  Synthetic microswimmers show great promise in biomedical applications such as
drug delivery and microsurgery. Their locomotion, however, is subject to
stringent constraints due to the dominance of viscous over inertial forces at
low Reynolds number (Re) in the microscopic world. Furthermore, locomotory
gaits designed for one medium may become ineffective in a different medium.
Successful biomedical applications of synthetic microswimmers rely on their
ability to traverse biological environments with vastly different properties.
Here we leverage the prowess of machine learning to present an alternative
approach to designing low Re swimmers. Instead of specifying any locomotory
gaits \textit{a priori}, here a swimmer develops its own propulsion strategy
based on its interactions with the surrounding medium via reinforcement
learning. This self-learning capability enables the swimmer to modify its
propulsion strategy in response to different environments. We illustrate this
new approach using a minimal example that integrates a standard reinforcement
learning algorithm ($Q$-learning) into the locomotion of a swimmer consisting
of an assembly of spheres connected by extensible rods. We showcase
theoretically that this first self-learning swimmer can recover a previously
known propulsion strategy without prior knowledge in low Re locomotion,
identify more effective locomotory gaits when the number of spheres increases,
and adapt its locomotory gaits in different media. These results represent
initial steps towards the design of a new class of self-learning, adaptive (or
"smart") swimmers with robust locomotive capabilities to traverse complex
biological environments.
