The ideas presented in this work can be applied to other control problems beyond the LQR, such as model-free reinforcement learning for general nonlinear systems. By extending the covariance-based policy parameterization and DeePO method to nonlinear control problems, it is possible to learn optimal control policies for a wide range of nonlinear dynamical systems. This extension may involve using more advanced function approximators, such as deep neural networks, to represent the policy in a nonlinear form. The adaptive learning framework can be tailored to handle the complexities of nonlinear dynamics and optimize control policies based on data-driven approaches. Overall, the concepts of data-enabled policy optimization and direct adaptive learning can be generalized to various control problems, offering a versatile and efficient approach to model-free control in nonlinear systems.