Autonomous Newspaper Page Composition Using Reinforcement Learning

This thesis explores the automation of newspaper pagination using AI, with a focus on offline reinforcement learning. The primary goal is to develop an autonomous system capable of composing newspaper pages with high accuracy and efficiency. Traditional pagination methods are labor-intensive, time-consuming, and costly. This research sought to address these challenges by creating an AI-powered solution that minimizes human intervention while maintaining or enhancing the quality of page layouts.