Leveraging Reinforcement Learning and Multi-Armed Bandit Algorithms for Real-Time Optimization in Ad Campaign Management

Abstract

This research paper explores the application of reinforcement learning (RL) and multi-armed bandit (MAB) algorithms in optimizing real-time ad campaign management. In the competitive landscape of digital advertising, efficiently allocating budgets and selecting the optimal set of ad creatives and targeting strategies is critical for maximizing returns on investment. Traditional methods, often reliant on historical data and static rules, fall short in accommodating the dynamic nature of ad interactions and consumer behavior. Our study introduces a framework that employs RL to dynamically adjust ad parameters in real-time, learning from interactions and continuously improving decision-making. Furthermore, we incorporate MAB algorithms to address the exploration-exploitation dilemma, allowing for adaptive experimentation with different ad options to identify the most effective strategies. Through extensive simulations and real-world data testing, our results demonstrate significant improvements in key performance indicators such as click-through rates and conversion metrics when compared to conventional optimization techniques. The RL component enables the system to learn directly from feedback, allowing for responsive adjustments to changing market conditions and user preferences. Meanwhile, the MAB approach provides a robust mechanism for handling the uncertainty and variability intrinsic to consumer engagement patterns, ensuring optimal allocation of advertising resources. Our framework also offers scalability and flexibility, accommodating diverse ad platforms and constraints. This paper contributes to the evolving field of ad tech by showcasing the potential of advanced machine learning algorithms in enhancing the efficacy and efficiency of digital ad campaigns.