  We investigate the piecewise-stationary combinatorial semi-bandit problem.
Compared to the original combinatorial semi-bandit problem, our setting assumes
the reward distributions of base arms may change in a piecewise-stationary
manner at unknown time steps. We propose an algorithm, \texttt{GLR-CUCB}, which
incorporates an efficient combinatorial semi-bandit algorithm, \texttt{CUCB},
with an almost parameter-free change-point detector, the \emph{Generalized
Likelihood Ratio Test} (GLRT). Our analysis shows that the regret of
\texttt{GLR-CUCB} is upper bounded by $\mathcal{O}(\sqrt{NKT\log{T}})$, where
$N$ is the number of piecewise-stationary segments, $K$ is the number of base
arms, and $T$ is the number of time steps. As a complement, we also derive a
nearly matching regret lower bound on the order of $\Omega(\sqrt{NKT}$), for
both piecewise-stationary multi-armed bandits and combinatorial semi-bandits,
using information-theoretic techniques and judiciously constructed
piecewise-stationary bandit instances. Our lower bound is tighter than the best
available regret lower bound, which is $\Omega(\sqrt{T})$. Numerical
experiments on both synthetic and real-world datasets demonstrate the
superiority of \texttt{GLR-CUCB} compared to other state-of-the-art algorithms.
