Deep reinforcement learning approaches for process control

@article{Spielberg2017DeepRL,
  title={Deep reinforcement learning approaches for process control},
  author={S.P.K. Spielberg and R. B. Gopaluni and P. D. Loewen},
  journal={2017 6th International Symposium on Advanced Control of Industrial Processes (AdCONIP)},
  year={2017},
  pages={201-206}
}
In this work, we have extended the current success of deep learning and reinforcement learning to process control problems. We have shown that if reward hypothesis functions are formulated properly, they can be used for industrial process control. The controller setup follows the typical reinforcement learning setup, whereby an agent (controller) interacts with an environment (process) through control actions and receives a reward in discrete time steps. Deep neural networks serve as function… CONTINUE READING

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