Double Q-Learning for Radiation Source Detection

• Main Authors: Zheng Liu, Shiva Abbaszadeh
• Format: Article
• Language: English
• Published: MDPI AG 2019-02-01
• Series: Sensors
• Subjects: reinforcement learning; radiation detection; source searching
• Online Access: https://www.mdpi.com/1424-8220/19/4/960

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.