  We study Thompson Sampling algorithms for stochastic multi-armed bandits in
the batched setting, in which we want to minimize the regret over a sequence of
arm pulls using a small number of policy changes (or, batches). We propose two
algorithms and demonstrate their effectiveness by experiments on both synthetic
and real datasets. We also analyze the proposed algorithms from the theoretical
aspect and obtain almost tight regret-batches tradeoffs for the two-arm case.
