We introduce general data-driven decision-making algorithms that achieve state-of-the-art \emph{dynamic regret} bounds for non-stationary bandit settings. It captures applications such as advertisement allocation and dynamic pricing in changing environments. We show how the difficulty posed by the (unknown \emph{a priori} and possibly adversarial) non-stationarity can be overcome by an unconventional marriage between stochastic and adversarial bandit learning algorithms. Our main contribution is a general algorithmic recipe that first converts the rate-optimal Upper-Confidence-Bound (UCB) algorithm for stationary bandit settings into a tuned Sliding Window-UCB algorithm with optimal dynamic regret for the corresponding non-stationary counterpart. Boosted by the novel bandit-over-bandit framework with automatic adaptation to the unknown changing environment, it even permits us to enjoy, in a (surprisingly) parameter-free manner, this optimal dynamic regret if the amount of non-stationarity is moderate to large or an improved (compared to existing literature) dynamic regret otherwise. In addition to the classical exploration-exploitation trade-off, our algorithms leverage the power of the "forgetting principle" in their online learning processes, which is vital in changing environments. We further conduct extensive numerical experiments on both synthetic data and the CPRM-12-001: On-Line Auto Lending dataset provided by the Center for Pricing and Revenue Management at Columbia University to show that our proposed algorithms achieve superior dynamic regret performances.

介绍针对非静态赌博机环境的最新数据驱动决策算法，采用了随机和对手式学习算法的非传统结合方法，通过滑动窗口-置信界算法，针对各种非静态赌博机问题实现了最优动态遗憾边界，并通过数字实验验证了算法的超越性能。

对漂移进行对冲：在非稳态环境下学习优化