  Swimming organisms can escape their predators by creating and harnessing
unsteady flow fields through their body motions. Stochastic optimization and
flow simulations have identified escape patterns that are consistent with those
observed in natural larval swimmers. However, these patterns have been limited
by the specification of a particular cost function and depend on a prescribed
functional form of the body motion. Here, we deploy reinforcement learning to
discover swimmer escape patterns for larval fish under energy constraints. The
identified patterns include the C-start mechanism, in addition to more
energetically efficient escapes. We find that maximizing distance with limited
energy requires swimming via short bursts of accelerating motion interlinked
with phases of gliding. The present, data efficient, reinforcement learning
algorithm results in an array of patterns that reveal practical flow
optimization principles for efficient swimming and the methodology can be
transferred to the control of aquatic robotic devices operating under energy
constraints.
