  Digital twins promise 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 twin's training combines adjoint-based
data assimilation and system identification methods, while the control agent's
training merges model-based optimal control with model-free reinforcement
learning. The control agent evolves along two independent paths: one driven by
model-based optimal control and the other by reinforcement learning. The
digital twin serves as a virtual environment for confrontation and indirect
interaction, functioning as an "expert demonstrator." The best policy is
selected for real-world interaction and cloned to the other path if training
stagnates. We call this framework Reinforcement Twinning (RT). The framework is
tested on three diverse engineering systems and control tasks: (1) controlling
a wind turbine under varying wind speeds, (2) trajectory control of
flapping-wing micro air vehicles (FWMAVs) facing wind gusts, and (3) mitigating
thermal loads in managing cryogenic storage tanks. These test cases use
simplified models with known ground truth closure laws. Results show that the
adjoint-based digital twin training is highly sample-efficient, completing
within a few iterations. For the control agent training, both model-based and
model-free approaches benefit from their complementary learning experiences.
The promising results pave the way for implementing the RT framework on real
systems.
