Policy Optimization in Dynamic Bayesian Network Hybrid Models of Biomanufacturing Processes

Hua Zheng (), Wei Xie (), Ilya O. Ryzhov () and Dongming Xie ()
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Hua Zheng: Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115
Wei Xie: Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115
Ilya O. Ryzhov: Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742
Dongming Xie: Department of Chemical Engineering, University of Massachusetts, Lowell, Massachusetts 01854

INFORMS Journal on Computing, 2023, vol. 35, issue 1, 66-82

Abstract: Biopharmaceutical manufacturing is a rapidly growing industry with impact in virtually all branches of medicine. Biomanufacturing processes require close monitoring and control, in the presence of complex bioprocess dynamics with many interdependent factors, as well as extremely limited data due to the high cost of experiments and the novelty of personalized bio-drugs. We develop a new model-based reinforcement learning framework that can achieve human-level control in low-data environments. A dynamic Bayesian network is used to capture causal interdependencies between factors and predict how the effects of different inputs propagate through the pathways of the bioprocess mechanisms. This model is interpretable and enables the design of process control policies that are robust against model risk. We present a computationally efficient, provably convergent stochastic gradient method for optimizing such policies. Validation is conducted on a realistic application with a multidimensional, continuous state variable.

Keywords: biomanufacturing; reinforcement learning; policy optimization; Bayesian networks; bioprocess hybrid model (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:35:y:2023:i:1:p:66-82

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