Enhancing data analyst decision-making with reinforcement learning: A comparative study of traditional vs AI-driven approaches
Year: 2024
Type: article
Abstract: This research paper explores the integration of reinforcement learning (RL) into data analysis, contrasting it with traditional methods. As the role of data analysts becomes increasingly crucial in decision-making processes across industries, the need for more sophisticated tools and approaches has grown. Reinforcement learning, a subset of machine learning, offers a promising avenue for enhancing decision-making by enabling systems to learn optimal strategies through trial and error. This paper examines the theoretical foundations of reinforcement learning, its applications in data analysis, and compares its effectiveness against traditional methods. We conclude by discussing the future implications of RL in data analysis and the potential for further research.
Author: Vinayak Pillai
Institution: The University of Texas at Arlington
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4401896420