  We give a complete characterization of the complexity of best-arm
identification in one-parameter bandit problems. We prove a new, tight lower
bound on the sample complexity. We propose the `Track-and-Stop' strategy, which
we prove to be asymptotically optimal. It consists in a new sampling rule
(which tracks the optimal proportions of arm draws highlighted by the lower
bound) and in a stopping rule named after Chernoff, for which we give a new
analysis.
