Smart navigation of microswimmers in Poiseuille flow via reinforcement learning

Artificial microswimmers, such as active colloids, have the potential to revolutionize targeted drug delivery, but controlling their motion under imposed flow conditions remains challenging. In this work, we implement reinforcement learning (RL) to control the navigation of a microswimmer in a plane Poiseuille flow, with applications in targeted drug delivery. With RL, the swimmer learns to efficiently reach its target by continuously adjusting its swinging or tumbling behavior depending upon its self-propulsion strength, chirality and the imposed flow strength. This RL-based approach enables precise control of the particle's path, achieving reliable targeting even in stringent scenarios such as upstream motion in high bulk flow, thus advancing the design of intelligent in vivo medical microrobots.