Applications of Reinforcement Learning in Ad Campaign Budget Optimization

Abstract

Reinforcement Learning (RL) has emerged as a promising approach for optimizing dynamic processes, including ad campaign budget allocation. This study explores how RL algorithms can efficiently distribute ad budgets across multiple channels to maximize key performance indicators (KPIs) like conversions and return on investment (ROI). Leveraging techniques such as Q-learning, policy gradient methods, and deep reinforcement learning, this paper highlights the potential of RL in handling uncertainties in consumer behavior and ad performance. Experimental results on simulated and real-world datasets demonstrate the effectiveness of RL compared to traditional methods.