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Double Q-Learning for Radiation Source Detection.

Zheng Liu1, Shiva Abbaszadeh2

  • 1Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois at Urbana-Champaign, 104 S Wright St, Urbana, IL 61801, USA. zliu86@illinois.edu.

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This summary is machine-generated.

This study introduces a reinforcement learning approach for autonomous radiation source detection in urban settings. The developed algorithm significantly reduces search time, improving efficiency in locating anomalous radiation.

Keywords:
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Area of Science:

  • Nuclear detection and instrumentation
  • Artificial intelligence in environmental monitoring
  • Robotics and autonomous systems

Background:

  • Urban radiation detection is complex due to background noise.
  • Traditional radiation surveys use fixed paths or basic data-driven methods.
  • Accurate and rapid source localization is crucial for safety and security.

Purpose of the Study:

  • To develop an autonomous, data-driven approach for anomalous radiation source detection.
  • To enhance the speed and accuracy of radiation surveys in urban environments.
  • To apply reinforcement learning for intelligent radiation source localization.

Main Methods:

  • Construction of a simulated urban radiation environment.
  • Implementation of a convolutional neural network-based double Q-learning algorithm.
  • Testing the algorithm's performance in navigating and detecting radiation sources autonomously.

Main Results:

  • The double Q-learning algorithm demonstrated reliable navigation of radiation detectors.
  • Search time was reduced by at least 44% compared to uniform and gradient search methods.
  • The approach enables radiation detection tasks with no human intervention.

Conclusions:

  • Reinforcement learning offers an effective solution for autonomous radiation source detection.
  • The proposed method significantly improves efficiency and reduces search time in complex environments.
  • This technology has potential applications in public safety and nuclear security.