The article discusses challenges and future directions in the field.

Abstract

The question of how "smart" active agents, like insects, microorganisms, or future colloidal robots need to steer to optimally reach or discover a target, such as an odor source, food, or a cancer cell in a complex environment has recently attracted great interest. Here, we provide an overview of recent developments, regarding such optimal navigation problems, from the micro- to the macroscale, and give a perspective by discussing some of the challenges which are ahead of us. Besides exemplifying an elementary approach to optimal navigation problems, the article focuses on works utilizing machine learning-based methods. Such learning-based approaches can uncover highly efficient navigation strategies even for problems that involve e.g. chaotic, high-dimensional, or unknown environments and are hardly solvable based on conventional analytical or simulation methods.