Double Q-Learning for Radiation Source Detection

Sensors
Zheng Liu, Shiva Abbaszadeh

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.

References

Feb 27, 2015·Nature·Volodymyr MnihDemis Hassabis

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Citations

Jan 24, 2020·PloS One·Gregory R RomanchekShiva Abbaszadeh
Jan 24, 2021·Journal of Chromatography. a·Alexander KensertDeirdre Cabooter

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