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

TL;DR

This paper introduces a green simulation-assisted reinforcement learning framework for biomanufacturing that effectively manages model risk and enhances data efficiency in process control.

Contribution

It proposes a novel model-based reinforcement learning method that quantifies process model risk and reuses simulation data to improve policy learning in biomanufacturing.

Findings

Demonstrates improved policy learning efficiency

Effectively manages model risk in stochastic systems

Shows promising results in numerical studies

Abstract

Biopharmaceutical manufacturing faces critical challenges, including complexity, high variability, lengthy lead time, and limited historical data and knowledge of the underlying system stochastic process. To address these challenges, we propose a green simulation assisted model-based reinforcement learning 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 prediction risk accounting for both inherent stochastic uncertainty and model risk. Then, we propose green simulation assisted reinforcement learning and derive the mixture proposal distribution of decision process and likelihood ratio based metamodel for the policy gradient, which can selectively reuse process trajectory outputs collected from previous experiments to increase the simulation data-efficiency, improve the policy gradient estimation accuracy, and speed up the search for the optimal policy. Our numerical study indicates that the proposed approach demonstrates the promising performance.