Reinforcement Learning-Based Augmentation of Data Collection for Bayesian Optimization Towards Radiation Survey and Source Localization

Safer and more efficient characterization of radioactive environments requires exploring intelligently, utilizing robotic systems which use smart strategies and physics-based statistical models. Bayesian Optimization (BO) provides one such statistical framework to explainably find the global maximum within noisy contexts while also minimizing the number of trials. For radiation survey and source location, the aid of such a machine learning algorithm could significantly cut down on time and health risks required for maintenance and emergency response scenarios. Maintaining the explainability while increasing the efficiency of the search has been found possible by including the high uncertainty data that is picked up while the agent is in transit. Now that the paths of transit matter to data acquisition they could be optimized as well. This paper introduces reinforcement learning (RL) to the BO search framework. The behavior of this RL additive is observed in simulation over three different datasets of real radiation data. It is shown that the RL additive can cause significant increases to the score of the maximum point discovered, but the computational time cost is increased by nearly 100% while the reconstructed radiation field root mean square error (RMSE) of the BO+RL algorithm matches BO performance within 1%.