BAYESIAN NETWORK MODEL GENERATION METHOD BASED ON REINFORCEMENT LEARNING

The network structure of the traditional Bayesian network needs to be determined in advance, and the reliability and accuracy of the model are low when used for prediction. Therefore, a Bayesian network model generation method based on reinforcement learning is proposed. The reinforcement learning was used to search for the optimal generalization residual score, and the Bayesian network was abstracted into a directed acyclic graph by constructing an adjacency matrix. For the completed Bayesian network, a Bayesian network structure optimization method based on causal direction judgment was proposed. The experimental results show that the method in this paper is superior to all kinds of traditional Bayesian network structure generation methods.