Digital Twin Calibration with Model-Based Reinforcement Learning

TL;DR

This paper introduces the Actor-Simulator framework that jointly calibrates digital twins and optimizes control policies using model-based reinforcement learning, improving control accuracy in complex, uncertain systems like biopharmaceutical manufacturing.

Contribution

It proposes a novel joint calibration and control method that accounts for model uncertainty and reduces error, outperforming existing approaches in complex nonlinear systems.

Findings

The approach converges to the optimal policy.

It outperforms existing methods in numerical experiments.

Effective in complex biopharmaceutical manufacturing scenarios.

Abstract

This paper presents a novel methodological framework, called the Actor-Simulator, that incorporates the calibration of digital twins into model-based reinforcement learning for more effective control of stochastic systems with complex nonlinear dynamics. Traditional model-based control often relies on restrictive structural assumptions (such as linear state transitions) and fails to account for parameter uncertainty in the model. These issues become particularly critical in industries such as biopharmaceutical manufacturing, where process dynamics are complex and not fully known, and only a limited amount of data is available. Our approach jointly calibrates the digital twin and searches for an optimal control policy, thus accounting for and reducing model error. We balance exploration and exploitation by using policy performance as a guide for data collection. This dual-component approach provably converges to the optimal policy, and outperforms existing methods in extensive numerical experiments based on the biopharmaceutical manufacturing domain.