Q-Learning Empowered Cavity Filter Tuning with Epsilon Decay Strategy

Abstract

In the ever-evolving landscape of engineering and technology, the optimization of complex systems is a perennial challenge. Cavity filters, pivotal in Radio Frequency (RF) systems, demand precise tuning for optimal performance. This article introduces an innovative approach to automate cavity filter tuning using Q-learning, enhanced with epsilon decay. While reinforcement learning algorithms like Q-learning have shown effectiveness in complex decision-making, the exploration-exploitation trade-off remains a crucial challenge. The study conducts a thorough investigation into the application of epsilon decay in conjunction with Q-learning, employing the well-established epsilon-greedy strategy. This research focuses on systematically decaying the exploration rate over time, aiming to strike a balance between exploring new actions and exploiting acquired knowledge. This strategic shift serves to not only refine the convergence of the Q-learning model but also remarkably elevate the overall tuning performances. Impressively, this optimization is achieved with a notable reduction in the number of tuning steps, demonstrating an efficiency boost of up to 45 steps.