TL;DR: Constrained continuous control of bioprocesses via policy gradients and recurrent neural networks.
Keywords: reinforcement learning, contiuous control, recurrent neural networks, policy gradient, bioprocess, optimisation
Abstract: Bioprocesses have recently received attention to produce clean and
sustainable alternatives to fossil-based materials. However, they are
generally difficult to optimize due to their unsteady-state operation
modes and stochastic behaviours. Furthermore, biological systems
are highly complex, therefore plant-model mismatch is often present.
In this work we leverage a model-free Reinforcement Learning
optimisation strategy. We apply the Policy Gradient method to tune
a control policy parametrized by a recurrent neural network. We
assume that a preliminary model of the process is available, which is
exploited to obtain an initial optimal control policy. Subsequently,
this policy is updated based on a variation of the starting model,
with adequate disturbance, to simulate the plan-model mismatch.
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