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Biopharmaceutical manufacturing faces critical challenges, includingcomplexity, high variability, lengthy lead time, and limited historical dataand knowledge of the underlying system stochastic process. To address thesechallenges, we propose a green simulation assisted model-based reinforcementlearning to support process online learning and guide dynamic decision making.Basically, the process model risk is quantified by the posterior distribution.At any given policy, we predict the expected system response with predictionrisk accounting for both inherent stochastic uncertainty and model risk. Then,we propose green simulation assisted reinforcement learning and derive themixture proposal distribution of decision process and likelihood ratio basedmetamodel for the policy gradient, which can selectively reuse processtrajectory outputs collected from previous experiments to increase thesimulation data-efficiency, improve the policy gradient estimation accuracy,and speed up the search for the optimal policy. Our numerical study indicatesthat the proposed approach demonstrates the promising performance.

Green Simulation Assisted Reinforcement Learning with Model Risk for Biomanufacturing Learning and Control