  We consider the fixed-budget best arm identification problem where the goal
is to find the arm of the largest mean with a fixed number of samples. It is
known that the probability of misidentifying the best arm is exponentially
small to the number of rounds. However, limited characterizations have been
discussed on the rate (exponent) of this value. In this paper, we characterize
the minimax optimal rate as a result of an optimization over all possible
parameters. We introduce two rates, $R^{\mathrm{go}}$ and
$R^{\mathrm{go}}_{\infty}$, corresponding to lower bounds on the probability of
misidentification, each of which is associated with a proposed algorithm. The
rate $R^{\mathrm{go}}$ is associated with $R^{\mathrm{go}}$-tracking, which can
be efficiently implemented by a neural network and is shown to outperform
existing algorithms. However, this rate requires a nontrivial condition to be
achievable. To address this issue, we introduce the second rate
$R^{\mathrm{go}}_\infty$. We show that this rate is indeed achievable by
introducing a conceptual algorithm called delayed optimal tracking (DOT).
