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

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 minimize 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 [N. Mousavi et al., Phys. Rev. Res. 6, L022034 (2024)2643-156410.1103/PhysRevResearch.6.L022034] 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 theory that explains when the time horizon matters for our optimization 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.