Reinforcement learning of reconfigurable microswimmers

The recent surge in applying artificial intelligence (AI) to fluid mechanics and propulsion problem has demonstrated the tremendous potential of AI in advancing solutions that are usually difficult to achieve with the traditional frameworks. In this talk, we will summarize our recent research in the reinforcement learning (RL) of reconfigurable microswimmers in a low Reynolds number fluid. We will discuss a set of simple models of reconfigurable microswimmers consisting of spheres with movable arms. We will introduce two RL approaches for a fully discrete reconfiguration system and a system with continuous variables, respectively. We will showcase how RL enables these swimmers to self-learn how to swim, rotate, and navigate towards specific targets. These findings present the initial step towards intelligent autonomous manipulation of artificial microswimmers.