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Koryakovskiy, I. (author), Kudruss, M. (author), Vallery, H. (author), Babuska, R. (author), Caarls, W. (author)
Learning-based approaches are suitable for the control of systems with unknown dynamics. However, learning from scratch involves many trials with exploratory actions until a good control policy is discovered. Real robots usually cannot withstand the exploratory actions and suffer damage. This problem can be circumvented by combining learning...
journal article 2018
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Koryakovskiy, I. (author), Kudruss, M. (author), Babuska, R. (author), Caarls, W. (author), Kirches, Christian (author), Mombaur, Katja (author), Schlöder, Johannes P. (author), Vallery, H. (author)
Model-free reinforcement learning and nonlinear model predictive control are two different approaches for controlling a dynamic system in an optimal way according to a prescribed cost function. Reinforcement learning acquires a control policy through exploratory interaction with the system, while nonlinear model predictive control exploits an...
journal article 2017