  Particular types of plankton in aquatic ecosystems can coordinate their
motion depending on the local flow environment to reach regions conducive to
their growth or reproduction. Investigating their swimming strategies with
regard to the local environment is important to obtain in-depth understanding
of their behavior in the aquatic environment. In the present research, to
examine an impact of the shape and gravity on a swimming strategy, plankton is
considered as settling swimming particles of ellipsoidal shape. The Q-learning
approach is adopted to obtain swimming strategies for smart particles with a
goal of efficiently moving upwards in a two-dimensional steady flow. Strategies
obtained from reinforcement learning are compared to those of naive gyrotactic
particles that is modeled considering the behavior of realistic plankton. It is
found that elongation of particles improves the performance of upward swimming
by facilitating particles' resistance to the perturbation of vortex. In the
case when the settling velocity is included, the strategy obtained by
reinforcement learning has similar performance to that of the naive gyrotactic
one, and they both align swimmers in upward direction. The similarity between
the strategy obtained from machine learning and the biological gyrotactic
strategy indicates the relationship between the aspherical shape and settling
effect of realistic plankton and their gyrotactic feature.
