  In this study, we reexamine a recent optimal control simulation targeting the
preparation of a superposition of two excited electronic states in the UV range
in a complex molecular system. We revisit this control from the perspective of
reinforcement learning, offering an efficient alternative to conventional
quantum control methods. The two excited states are addressable by orthogonal
polarizations and their superposition corresponds to a right or left
localization of the electronic density. The pulse duration spans tens of
femtoseconds to prevent excitation of higher excited bright states what leads
to a strong perturbation by the nuclear motions. We modify an open source
software by L. Giannelli et al., Phys. Lett. A, 434, 128054 (2022) that
implements reinforcement learning with Lindblad dynamics, to introduce
non-Markovianity of the surrounding either by timedependent rates or more
exactly by using the hierarchical equations of motion with the QuTiP-BoFiN
package. This extension opens the way to wider applications for non-Markovian
environments, in particular when the active system interacts with a highly
structured noise.
