Optimal Control tools to minimize dispersion in turbulent flows

We develop optimal and quasi-optimal strategies to control Lagrangian objects navigating in 3d turbulent flows. We consider the problem of minimizing the dispersion rate of a couple of autonomous explorers moving into the complex fluid environment. Starting from the optimal solutions derived in control theory, we find approximated solutions that could be applied also under less restrictive conditions as, e.g., in the presence of partial observability. We are going to compare hard-wired policies resulting from different approximated solutions of the optimal control theory against strategies obtained by data-driven tools based on Reinforcement Learning.