Scalable reinforcement learning for plant-wide control of vinyl acetate monomer process

Author:

Zhu Lingwei,Cui Yunduan,Takami Go,Kanokogi Hiroaki,Matsubara Takamitsu

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Reference41 articles.

1. Azar, M. G., Gómez, V., & Kappen, H. J. (2011). Dynamic policy programming with function approximation. In International conference on artificial intelligence and statistics (pp. 119–127).

2. Dynamic policy programming;Azar;Journal of Machine Learning Research (JMLR),2012

3. Badgwell, T. A., Lee, J. H., & Liu, K.-H. (2018). Reinforcement learning – overview of recent progress and implications for process control. In 13th international symposium on process systems engineering. Computer and chemical engineering (pp. 71–85).

4. Dynamic Programming;Bellman,2003

5. A nonlinear dynamic model of a vinyl acetate process;Chen;Industrial and Engineering Chemistry Research,2003

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