On Reinforcement Learning and Digital Twins for Intelligent Automation

Abstract: Current trends, such as the fourth industrial revolution and sustainable manufacturing, enable and necessitate manufacturing automation to become more intelligent to meet ever new design requirements in terms of flexibility, speed, quality, and cost. Two distinct research streams towards intelligent manufacturing exist in the scientific literature: the model-based digital twin approach and the data-driven learning approach. Research that incorporates advantages of the one into the other approach is frequently called for. Accordingly, this thesis investigates how machine learning can be used to mitigate the model-system mismatch in digital twins and how prior model-based knowledge can be introduced in reinforcement learning in the context of intelligent automation. In terms of mitigating mismatches in digital twins, research presented in this thesis suggests that learning is of limited usefulness when employed naively in static and systemic mismatch scenarios. In such settings, blackbox optimization algorithms, that leverage properties of the problem, are more useful in terms of sample-efficiency, performance within a given budget, and regret (i.e. when compared to an optimal controller). Learning seems to be of some merit, however, in individualized production control and when used for adapting parameters within a digital twin. An additional research outcome presented in this thesis is a principled method for incorporating prior knowledge in form of automata specifications into reinforcement learning. Furthermore, the benefits of introducing rich prior model-based knowledge in form of economic non-linear model predictive controllers as model class for function approximation in reinforcement learning is demonstrated in the context of energy optimization. Lastly, this thesis highlights that adaptive economic non-linear model predictive control may be understood as a unifying framework for both research streams towards intelligent automation.

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