Advancing decision-making strategies through a comprehensive study of Multi-Armed Bandit algorithms and applications

Abstract

Multi-Armed Bandit (MAB) strategies play a pivotal role in decision-making algorithms by adeptly managing the exploration-exploitation trade-off in environments characterized by multiple options and constrained resources. This paper delves into the core MAB algorithms, including Explore-Then-Commit (ETC), Thompson Sampling, and Upper Confidence Bound (UCB). It provides a detailed examination of their theoretical underpinnings and their application across diverse sectors such as recommender systems, healthcare, and finance. MAB algorithms are celebrated for their efficiency in optimizing decision outcomes; however, they are not without challenges. Significant issues include managing the complexity of exploration and adapting to non-stationary environments where the dynamics of the available options may change over time. A nuanced understanding of these challenges is crucial for effectively implementing MAB strategies in complex decision-making scenarios. This study not only highlights the versatility and potential of MAB algorithms but also underscores the need for ongoing research to refine these techniques and expand their applicability.