DAL: A Practical Prior-Free Black-Box Framework for Piecewise Stationary Bandits
Researchers have introduced Detection Augmented Learning (DAL), a new framework designed for piecewise stationary bandits that does not require prior knowledge of non-stationarity. DAL functions by integrating any existing stationary bandit algorithm with a change detector, thereby extending its applicability to a wide range of bandit problems. Empirical results across various synthetic and real-world datasets indicate that DAL consistently outperforms current state-of-the-art methods, demonstrating its effectiveness and scalability. AI