CONTROLLED SENSING AND ANOMALY DETECTION VIA SOFT ACTOR-CRITIC REINFORCEMENT LEARNING

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

To address the anomaly detection problem in the presence of noisy observations and to tackle the tuning and efficient exploration challenges that arise in deep reinforcement learning algorithms, we in this paper propose a soft actor-critic deep reinforcement learning framework. To evaluate the proposed framework, we measure its performance in terms of detection accuracy, stopping time, and the total number of samples needed for detection. Via simulation results, we demonstrate the performance when soft actor-critic algorithms are employed, and identify the impact of key parameters, such as the sensing cost, on the performance. In all results, we further provide comparisons between the performances of the proposed soft actor-critic and conventional actor-critic algorithms.

Original languageEnglish (US)
Title of host publication2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4198-4202
Number of pages5
ISBN (Electronic)9781665405409
DOIs
StatePublished - 2022
Event47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore
Duration: May 23 2022May 27 2022

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2022-May
ISSN (Print)1520-6149

Conference

Conference47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Country/TerritorySingapore
CityVirtual, Online
Period5/23/225/27/22

Keywords

  • Anomaly detection
  • controlled sensing
  • reinforcement learning
  • soft actor-critic algorithm

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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