The adaptive combination of model-based and model-free components in the gray-box controller can be further improved and generalized by considering the following strategies:
Dynamic Weighting: Instead of using fixed combination coefficients, dynamic weighting schemes can be employed to adjust the influence of model-based and model-free components based on the quality of sensitivity estimation and problem characteristics. Adaptive algorithms can dynamically tune the coefficients during the optimization process to optimize performance.
Ensemble Methods: Utilizing ensemble methods that combine multiple models or estimates can enhance the robustness and accuracy of the sensitivity estimation. By integrating diverse sources of information and estimates, the gray-box controller can make more informed decisions and adapt to a wider range of problem characteristics.
Meta-Learning: Incorporating meta-learning techniques can enable the gray-box controller to learn the optimal combination strategy for different problem settings and sensitivity estimation qualities. By leveraging meta-learning algorithms, the controller can adapt its behavior based on past experiences and performance feedback.
Transfer Learning: Leveraging transfer learning approaches can allow the gray-box controller to generalize its adaptive combination strategy across different systems and problem domains. By transferring knowledge and insights from one problem to another, the controller can improve its performance and efficiency in handling diverse sensitivity estimation qualities.
By implementing these advanced strategies and techniques, the adaptive combination of model-based and model-free components in the gray-box controller can be further enhanced to handle a wider range of sensitivity estimation qualities and problem characteristics, leading to improved optimization performance and robustness.