Double Q-Learning for Radiation Source Detection

Zheng Liu, Shiva Abbaszadeh

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish (US)
Article number960
Number of pages19
Issue number4
StatePublished - Feb 2 2019


  • Radiation detection
  • Reinforcement learning
  • Source searching

ASJC Scopus subject areas

  • Analytical Chemistry
  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering
  • Biochemistry


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