Assessing the robustness of Multi-Armed Bandit algorithms against biased initialization

Abstract

The robustness of Multi-Armed Bandit (MAB) algorithms forms a cornerstone of the efficacy of contemporary recommender systems. This study provides a comparative analysis of four widely-adopted MAB algorithmsEpsilon Greedy, Explore Then Commit (ETC), Upper Confidence Bound (UCB1), and Thompson Samplingunder the influence of biased initialization. Conducted in a simulated environment that mirrors practical recommender scenarios, the study examines the adaptive responses of these algorithms over time, quantifying their performance using cumulative regret as a primary metric. Our findings indicate varying degrees of resilience, with Epsilon Greedy exhibiting the slowest recovery from initial bias and Thompson Sampling demonstrating consistent adaptability. By exploring the implications of static biases to various multi-armed bandit algorithms, this research contributes foundational insights for advancing the development of robust and equitable recommender systems.