论文标题
无限视野的部分反事实识别部分可观察到的马尔可夫决策过程
Partial Counterfactual Identification for Infinite Horizon Partially Observable Markov Decision Process
论文作者
论文摘要
本文研究了一组观察数据，研究了反事实查询的可能输出的问题。尽管各种文献作品描述了生成有效算法的方法，该算法为反事实查询提供了最佳结合，但所有这些都假设有限 - 霍斯 - 霍斯 - 霍斯 - 霍斯曲线图。本文旨在通过修改Q学习算法来扩展先前的工作，以提供无限 - 马利亚因果图的因果查询的信息界限。通过模拟，与现有算法相比，我们的算法的性能更好。
This paper investigates the problem of bounding possible output from a counterfactual query given a set of observational data. While various works of literature have described methodologies to generate efficient algorithms that provide an optimal bound for the counterfactual query, all of them assume a finite-horizon causal diagram. This paper aims to extend the previous work by modifying Q-learning algorithm to provide informative bounds of a causal query given an infinite-horizon causal diagram. Through simulations, our algorithms are proven to perform better compared to existing algorithm.
