  In a typical stochastic multi-armed bandit problem, the objective is often to
maximize the expected sum of rewards over some time horizon $T$. While the
choice of a strategy that accomplishes that is optimal with no additional
information, it is no longer the case when provided additional
environment-specific knowledge. In particular, in areas of high volatility like
healthcare or finance, a naive reward maximization approach often does not
accurately capture the complexity of the learning problem and results in
unreliable solutions. To tackle problems of this nature, we propose a framework
of adaptive risk-aware strategies that operate in non-stationary environments.
Our framework incorporates various risk measures prevalent in the literature to
map multiple families of multi-armed bandit algorithms into a risk-sensitive
setting. In addition, we equip the resulting algorithms with the Restarted
Bayesian Online Change-Point Detection (R-BOCPD) algorithm and impose a
(tunable) forced exploration strategy to detect local (per-arm) switches. We
provide finite-time theoretical guarantees and an asymptotic regret bound of
order $\tilde O(\sqrt{K_T T})$ up to time horizon $T$ with $K_T$ the total
number of change-points. In practice, our framework compares favorably to the
state-of-the-art in both synthetic and real-world environments and manages to
perform efficiently with respect to both risk-sensitivity and non-stationarity.
