Reinforcement learning based group testing

Abstract

A system performs group testing on a population of items. The group testing identifies items satisfying particular criteria from a population of items, for example, defective items from the population. The group testing may be performed for software or hardware testing, for testing a human population, for training of deep learning applications, and so on. The system trains a machine learning based model, for example, a reinforcement learning based model to evaluate groups. The model may further determine system dynamics that may represent priors of items. An agent treats the population and groups of items being tested as the environment and performs actions, for example, adjusting the groups. The system also performs a non-adaptive strategy based on monte carlo simulation of tests based on a simulation results.