Optimizing Dynamic Ad Bidding Strategies Using Reinforcement Learning and Multi-Armed Bandit Algorithms

Abstract

This research paper explores the application of reinforcement learning and multi-armed bandit (MAB) algorithms to optimize dynamic ad bidding strategies in digital advertising. The growing complexity and competitiveness of online ad auctions necessitate more sophisticated approaches to bid optimization. This study introduces a novel framework that integrates deep reinforcement learning with traditional MAB models to dynamically adjust bids to maximize advertiser return on investment. The proposed model leverages historical bidding data and real-time performance feedback to learn and predict optimal bidding decisions across various advertising channels and formats. A key aspect of this research is the incorporation of contextual bandits, which allow the algorithm to adapt to changing market conditions and user behavior in real-time. Experimental results demonstrate significant improvements in key performance metrics, such as click-through rates and conversion rates, compared to conventional static and heuristic-based bidding strategies. The reinforcement learning model shows a greater capacity to generalize across different scenarios, offering robust performance even as underlying dynamics evolve. Additionally, the integration of exploration-exploitation mechanisms inherent in MAB frameworks proves essential in balancing the trade-off between bidding aggressiveness and cost efficiency. This research contributes to the field by providing an adaptive, automated approach to ad bidding, thereby enhancing advertisers' ability to achieve desired outcomes in rapidly fluctuating auction environments.