New research explores 1/2-Tsallis-INF algorithm for best-arm identification

This research paper investigates the effectiveness of the 1/2-Tsallis-INF algorithm, a known method for minimizing regret in multi-armed bandit problems, when applied to the task of identifying the best arm. The study analyzes the algorithm's failure probability in stochastic bandit settings, focusing on how suboptimal arms are sampled and how this impacts cumulative loss estimators. By developing a Lyapunov function for the gap process between estimated cumulative losses, the paper establishes polynomial upper bounds on the failure probability and demonstrates that the exponent in these bounds is essentially tight. AI

RANK_REASON The item is an academic paper detailing theoretical analysis of an algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

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