用强化学习同时校准数字孪生并优化控制策略，提升复杂制造系统的控制效果。
Digital Twin Calibration with Model-Based Reinforcement Learning
- 将数字孪生校准与强化学习结合，同步优化模型和控制策略
- 在生物制药场景中显著降低模型误差，性能优于现有方法
- 适合工业界复杂系统建模与智能控制的科研与工程人员
本文提出一种名为 Actor-Simulator 的新方法框架，将数字孪生校准融入基于模型的强化学习，以更有效地控制具有复杂非线性动力学的随机系统。传统基于模型的控制常依赖于线性状态转移等严格假设，且无法处理模型参数不确定性，这在生物制药制造等工业领域尤为关键——过程动态复杂、信息不全，可用数据有限。本方法联合校准数字孪生并搜索最优控制策略，从而减少模型误差。通过以策略性能为指导进行数据采集，平衡探索与利用。该双组件方法可证明收敛至最优策略，在生物制药制造领域的大量数值实验中表现优于现有方法。
原文摘要 · Abstract (English)
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.
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