In this Master's thesis the option of using deep reinforcement learning for cavity filter tuning has been explored. Several reinforcement learning algorithms have been explained and discussed, and then the deep deterministic policy gradient algorithm has been used to solve a simulated filter tuning problem. Both the filter environment and the reinforcement learning agent were implemented, with the filter environment making use of existing circuit models. The reinforcement learning agent learned how to tune filters with four poles and one transmission zero, or eight tune-able screws in total. A comparison was also made between constant exploration noise and exploration noise decaying over time, together with different maximum lengths of the episodes. For the particular noise used here, decaying exploration noise was shown to be better than constant, and a maximum length of 100 steps was shown to be better than 200 for the 8 screw filter.


    Access

    Download


    Export, share and cite



    Title :

    Deep Reinforcement Learning for Cavity Filter Tuning


    Contributors:

    Publication date :

    2018-01-01


    Type of media :

    Theses


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    006 / 629



    Deep reinforcement learning based parameter self-tuning control strategy for VSG

    Xiong, Kang / Hu, Weihao / Zhang, Guozhou et al. | BASE | 2022

    Free access

    Deep Reinforcement Learning

    Huang, Xiaowei / Jin, Gaojie / Ruan, Wenjie | Springer Verlag | 2012



    Pound Cavity Tuning

    Jois, Shriram / Sullivan, N. S. / Tanner, D. B. | British Library Conference Proceedings | 2018