Double Q-Learning for Radiation Source Detection

Clicks: 128
ID: 73763
2019
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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.
Reference Key
liu2019doublesensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Liu, Zheng;Abbaszadeh, Shiva;
Journal sensors
Year 2019
DOI
DOI not found
URL
https://www.mdpi.com/1424-8220/19/4/960
Keywords
chemistry Biology (General) Chemical technology Engineering (General). Civil engineering (General) Information technology Technology Science physics special aspects of education theory and practice of education

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