Deep Reinforcement Learning for Cavity Filter Tuning
Deep Reinforcement Learning for Cavity Filter Tuning
- Neue Suche nach: Larsson, Hannes
- Neue Suche nach: Larsson, Hannes
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.
Dokumentinformationen
Deep Reinforcement Learning for Cavity Filter Tuning
2018-01-01
Hochschulschrift
Elektronische Ressource
Englisch
Ähnliche Titel
Deep reinforcement learning based parameter self-tuning control strategy for VSG
| BASE | 2022
| Springer Verlag | 2012
| BASE | 2019
| British Library Conference Proceedings | 2018