Short term vs. long term: optimization of microswimmer navigation on different time horizons

TL;DR

This paper uses reinforcement learning to optimize microswimmer navigation strategies in turbulent flows over different time horizons, revealing how local strain measurements and update frequency influence avoidance efficiency.

Contribution

It introduces a new theory explaining the impact of time-horizon and update frequency on microswimmer strategy optimization in turbulence.

Findings

Measuring local strain magnitude enables effective avoidance without directional cues.

Sign of local strain gradients improves avoidance efficiency.

Optimal strategies depend on the turbulence correlation time and update frequency.

Abstract

We use reinforcement learning to find strategies that allow microswimmers in turbulence to avoid regions of large strain. This question is motivated by the hypothesis that swimming microorganisms tend to avoid such regions to minimise the risk of predation. We ask which local cues a microswimmer must measure to efficiently avoid such straining regions. We find that it can succeed without directional information, merely by measuring the magnitude of the local strain. However, the swimmer avoids straining regions more efficiently if it can measure the sign of local strain gradients. We compare our results with those of an earlier study [Mousavi {\em et al.} Phys. Rev. Res. {\bf 6}, L022034 (2024)] where a short-time expansion was used to find optimal strategies. We find that the short-time strategies work well in some cases but not in others. We derive a new theory that explains when the time-horizon matters for our optimisation problem, and when it does not. We find the strategy with best performance when the time-horizon coincides with the correlation time of the turbulent fluctuations. We also explain how the update frequency (the frequency at which the swimmer updates its strategy) affects the found strategies. We find that higher update frequencies yield better performance.