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人机交互的收益：混杂环境中的超级策略学习AI 解读

As AI becomes more prevalent throughout society, effective methods of integrating humans and AI systems that leverage their respective strengths and mitigate risk have become an important priority. In this article, we introduce the paradigm of super policy learning that takes advantage of Human-AI interaction for data driven sequential decision making. This approach uses the observed action, either from AI or humans, as input for achieving a stronger oracle in policy…

离线强化学习中的投影状态—动作平衡权重AI 解读

Off-policy evaluation is considered a fundamental and challenging problem in reinforcement learning (RL). This paper focuses on value estimation of a target policy based on pre-collected data generated from a possibly different policy, under the framework of infinite-horizon Markov decision processes. Motivated by the recently developed marginal importance sampling method in RL and the covariate balancing idea in causal inference, we propose a novel estimator with…

多维函数型数据的低秩协方差函数估计AI 解读

Jiayi Wanga, Raymond K. W. Wonga* & Xiaoke Zhangb a Department of Statistics, Texas A&M University, College Station, TX; b Department of Statistics, George Washington University, Washington, DC Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA. CONTACT Raymond K. W. Wong raywong@tamu.edu Department of Statistics, Texas A&M University , College Station , TX 77843.AbstractMultidimensional function data arise from many fields…