Can flocking aid the path planning of microswimmers in turbulent flows?

We show that the flocking of microswimmers in a turbulent flow can enhance the efficacy of reinforcement-learning-based path planning of microswimmers in turbulent flows. In particular, we develop a machine-learning strategy that incorporates Vicsek-model-type flocking in microswimmer assemblies in a statistically homogeneous and isotropic turbulent flow in two dimensions. We build on the adversarial-reinforcement-learning of Alageshan et al. [“Machine learning strategies for path-planning microswimmers in turbulent flows,” Phys. Rev. E 101, 043110 (2020)] for non-interacting microswimmers in turbulent flows. Such microswimmers aim to move optimally from an initial position to a target. We demonstrate that our flocking-aided version of the adversarial-reinforcement-learning strategy of Ref. 1 can be superior to earlier microswimmer path-planning strategies.