Training data generation via reinforcement learning fault-injection

Abstract

Systems/techniques for generating training data via reinforcement learning fault-injection are provided. A system can access a computing application. In various aspects, the system can train one or more machine learning models based on responses of the computing application to iterative fault-injections determined via reinforcement learning. More specifically, the system can: inject a first fault into the computing application; record a resultant dataset outputted by the computing application in response to the first fault; train the one or more machine learning models on the resultant dataset and the first fault; compute a reinforcement learning reward based on performance metrics of the one or more machine learning models and based on a quantity of the resultant dataset; update, via execution of a reinforcement learning algorithm, the fault-injection policy based on the reinforcement learning reward; and inject a second fault into the computing application, based on the updated fault-injection policy.