Double Q-Learning for Radiation Source Detection
Abstract
Anomalous radiation source detection in urban environments is challenging due to the complex nature of background radiation. When a suspicious area is determined, a radiation survey is usually carried out to search for anomalous radiation sources. To locate the source with high accuracy and in a short time, different survey approaches have been studied such as scanning the area with fixed survey paths and data-driven approaches that update the survey path on the fly with newly acquired measurements. In this work, we propose reinforcement learning as a data-driven approach to conduct radiation detection tasks with no human intervention. A simulated radiation environment is constructed, and a convolutional neural network-based double Q-learning algorithm is built and tested for radiation source detection tasks. Simulation results show that the double Q-learning algorithm can reliably navigate the detector and reduce the searching time by at least 44% compared with traditional uniform search methods and gradient search methods.
- Publication Date:
- Research Org.:
- North Carolina State University, Raleigh, NC (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA); Defense Threat Reduction Agency (DTRA)
- OSTI Identifier:
- 1496491
- Alternate Identifier(s):
- OSTI ID: 1614545
- Grant/Contract Number:
- NA0002576; HDTRA 1-14-1-0011
- Resource Type:
- Published Article
- Journal Name:
- Sensors
- Additional Journal Information:
- Journal Name: Sensors Journal Volume: 19 Journal Issue: 4; Journal ID: ISSN 1424-8220
- Publisher:
- MDPI AG
- Country of Publication:
- Switzerland
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 36 MATERIALS SCIENCE; 47 OTHER INSTRUMENTATION; chemistry; engineering; instruments & instrumentation; reinforcement learning; radiation detection; source searching
Citation Formats
Liu, Zheng, and Abbaszadeh, Shiva. Double Q-Learning for Radiation Source Detection. Switzerland: N. p., 2019.
Web. doi:10.3390/s19040960.
Liu, Zheng, & Abbaszadeh, Shiva. Double Q-Learning for Radiation Source Detection. Switzerland. https://doi.org/10.3390/s19040960
Liu, Zheng, and Abbaszadeh, Shiva. Sun .
"Double Q-Learning for Radiation Source Detection". Switzerland. https://doi.org/10.3390/s19040960.
@article{osti_1496491,
title = {Double Q-Learning for Radiation Source Detection},
author = {Liu, Zheng and Abbaszadeh, Shiva},
abstractNote = {Anomalous radiation source detection in urban environments is challenging due to the complex nature of background radiation. When a suspicious area is determined, a radiation survey is usually carried out to search for anomalous radiation sources. To locate the source with high accuracy and in a short time, different survey approaches have been studied such as scanning the area with fixed survey paths and data-driven approaches that update the survey path on the fly with newly acquired measurements. In this work, we propose reinforcement learning as a data-driven approach to conduct radiation detection tasks with no human intervention. A simulated radiation environment is constructed, and a convolutional neural network-based double Q-learning algorithm is built and tested for radiation source detection tasks. Simulation results show that the double Q-learning algorithm can reliably navigate the detector and reduce the searching time by at least 44% compared with traditional uniform search methods and gradient search methods.},
doi = {10.3390/s19040960},
journal = {Sensors},
number = 4,
volume = 19,
place = {Switzerland},
year = {Sun Feb 24 00:00:00 EST 2019},
month = {Sun Feb 24 00:00:00 EST 2019}
}
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Cited by: 26 works
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