Reinforcement Twinning: From digital twins to model-based reinforcement learning

The concept of digital twins promises to revolutionize engineering by offering new avenues for optimization, control, and predictive maintenance. We propose a novel framework for simultaneously training the digital twin of an engineering system and an associated control agent. The training of the twin combines methods from adjoint-based data assimilation and system identification, while the training of the control agent combines model-based optimal control and model-free reinforcement learning. The training of the control agent is achieved by letting it evolve independently along two paths: one driven by a model-based optimal control and another driven by reinforcement learning. The virtual environment offered by the digital twin is used as a playground for confrontation and indirect interaction. This interaction occurs as an “expert demonstrator”, where the best policy is selected for the interaction with the real environment and “cloned” to the other if the independent training stagnates. We refer to this framework as Reinforcement Twinning (RT). The framework is tested on three vastly different engineering systems and control tasks, namely (1) the control of a wind turbine subject to time-varying wind speed, (2) the trajectory control of flapping-wing micro air vehicles (FWMAVs) subject to wind gusts, and (3) the mitigation of thermal loads in the management of cryogenic storage tanks. The test cases are implemented using simplified models for which the ground truth on the closure law is available. The results show that the adjoint-based training of the digital twin is remarkably sample-efficient and completed within a few iterations. Concerning the control agent training, the results show that the model-based and the model-free control training benefit from the learning experience and the complementary learning approach of each other. The encouraging results open the path towards implementing the RT framework on real systems.