  We consider the two-armed bandit problem as applied to data processing if
there are two alternative processing methods available with different a priori
unknown efficiencies. One should determine the most effective method and
provide its predominant application. Gaussian two-armed bandit describes the
batch, and possibly parallel, processing when the same methods are applied to
sufficiently large packets of data and accumulated incomes are used for the
control. If the number of packets is large enough then such control does not
deteriorate the control performance, i.e. does not increase the minimax risk.
For example, in case of 50 packets the minimax risk is about 2% larger than
that one corresponding to one-by-one optimal processing. However, this is
completely true only for methods with close efficiencies because otherwise
there may be significant expected losses at the initial stage of control when
both actions are applied turn-by-turn. To avoid significant losses at the
initial stage of control one should take initial packets of data having smaller
sizes.
