Cooperative swimming at low Reynolds numbers using deep reinforcement learning

Biological microswimmers perform cooperative behaviors to exploit their fluid environments, leading to enhanced swimming performance and/or energy efficiency. In this study, we employed a deep reinforcement learning approach to investigate the effective strategy for cooperative locomotion of reconfigurable swimmers. We consider a pair of 3-sphere swimmers arranged in a collinear configuration in a low Re fluid. The strategy obtained by the reinforcement learning approach consists of an approach stage and a synchronization stage. During the approach stage, the swimmers approach each other to reduce their relative distance and increase their hydrodynamic interactions. Subsequently, in the synchronization stage, the swimmers synchronize their locomotory gaits with a particular phase mismatch, resulting in locomotion performance that significantly surpasses an individual swimmer. This research highlights the potential of reinforcement learning in the control of cooperative behaviors of multiple swimmers.