  We investigate the adversarial bandit problem with multiple plays under
semi-bandit feedback. We introduce a highly efficient algorithm that
asymptotically achieves the performance of the best switching $m$-arm strategy
with minimax optimal regret bounds. To construct our algorithm, we introduce a
new expert advice algorithm for the multiple-play setting. By using our expert
advice algorithm, we additionally improve the best-known high-probability bound
for the multi-play setting by $O(\sqrt{m})$. Our results are guaranteed to hold
in an individual sequence manner since we have no statistical assumption on the
bandit arm gains. Through an extensive set of experiments involving synthetic
and real data, we demonstrate significant performance gains achieved by the
proposed algorithm with respect to the state-of-the-art algorithms.
