RBNets: A Reinforcement Learning Approach for Learning Bayesian Network Structure
Abstract
Bayesian networks are graphical models that are capable of encoding complex statistical and causal dependencies, thereby facilitating powerful probabilistic inferences. To apply these models to real-world problems, it is first necessary to determine the Bayesian network structure, which represents the dependencies. Classic methods for this problem typically employ score-based search techniques, which are often heuristic in nature and have limited running times and performances that do not scale well for larger problems. In this paper, we propose a novel technique called RBNets, which uses deep reinforcement learning along with an exploration strategy guided by Upper Confidence Bound for learning Bayesian Network structures. RBNets solves the highest-value path problem and progressively finds better solutions. We demonstrate the efficiency and effectiveness of our approach against several state-of-the-art methods in extensive experiments using both real-world and synthetic datasets.
Cite
Text
Zheng et al. "RBNets: A Reinforcement Learning Approach for Learning Bayesian Network Structure." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023. doi:10.1007/978-3-031-43418-1_12Markdown
[Zheng et al. "RBNets: A Reinforcement Learning Approach for Learning Bayesian Network Structure." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023.](https://mlanthology.org/ecmlpkdd/2023/zheng2023ecmlpkdd-rbnets/) doi:10.1007/978-3-031-43418-1_12BibTeX
@inproceedings{zheng2023ecmlpkdd-rbnets,
title = {{RBNets: A Reinforcement Learning Approach for Learning Bayesian Network Structure}},
author = {Zheng, Zuowu and Wang, Chao and Gao, Xiaofeng and Chen, Guihai},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2023},
pages = {193-208},
doi = {10.1007/978-3-031-43418-1_12},
url = {https://mlanthology.org/ecmlpkdd/2023/zheng2023ecmlpkdd-rbnets/}
}