  We present a numerical study of training a self-propelling agent to migrate
in the unsteady flow environment. We control the agent to utilize the
background flow structure by adopting the reinforcement learning algorithm to
minimize energy consumption. We considered the agent migrating in two types of
flows: one is simple periodical double-gyre flow as a proof-of-concept example,
while the other is complex turbulent Rayleigh-B\'enard convection as a paradigm
for migrating in the convective atmosphere or the ocean. The results show that
the smart agent in both flows can learn to migrate from one position to another
while utilizing background flow currents as much as possible to minimize the
energy consumption, which is evident by comparing the smart agent with a naive
agent that moves straight from the origin to the destination. In addition, we
found that compared to the double-gyre flow, the flow field in the turbulent
Rayleigh-B\'enard convection exhibits more substantial fluctuations, and the
training agent is more likely to explore different migration strategies; thus,
the training process is more difficult to converge. Nevertheless, we can still
identify an energy-efficient trajectory that corresponds to the strategy with
the highest reward received by the agent. These results have important
implications for many migration problems such as unmanned aerial vehicles
flying in a turbulent convective environment, where planning energy-efficient
trajectories are often involved.
