Adaptation Entropy Regularization Actor–Critic for Process Operation Performance Assessment

In the process operation performance assessment (POPA) of electro-fused magnesium furnace (EFMF), the distribution of testing and training data is inconsistent. Therefore, in order to solve the problem, a new adaptation entropy regularization actor–critic (AERAC) reinforcement learning method is proposed based on the multisource heterogeneous information. The core of the method is that the parameters
$\theta _{\text{history}}$
of the POPA model trained with historical data and the POPA parameters
$\theta _{\text{current}}$
trained with current data are combined into the state input policy network. Then, the policy network predicts the action according to the state. Finally, the action will integrate the parameters
$\theta _{\text{history}}$
and
$\theta _{\text{current}}$
into new parameters
$\theta _{\text{test}}$
of the POPA model for the adaptive test dataset. The method is verified by the dataset of EFMF. The results show that the AERAC method can effectively solve the problem that the distributions of training and testing set are different.