燕山大学学报2024，Vol.48Issue(4)：356-368,13.DOI:10.3969/j.issn.1007-791X.2024.04.008
异步策略的强化因果发现方法
A reinforcement causal discovery approach for asynchronous strategy
摘要
Abstract
The research and discovery of causality between things is one of the core issues in data science.Causal discovery usually faces problems such as super exponential growth of the search space,low evaluation index,slow rate of convergence and poor effect.To solve them,a reinforcement causal discovery method is proposed for asynchronous strategy.Firstly,a self-attentional encoder and a single-layer decoder model are used to explore the causal relationship between the data.Secondly,the structural constraints in the reinforcement learning model are improved,and the parameters of the network model are updated based on the asynchronous dominance algorithm.Finally,the directed acyclic graph with the maximum reward is given by searching.The good performance of this method has been verified through experimental comparison.关键词
因果关系/有向无环图/强化因果发现/结构约束/异步优势算法Key words
causal relationship/directed acyclic graph/reinforcement causal discovery/structural constraint/asynchronous dominance algorithm分类
信息技术与安全科学引用本文复制引用
张英,郭辉..异步策略的强化因果发现方法[J].燕山大学学报,2024,48(4):356-368,13.基金项目
宁夏自然科学基金资助项目(2021AAC03117) （2021AAC03117）
