首页|A Survey on Causal Reinforcement Learning

A Survey on Causal Reinforcement Learning

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  • NETL
  • NSTL
  • IEEE
While reinforcement learning (RL) achieves tremendous success in sequential decision-making problems of many domains, it still faces key challenges of data inefficiency and the lack of interpretability. Interestingly, many researchers have leveraged insights from the causality literature recently, bringing forth flourishing works to unify the merits of causality and address well the challenges from RL. As such, it is of great necessity and significance to collate these causal RL (CRL) works, offer a review of CRL methods, and investigate the potential functionality from causality toward RL. In particular, we divide the existing CRL approaches into two categories according to whether their causality-based information is given in advance or not. We further analyze each category in terms of the formalization of different models, ranging from the Markov decision process (MDP), partially observed MDP (POMDP), multiarmed bandits (MABs), imitation learning (IL), and dynamic treatment regime (DTR). Each of them represents a distinct type of causal graphical illustration. Moreover, we summarize the evaluation matrices and open sources, while we discuss emerging applications, along with promising prospects for the future development of CRL.

Cause effect analysisSurveysMathematical modelsDrugsSunReviewsReinforcement learningDecision makingData modelsComputer science

Yan Zeng、Ruichu Cai、Fuchun Sun、Libo Huang、Zhifeng Hao

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School of Mathematics and Statistics, Beijing Technology and Business University, Beijing, China|Department of Computer Science and Technology, Tsinghua University, Beijing, China

School of Computer Science, Guangdong University of Technology, Guangzhou, China|Pazhou Laboratory (Huangpu), Guangzhou, China

Department of Computer Science and Technology, Tsinghua University, Beijing, China

Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

College of Science, Shantou University, Shantou, China

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2025

10.1109/TNNLS.2024.3403001

IEEE transactions on neural networks and learning systems

IEEE transactions on neural networks and learning systems

ISSN：
年,卷(期)：2025.36(4)
  • 223