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Lecture SS 23 Selected Topics in Numerical Methods in Science and Technology

Model Predictive Control and Reinforcement Learning

Prof. Jochen Garcke
Tuesday, 14:15 to 16:00
Room 2.035, Friedrich-Hirzebruch-Allee 7
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Model predictive control (MPC) is a method of process control that is used to control a process while satisfying a set of constraints. The models used in MPC are generally intended to represent the behavior of dynamical systems. MPC models predict the change in the dependent variables of the modeled system that will be caused by changes in the independent variables.

In reinforcement learning, we consider a system in interaction with some a priori (at least partially) unknown environment, which learns “from experience’, i.e. the underlying dynamical system is not perfectly known, but its effects have to be approximated during learning. Different from the off-line design of MPC, reinforcement learning is based on the adaptation of on-line data to achieve the purpose of control strategy optimization.

We in particular aim to consider MPC in combination with models learned by data-driven approaches, e.g. inspired by reinforcement learning or based on Koopman operators. MPC also plays a role in reinforcement learning approaches at the planning stage.

Knowledge and understanding of numerical mathematics as taught in the first and second year of the Bachelor is expected and helpful, although not explicitly needed.