Multi modal hierarchical reinforcement learning framework for dynamic sports sponsorship optimization

Abstract

Abstract This paper presents a novel approach to optimizing sports sponsorship strategies by integrating reinforcement learning (RL) with a multi-modal hierarchical framework, enhancing real-time decision-making using diverse data sources such as computer vision, natural language processing, and graph neural networks (GNNs). The system utilizes RL to dynamically optimize sponsorship strategies across strategic, tactical, and operational levels. Using the Meta-Soft Actor-Critic (Meta-SAC) algorithm, it adapts to real-time data streams, including social media sentiment, event footage, and stakeholder interactions. Our system demonstrates a 25–35% improvement in ROI, a 20–30% increase in brand exposure, and a 15–25% rise in audience engagement compared to conventional strategies. The proposed RL-driven, multi-modal framework significantly outperforms traditional methods, providing scalable, adaptive solutions for optimizing sports sponsorship effectiveness.