Reinforcement learning approach to nonequilibrium quantum thermodynamics

We use a reinforcement learning approach to reduce entropy production in a closed quantum system brought
out of equilibrium. Our strategy makes use of an external control Hamiltonian and a policy gradient technique.
Our approach bears no dependence on the quantitative tool chosen to characterize the degree of thermodynamic
irreversibility induced by the dynamical process being considered, require little knowledge of the dynamics
itself and does not need the tracking of the quantum state of the system during the evolution, thus embodying
an experimentally non-demanding approach to the control of non-equilibrium quantum thermodynamics. We
successfully apply our methods to the case of single- and two-particle systems subjected to time-dependent
driving potentials.