  By imitating biological microswimmers, microrobots can be designed to
accomplish targeted delivery of cargos and biomedical manipulations at
microscale. However, it is still a great challenge to enable microrobots to
maneuver in a complex environment. Machine learning algorithms offer a tool to
boost mobility and flexibility of a synthetic microswimmer, hence could help us
design truly smart microrobots. In this work, we investigate how a model of sea
urchin sperm cell can self-learn chemotactic motion in a chemoattractant
concentration field. We employ an artificial neural network to act as a
decision-making agent and facilitate the sperm cell to discover efficient
maneuver strategies through a deep reinforcement learning (DRL) algorithm. Our
results show that chemotactic behaviours, very similar to the realistic ones,
can be achieved by the DRL utilizing only limited environmental information. In
most cases, the DRL algorithm discovers more efficient strategies than the
human-devised one. Furthermore, the DRL can even utilize an external
disturbance to facilitate the chemotactic motion if the extra flow information
is also taken into account by the artificial neural network. Our results
provide insights to the chemotactic process of sea urchin sperm cells and also
prepare guidance for the intelligent maneuver of microrobots.
