Leveraging Reinforcement Learning and Multi-Armed Bandit Algorithms for Optimizing AI-Driven Dynamic Ad Bidding Strategies

Abstract

This research paper investigates the integration of reinforcement learning (RL) and multi-armed bandit (MAB) algorithms to enhance AI-driven dynamic ad bidding strategies. With the increasing complexity and competitiveness in digital advertising, traditional ad bidding approaches struggle to adapt to rapid market changes and user behavior. This study proposes a novel framework that leverages the strengths of RL in learning optimal policies through interaction with dynamic environments and MAB's efficiency in decision-making under uncertainty. We conduct an extensive analysis of existing ad bidding models, identifying key limitations and challenges, and demonstrate how our hybrid approach addresses these by continuously learning and adapting to market fluctuations. The proposed model utilizes a reward system that incorporates real-time feedback from ad performance metrics, enabling the model to optimize bids strategically. We employ both simulated and real-world datasets to evaluate our framework, demonstrating significant improvements in click-through rates (CTR) and return on ad spend (ROAS), with an observed increase in efficiency and cost-effectiveness compared to traditional methods. Furthermore, the study explores the robustness of the model in handling various market scenarios, including the introduction of new competitors and sudden changes in user preferences. The results indicate that the integration of RL and MAB provides a powerful toolset for advertisers, equipping them with adaptive and scalable mechanisms suitable for the fast-paced demands of digital advertising environments. This research contributes to the field by offering a comprehensive approach to dynamic ad bidding, highlighting not only the theoretical implications but also practical applications for advertisers seeking to maximize their investment returns.