  Many microorganisms are capable of swimming through viscous fluids such as
water in order to search for nutrients, swim toward oxygen or light, or to
escape from predators. To navigate their environment they often perform large
nonreciprocal periodic deformations of their shape, by waving appendages such
as cilia or flagella, or by deforming their entire body. Even unicellular
organisms are fundamentally made of parts, which need to be cooperatively
utilized to allow these creatures to navigate their environment, without using
a centralized control mechanism. Here, we investigate the physical implications
of decentralized decision-making of the actuators of a generalized N-bead
Najafi Golestanian microswimmer, self-propelling via coordinated non-reciprocal
swimming strokes. We treat each bead as an artificial neural network-based
agent that perceives information about its neighbors and whose actions induce
strokes of its adjacent arms. With neuroevolution techniques, we evolve optimal
policies for the single-bead decision centers such that the N-bead collective
efficiently self-propels as an individual, allowing us to investigate optimal
locomotion policies for increasingly large microswimmer bodies. We demonstrate
that such decentralized policies are robust and tolerant concerning
morphological changes or defects and facilitate cargo transport or drug
delivery applications "out of the box", without further optimization. Our
approach allows us to train large swimmers ($N=100$ and more), and we show that
long-wavelength solutions lead to surprisingly efficient swimming gaits. Our
work is of relevance to understand robust locomotion of biological
microswimmers, to develop robust artificial microswimmer navigation strategies,
and, in a broader conceptional context, for Artificial Life< and in general
emergent levels of individuality.
