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Adaptation Entropy Regularization Actor-Critic for Process Operation Performance Assessment
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DOI：10.1109/TII.2023.3342411.png)
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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(history) of the POPA model trained with historical data and the POPA parameters theta(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(history) and theta(current) into new parameters theta(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.
Keyword:
Advantage actor-critic
different data distribution
electro-fused magnesium furnace (EFMF)
multisource heterogeneous information (MSHI)
process operation performance assessment (POPA)
期刊
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9.9
论文数:
8.6K
被引数:
6.0W
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