Exploring the efficacy of Multi-Armed Bandit Algorithms in dynamic decision-making

Abstract. Originating from the scenario of gambling machines in casinos, the Multi-Armed Bandit problem aims to optimize decision-making processes under limited resources to achieve maximum returns. This article delves into the principles, classifications, and practical applications of this problem. Researchers have proposed various algorithms to address this issue, including -greedy, Upper Confidence Bound, and Thompson Sampling, which have demonstrated good performance across different scenarios. The article further elaborates on the fundamental principles of Multi-Armed Bandit algorithms, encompassing the trade-off between exploration and exploitation, and provides a detailed classification of algorithms based on probability (e.g., -greedy) and value (e.g., UCB). These algorithms not only provide a framework for addressing real-world problems such as advertisement placement and resource allocation, but also possess significant theoretical value in the fields of machine learning and reinforcement learning. By balancing exploration and exploitation, Multi-Armed Bandit algorithms offer effective tools for making optimal decisions in uncertain environments, thus driving the development of related fields.