Opportunities of Hybrid Model-based Reinforcement Learning for Cell Therapy Manufacturing Process Control. (arXiv:2201.03116v2 [eess.SY] UPDATED)

Driven by the key challenges of cell therapy manufacturing, including high
complexity, high uncertainty, and very limited process observations, we propose
a hybrid model-based reinforcement learning (RL) to efficiently guide process
control. We first create a probabilistic knowledge graph (KG) hybrid model
characterizing the risk- and science-based understanding of biomanufacturing
process mechanisms and quantifying inherent stochasticity, e.g., batch-to-batch
variation. It can capture the key features, including nonlinear reactions,
nonstationary dynamics, and partially observed state. This hybrid model can
leverage existing …

arxiv cell therapy hybrid learning manufacturing process reinforcement learning