Comprehensive Exploration and Implementation of Multi-Armed Bandit Algorithms Across Various Domains

Abstract

This paper presents a comprehensive analysis of Multi-Armed Bandit (MAB) algorithms, elucidating their decision-making mechanisms in uncertain environments and their widespread applications in fields such as recommendation systems, advertisement delivery, network traffic control, and medical experiment design. Despite the notable successes of MAB algorithms, they encounter significant challenges in practical deployment, including the balance between exploration and exploitation, addressing unfairness, and managing large-scale implementations. An in-depth theoretical and practical examination of MAB algorithms is thus both theoretically and practically vital. This study conducts a meticulous literature review, theoretical analysis, and empirical research to offer insights into the current state and future prospects of MAB algorithms. It begins with a literature review, mapping out the research landscape and developmental trajectory of MAB algorithms. This is followed by a theoretical dissection, delving into the fundamental theories and diverse applications of MAB algorithms, with a focus on their utilization in recommendation systems, vehicle edge computing, and taxi route recommendation systems. Empirical research is then employed to validate proposed solutions and enhancement strategies. The study reinterprets the internal mechanics of MAB algorithms, affirming their effectiveness and superiority across various domains. The final section addresses the practical challenges and issues faced by MAB algorithms, such as fairness concerns and scalability. Corresponding solutions and improvement strategies are suggested, aiming to enhance the efficiency and applicability of MAB algorithms in real-world scenarios.