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Double Q-Learning for Radiation Source Detection

Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois at Urbana-Champaign, 104 S Wright St, Urbana, IL 61801, USA
Author to whom correspondence should be addressed.
Sensors 2019, 19(4), 960;
Received: 17 January 2019 / Revised: 19 February 2019 / Accepted: 22 February 2019 / Published: 24 February 2019
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. View Full-Text
Keywords: reinforcement learning; radiation detection; source searching reinforcement learning; radiation detection; source searching
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MDPI and ACS Style

Liu, Z.; Abbaszadeh, S. Double Q-Learning for Radiation Source Detection. Sensors 2019, 19, 960.

AMA Style

Liu Z, Abbaszadeh S. Double Q-Learning for Radiation Source Detection. Sensors. 2019; 19(4):960.

Chicago/Turabian Style

Liu, Zheng, and Shiva Abbaszadeh. 2019. "Double Q-Learning for Radiation Source Detection" Sensors 19, no. 4: 960.

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