Safe Exploration for Efficient Policy Evaluation and Comparison
Safe Exploration for Efficient Policy Evaluation and Comparison
复制标题
DOI：
--
复制
发表时间：
2022-02
期刊：
影响因子：
--
通讯作者：
Runzhe Wan;B. Kveton;Rui Song
中科院分区：
文献类型：
--
作者：
Runzhe Wan;B. Kveton;Rui Song
高质量的数据在确保政策评估的准确性方面发挥着核心作用。本文提出了有效和安全的数据收集的土匪政策评估的研究。我们制定的问题，并调查其几个有代表性的变种。对于每一个变异体，我们分析了它的统计特性，推导出相应的探索策略，并设计了一个有效的算法来计算它，理论分析和实验都证明了所提出方法的有效性.
High-quality data plays a central role in ensuring the accuracy of policy evaluation. This paper initiates the study of efficient and safe data collection for bandit policy evaluation. We formulate the problem and investigate its several representative variants. For each variant, we analyze its statistical properties, derive the corresponding exploration policy, and design an efficient algorithm for computing it. Both theoretical analysis and experiments support the usefulness of the proposed methods.