论文标题
在异质环境中的平行最佳手臂识别
Parallel Best Arm Identification in Heterogeneous Environments
论文作者
论文摘要
在本文中，我们研究了在异质协作学习模型中最佳ARM识别问题的时间与交流回合之间的权衡，其中多种代理与可能不同的环境进行交互，他们希望在聚合环境中并行学习目标功能。通过证明几乎紧密的上限和下限，我们表明在异质环境中的协作学习本质上比在均匀环境中的折衷方案更加困难。
In this paper, we study the tradeoffs between the time and the number of communication rounds of the best arm identification problem in the heterogeneous collaborative learning model, where multiple agents interact with possibly different environments and they want to learn in parallel an objective function in the aggregated environment. By proving almost tight upper and lower bounds, we show that collaborative learning in the heterogeneous setting is inherently more difficult than that in the homogeneous setting in terms of the time-round tradeoff.
