Control of a bioreactor using a new partially supervised reinforcement learning algorithm

@article{Pandian2018ControlOA,
  title={Control of a bioreactor using a new partially supervised reinforcement learning algorithm},
  author={B. Jaganatha Pandian and Mathew Mithra Noel},
  journal={Journal of Process Control},
  year={2018}
}
Meta Reinforcement Learning for Adaptive Control: An Offline Approach
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It is confirmed that feedback via a trained reinforcement learning agent can be used to maintain populations at target levels, and that model-free performance with bang-bang control can outperform a traditional proportional integral controller with continuous control, when faced with infrequent sampling.
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This work develops a mathematical model of bacterial communities within a che mostat that incorporates generalised Lotka-Volterra interactions and shows that reinforcement learning agents can learn to maintain multiple species of cells in a variety of chemostat systems, subject to competition for nutrients and other competitive interactions.
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