提出离线决策的统计复杂度新理论，突破传统数据覆盖限制。
On The Statistical Complexity of Offline Decision-Making
- 用伪维数和新行为策略表征刻画复杂度
- 在随机上下文猜拳和马尔可夫决策中达近最优率
- 适用于从离线数据中学习并提升在线决策
我们研究了带函数逼近的离线决策问题的统计复杂度，为随机上下文猜拳和马尔可夫决策过程建立了（近）最小最大最优率。性能极限由（价值）函数类的伪维数以及一个严格涵盖此前所有离线决策文献中数据覆盖概念的新行为策略表征所捕获。此外，我们探讨了利用离线数据对在线决策的增益，并在多种情形下证明了近乎最小最大最优的性能率。
原文摘要 · Abstract (English)
We study the statistical complexity of offline decision-making with function approximation, establishing (near) minimax-optimal rates for stochastic contextual bandits and Markov decision processes. The performance limits are captured by the pseudo-dimension of the (value) function class and a new characterization of the behavior policy that \emph{strictly} subsumes all the previous notions of data coverage in the offline decision-making literature. In addition, we seek to understand the benefits of using offline data in online decision-making and show nearly minimax-optimal rates in a wide range of regimes.
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