  We study the multi-player stochastic multiarmed bandit (MAB) problem in an
abruptly changing environment. We consider a collision model in which a player
receives reward at an arm if it is the only player to select the arm. We design
two novel algorithms, namely, Round-Robin Sliding-Window Upper Confidence
Bound\# (RR-SW-UCB\#), and the Sliding-Window Distributed Learning with
Prioritization (SW-DLP). We rigorously analyze these algorithms and show that
the expected cumulative group regret for these algorithms is upper bounded by
sublinear functions of time, i.e., the time average of the regret
asymptotically converges to zero. We complement our analytic results with
numerical illustrations.
