Balancing Exploration and Exploitation: An Analytical Review of Three Classical Bandit Algorithms

Abstract

In daily lives, numerous decision-making challenges can be framed as multi-armed bandit problems, where people must strategically balance between exploration and exploitation. This paper focuses on three prominent algorithms developed to tackle this dilemma: the Explore-then-commit (ETC) algorithm, the Upper Confidence Bound (UCB) algorithm, and Thompson Sampling (TS) algorithm. The paper provides a detailed examination of their core idea and a critical analysis of their respective advantages and disadvantages. Furthermore, this paper highlights how their inherent characteristics lead to different performance profiles in varied contexts and contextualize their differences by proposing targeted real-world application scenarios for each algorithm. This paper not only concludes key insights for researchers but also offers an accessible entry point for beginners in the reinforcement learning and data science. By elucidating the essence behind each method and their practical implications, this paper equips newcomers with the foundational knowledge necessary to understand and apply these powerful tools in real-world problems.