Reinforcement Learning for Adaptive Illumination with X-rays

Abstract

We propose a learning algorithm for automating image sampling in scientific applications. We consider settings where images are sampled by controlling a probe beam's scanning trajectory over the image surface. We explore alternatives to obtaining images by the standard rastering method. We formulate the scanner control problem as a reinforcement learning (RL) problem and train a policy to adaptively sample only the highest value regions of the image, choosing the acquisition time and resolution for each sample position based on an observation of previous readings. We use convolutional neural network (CNN) policies to control the scanner as a way to generalize our approach to larger samples. We show simulation results for a simple policy on both synthetic data and real world data from an archaeological application.