  We study a multi-objective multi-armed bandit problem in a dynamic
environment. The problem portrays a decision-maker that sequentially selects an
arm from a given set. If selected, each action produces a reward vector, where
every element follows a piecewise-stationary Bernoulli distribution. The agent
aims at choosing an arm among the Pareto optimal set of arms to minimize its
regret. We propose a Pareto generic upper confidence bound (UCB)-based
algorithm with change detection to solve this problem. By developing the
essential inequalities for multi-dimensional spaces, we establish that our
proposal guarantees a regret bound in the order of $\gamma_T\log(T/{\gamma_T})$
when the number of breakpoints $\gamma_T$ is known. Without this assumption,
the regret bound of our algorithm is $\gamma_T\log(T)$. Finally, we formulate
an energy-efficient waveform design problem in an integrated communication and
sensing system as a toy example. Numerical experiments on the toy example and
synthetic and real-world datasets demonstrate the efficiency of our policy
compared to the current methods.
