Automated Hyperparameter Optimization Using Reinforcement Learning for Scalable Deep Learning Models

Abstract

Hyperparameter optimization assumes a cardinal value like a hyperlink in the implementation of deep learning models. One of the tasks that reinforce these models is the hyperparameters fine-tuning process; learning rate, batch size, and model architecture parameters are thereby of utmost importance for reaching peak performance. However, hyperparameter optimization is still a difficult and time-consuming task even with the wide array of remedies at the disposal. This paper tackles the essential issues entangled into the process of hyperparameter optimization, including the computational burden, high-dimensional search spaces, overfitting risks, and the lack of cross-dataset generalization. Additionally, emerging trends such as meta-learning and neural architecture search developments provide new ways of improving efficiency and scalability of optimization processes. The study demonstrates the need for better methods of managing the contradiction between performance, efficiency, and generalization, thus, creating a more effective way to use deep learning models.