# Optimal active particle navigation meets machine learning

arXiv: https://arxiv.org/abs/2303.05558 · HTML: https://mlsanity.com/paper/2303.05558/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 9 Mar 2023.

## Builds on (most cited first, 2 of 2)

- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Deep R-Learning for Continual Area Sweeping](https://mlsanity.com/paper/2006.00589/) (2020)

## Built on by (most cited first, 3 of 3)

- [Reinforcement Learning for Active Matter](https://mlsanity.com/paper/2503.23308/) (2025)
- [SwarmRL: Building the Future of Smart Active Systems](https://mlsanity.com/paper/2404.16388/) (2024)
- [Learning to reset in target search problems](https://mlsanity.com/paper/2503.11330/) (2025)

## Abstract (arXiv)

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.