  We deploy a combination of reinforcement learning-based approaches and more
traditional optimization techniques to identify optimal protocols for
population transfer in a multi-level system. We constraint our strategy to the
case of fixed coupling rates but time-varying detunings, a situation that would
simplify considerably the implementation of population transfer in relevant
experimental platforms, such as semiconducting and superconducting ones. Our
approach is able to explore the space of possible control protocols to reveal
the existence of efficient protocols that, remarkably, differ from (and can be
superior to) standard Raman, STIRAP or other adiabatic schemes. The new
protocols that we identify are robust against both energy losses and dephasing.
