  We consider a novel stochastic multi-armed bandit problem called {\em good
arm identification} (GAI), where a good arm is defined as an arm with expected
reward greater than or equal to a given threshold. GAI is a pure-exploration
problem that a single agent repeats a process of outputting an arm as soon as
it is identified as a good one before confirming the other arms are actually
not good. The objective of GAI is to minimize the number of samples for each
process. We find that GAI faces a new kind of dilemma, the {\em
exploration-exploitation dilemma of confidence}, which is different difficulty
from the best arm identification. As a result, an efficient design of
algorithms for GAI is quite different from that for the best arm
identification. We derive a lower bound on the sample complexity of GAI that is
tight up to the logarithmic factor $\mathrm{O}(\log \frac{1}{\delta})$ for
acceptance error rate $\delta$. We also develop an algorithm whose sample
complexity almost matches the lower bound. We also confirm experimentally that
our proposed algorithm outperforms naive algorithms in synthetic settings based
on a conventional bandit problem and clinical trial researches for rheumatoid
arthritis.
