  Turbulent diffusion causes particles placed in proximity to separate. We
investigate the required swimming efforts to maintain a particle close to its
passively advected counterpart. We explore optimally balancing these efforts
with the intended goal by developing and comparing a novel Physics-Informed
Reinforcement Learning (PIRL) strategy with prescribed control (PC) and
standard physics-agnostic Reinforcement Learning strategies. Our PIRL scheme,
coined the Actor-Physicist, is an adaptation of the Actor-Critic algorithm in
which the Neural Network parameterized Critic is replaced with an analytically
derived physical heuristic function (the physicist). This strategy is then
compared with an analytically computed optimal PC policy derived from a
stochastic optimal control formulation and standard physics-agnostic
Actor-Critic type algorithms.
