  Top-$2$ methods have become popular in solving the best arm identification
(BAI) problem. The best arm, or the arm with the largest mean amongst finitely
many, is identified through an algorithm that at any sequential step
independently pulls the empirical best arm, with a fixed probability $\beta$,
and pulls the best challenger arm otherwise. The probability of incorrect
selection is guaranteed to lie below a specified $\delta >0$. Information
theoretic lower bounds on sample complexity are well known for BAI problem and
are matched asymptotically as $\delta \rightarrow 0$ by computationally
demanding plug-in methods. The above top 2 algorithm for any $\beta \in (0,1)$
has sample complexity within a constant of the lower bound. However,
determining the optimal $\beta$ that matches the lower bound has proven
difficult. In this paper, we address this and propose an optimal top-2 type
algorithm. We consider a function of allocations anchored at a threshold. If it
exceeds the threshold then the algorithm samples the empirical best arm.
Otherwise, it samples the challenger arm. We show that the proposed algorithm
is optimal as $\delta \rightarrow 0$. Our analysis relies on identifying a
limiting fluid dynamics of allocations that satisfy a series of ordinary
differential equations pasted together and that describe the asymptotic path
followed by our algorithm. We rely on the implicit function theorem to show
existence and uniqueness of these fluid ode's and to show that the proposed
algorithm remains close to the ode solution.
