Double Q-Learning for Radiation Source Detection
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 24, 2019
- Source ID
- 10.3390/s19040960
Entities
People
- Shiva Abbaszadeh
- Zheng Liu
Organizations
- Defense Threat Reduction Agency
- National Nuclear Security Administration