Reinforcement Learning with Imitation for Cavity Filter Tuning

Cavity filters are vital components of radio base stations and networks. After production, they need tuning, which has proven to be a difficult process to do manually and even more so to automate. Previous attempts to automate this process with Reinforcement Learning have failed to reach consistent performance on anything but the simplest filter models. In this paper, we build upon these results and aim to improve them. Multiple methods are tested and evaluated, including introducing a pre-processing step, tuning hyperparameters and dividing the problem into multiple sub-tasks. In particular, by using supervised Imitation Learning as an initial phase, a semi-realistic filter model with 13 tuning screws is tuned, fulfilling both insertion loss and return loss requirements. On this problem, this algorithm has a greater efficiency than any previously published results on Reinforcement Learning for Cavity filter tuning.