# Machine learning strategies for path-planning microswimmers in turbulent flows

arXiv: https://arxiv.org/abs/1910.01728 · HTML: https://mlsanity.com/paper/1910.01728/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2019.

## Built on by (most cited first, 14 of 14)

- [Reinforcement learning for pursuit and evasion of microswimmers at low Reynolds number](https://mlsanity.com/paper/2106.08609/) (2021)
- [Reinforcement Learning for Active Matter](https://mlsanity.com/paper/2503.23308/) (2025)
- [Taming Lagrangian Chaos with Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2212.09612/) (2022)
- [Training microrobots to swim by a large language model](https://mlsanity.com/paper/2402.00044/) (2024)
- [Physics-Guided Actor-Critic Reinforcement Learning for Swimming in Turbulence](https://mlsanity.com/paper/2406.10242/) (2024)
- [Deep-learning-powered data analysis in plankton ecology](https://mlsanity.com/paper/2309.08500/) (2023)
- [Optimal navigation of magnetic artificial microswimmers in blood capillaries with deep reinforcement learning](https://mlsanity.com/paper/2404.02171/) (2024)
- [Navigation of a Three-Link Microswimmer via Deep Reinforcement Learning](https://mlsanity.com/paper/2506.00084/) (2025)
- [Chemoreception and chemotaxis of a three-sphere swimmer](https://mlsanity.com/paper/2205.02678/) (2022)
- [Mixing Artificial and Natural Intelligence: From Statistical Mechanics to AI and Back to Turbulence](https://mlsanity.com/paper/2403.17993/) (2024)
- [A critical assessment of reinforcement learning methods for microswimmer navigation in complex flows](https://mlsanity.com/paper/2505.05525/) (2025)
- [Smart navigation of a gravity-driven glider with adjustable centre-of-mass](https://mlsanity.com/paper/2510.09250/) (2025)
- [Active Electrosensing and Communication in MARL-trained Weakly Electric Fish Collectives](https://mlsanity.com/paper/2511.08436/) (2025)
- [Multi-agent rendezvous in fluid flows via reinforcement learning](https://mlsanity.com/paper/2606.11274/) (2026)

## Abstract (arXiv)

We develop an adversarial-reinforcement learning scheme for microswimmers in statistically homogeneous and isotropic turbulent fluid flows, in both two (2D) and three dimensions (3D). We show that this scheme allows microswimmers to find non-trivial paths, which enable them to reach a target on average in less time than a naive microswimmer, which tries, at any instant of time and at a given position in space, to swim in the direction of the target. We use pseudospectral direct numerical simulations (DNSs) of the 2D and 3D (incompressible) Navier-Stokes equations to obtain the turbulent flows. We then introduce passive microswimmers that try to swim along a given direction in these flows; the microswimmers do not affect the flow, but they are advected by it.