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Deep Reinforcement Learning for Cavity Filter Tuning
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
2018 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
2018. , p. 44
Series
UPTEC F, ISSN 1401-5757 ; 18038
Keywords [en]
Reinforcement Learning, Cavity Filter, Deep Learning, Machine Learning, Automation
National Category
Engineering and Technology Robotics
Identifiers
URN: urn:nbn:se:uu:diva-354815OAI: oai:DiVA.org:uu-354815DiVA, id: diva2:1222744
External cooperation
Ericsson
Educational program
Master Programme in Engineering Physics
Supervisors
Examiners
Available from: 2018-06-26 Created: 2018-06-22 Last updated: 2018-06-26Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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Output format
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