  In this paper, we consider the setting of piecewise i.i.d. bandits under a
safety constraint. In this piecewise i.i.d. setting, there exists a finite
number of changepoints where the mean of some or all arms change
simultaneously. We introduce the safety constraint studied in
\citet{wu2016conservative} to this setting such that at any round the
cumulative reward is above a constant factor of the default action reward. We
propose two actively adaptive algorithms for this setting that satisfy the
safety constraint, detect changepoints, and restart without the knowledge of
the number of changepoints or their locations. We provide regret bounds for our
algorithms and show that the bounds are comparable to their counterparts from
the safe bandit and piecewise i.i.d. bandit literature. We also provide the
first matching lower bounds for this setting. Empirically, we show that our
safety-aware algorithms perform similarly to the state-of-the-art actively
adaptive algorithms that do not satisfy the safety constraint.
