Multi-Armed Bandit Algorithms in A/B Testing: Comparing the Performance of Various Multi-Armed Bandit Algorithms in the Context of A/B Testing

Abstract

A/B testing is a widely used technique for comparing the effectiveness of different versions of a product or service. Multi-armed bandit algorithms have emerged as a promising approach to optimize A/B testing by dynamically allocating traffic to the best-performing variant. This paper provides an in-depth comparison of the performance of various multi-armed bandit algorithms in the context of A/B testing. We evaluate the algorithms based on their ability to maximize rewards, minimize regret, and adapt to changing environments. The findings highlight the strengths and limitations of each algorithm and guide the selection of the most suitable algorithm for different A/B testing scenarios. We also discuss the trade-offs between exploration and exploitation, the impact of prior knowledge, and the scalability of multi-armed bandit algorithms in large-scale A/B testing. The paper concludes with recommendations for future research directions and practical implications for implementing multi-armed bandit algorithms in A/B testing