Double Q-Learning for Radiation Source Detection.

Zheng Liu,Shiva Abbaszadeh

doi:10.3390/s19040960

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.

Highlights

In the field of homeland security, detecting anomalous radiation sources in urban environments is an important yet challenging task due to the complexity of urban radiation background.When a suspicious area is determined, a radiation survey is usually carried out to search for anomalous radiation sources
This study proposes the reinforcement learning algorithm as a data-driven approach to navigate automated robots equipped with radiation detectors for radiation source detection tasks
30 randomly initialized episodes were performed with convolutional neural network (CNN) parameters fixed, and the average, minimum, and maximum reward in these episodes were recorded (Figure 7)

Summary

Introduction

In the field of homeland security, detecting anomalous radiation sources in urban environments is an important yet challenging task due to the complexity of urban radiation background.When a suspicious area is determined, a radiation survey is usually carried out to search for anomalous radiation sources. To deliver a comprehensive and efficient survey, different survey approaches have been studied such as manually scanning of the area by human operated detectors and automatically scanning the area with robots under the navigation of pre-defined survey paths [1,2]. The pre-defined survey path method does not require human efforts during survey, but it cannot use new information gained during the survey and is not flexible to adjust survey paths. To address those issues, this study proposes the reinforcement learning algorithm as a data-driven approach to navigate automated robots equipped with radiation detectors for radiation source detection tasks

Methods

Results

Conclusion