Machine learning strategies for path-planning microswimmers in turbulent flows

TL;DR

This paper introduces an adversarial-reinforcement learning approach enabling microswimmers to navigate turbulent flows more efficiently, outperforming naive strategies by finding optimized paths in complex fluid environments.

Contribution

The study develops a novel adversarial-reinforcement learning scheme for microswimmers in turbulent flows, demonstrating improved navigation strategies in 2D and 3D simulations.

Findings

Microswimmers reach targets faster than naive strategies.

The RL scheme finds non-trivial, efficient paths in turbulence.

Effective in both 2D and 3D turbulent flows.

Abstract

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.