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, requires 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 nondemanding approach to the control of nonequilibrium quantum thermodynamics. We successfully apply our methods to the case of single- and two-particle systems subjected to time-dependent driving potentials.