  We propose a systematic method based on reinforcement learning (RL)
techniques to find the optimal path that can minimize the total entropy
production between two equilibrium states of open systems at the same
temperature in a given fixed time period. Benefited from the generalization of
the deep RL techniques, our method can provide a powerful tool to address this
problem in quantum systems even with two-dimensional continuous controllable
parameters. We successfully apply our method on the classical and quantum
two-level systems.
