The article presents a new method for solving complex optimization problems with two levels of objectives. By using a two-timescale stochastic algorithm, the researchers were able to find optimal solutions efficiently. They showed that this method can be applied to actor-critic algorithms, which are used in reinforcement learning, and achieve significant improvements in convergence rates compared to existing approaches.