We study the problem of Bayesian fixed-budget best-arm identification (BAI) in structured bandits. We propose an algorithm that uses fixed allocations based on the prior information and the structure of the environment. We provide theoretical bounds on its performance across diverse models, including the first prior-dependent upper bounds for linear and hierarchical BAI. Our key contribution is introducing new proof methods that result in tighter bounds for multi-armed BAI compared to existing methods. We extensively compare our approach to other fixed-budget BAI methods, demonstrating its consistent and robust performance in various settings. Our work improves our understanding of Bayesian fixed-budget BAI in structured bandits and highlights the effectiveness of our approach in practical scenarios.

我们研究了基于贝叶斯的固定预算最佳臂识别（BAI）在结构化赌博问题中的应用，提出了一种算法，该算法基于先前的信息和环境的结构使用固定分配，我们对该算法在各种模型上的性能给出了理论上的界限，包括首次基于先验信息的线性和分层BAI的上界。我们的主要贡献是引入了新的证明方法，相比现有方法，该方法对多臂BAI的界限更紧。我们广泛比较了我们的方法与其他固定预算BAI方法，在各种场景下展示了其一致且稳健的性能，我们的工作提升了我们对结构化赌博中基于贝叶斯的固定预算BAI的理解，并突显了我们方法在实际场景中的有效性。

基于先验的分配策略在结构化赌博机中进行贝叶斯固定预算最佳臂识别