Simulation augmented reinforcement learning for real-time content selection

Abstract

Systems, devices, and methods are described herein for improving inventory management. As used herein, 'inventory' refers to digital space at an inventory providers webpage at which content can be delivered. The disclosed techniques utilize reinforced machine learning and an offline training process to train various models with which a content request corresponding to the inventory can be classified according to historical requests and a selection process identified for the request (e.g., a direct or an indirect selection process). If an indirect selection process is chosen, the content request may be optimized for that process utilizing additional machine learning models trained using reinforced machine learning and the offline training process. The disclosed techniques enable the inventory provider to optimize content selections according to a preferred objective. The training operations are performed offline, in a training system configured to simulate the run time environment.