  Marine microorganisms must cope with complex flow patterns and even
turbulence as they navigate the ocean. To survive they must avoid predation and
find efficient energy sources. A major difficulty in analysing possible
survival strategies is that the time series of environmental cues in non-linear
flow is complex, and that it depends on the decisions taken by the organism.
One way of determining and evaluating optimal strategies is reinforcement
learning. In a proof-of-principle study, Colabrese et al. [Phys. Rev. Lett.
(2017)] used this method to find out how a micro-swimmer in a vortex flow can
navigate towards the surface as quickly as possible, given a fixed swimming
speed. The swimmer measured its instantaneous swimming direction and the local
flow vorticity in the laboratory frame, and reacted to these cues by swimming
either left, right, up, or down. However, usually a motile microorganism
measures the local flow rather than global information, and it can only react
in relation to the local flow, because in general it cannot access global
information (such as up or down in the laboratory frame). Here we analyse
optimal strategies with local signals and actions that do not refer to the
laboratory frame. We demonstrate that symmetry-breaking is required in order to
learn vertical migration in a meaningful way. Using reinforcement learning we
analyse the emerging strategies for different sets of environmental cues that
microorganisms are known to measure.
