REINFORCEMENT LEARNING MEETS DATA SCIENCE: ADAPTIVE SYSTEMS FOR REAL-WORLD OPTIMIZATION

Abstract

Reinforcement Learning (RL) has emerged as a transformative paradigm within data science, offering the ability to develop adaptive systems capable of optimizing decisions through experience. By interacting with complex environments, RL agents learn optimal actions without the need for labeled datasets—making it ideal for real-world applications such as dynamic pricing, robotics, recommendation systems, and resource allocation. This paper explores the intersection of RL and data science, highlighting architectural foundations, key integration challenges, hybrid approaches with supervised learning, and sector-specific case studies. Emphasis is placed on how RL enhances adaptability, decision accuracy, and long-term optimization strategies in uncertain and evolving environments.