  We study best arm identification in a variant of the multi-armed bandit
problem where the learner has limited precision in arm selection. The learner
can only sample arms via certain exploration bundles, which we refer to as
boxes. In particular, at each sampling epoch, the learner selects a box, which
in turn causes an arm to get pulled as per a box-specific probability
distribution. The pulled arm and its instantaneous reward are revealed to the
learner, whose goal is to find the best arm by minimising the expected stopping
time, subject to an upper bound on the error probability. We present an
asymptotic lower bound on the expected stopping time, which holds as the error
probability vanishes. We show that the optimal allocation suggested by the
lower bound is, in general, non-unique and therefore challenging to track. We
propose a modified tracking-based algorithm to handle non-unique optimal
allocations, and demonstrate that it is asymptotically optimal. We also present
non-asymptotic lower and upper bounds on the stopping time in the simpler
setting when the arms accessible from one box do not overlap with those of
others.
