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Reinforcement Learning in Order to Control Biomechanical Models

: Gottschalk, S.; Burger, M.


Faragó, I.:
Progress in Industrial Mathematics at ECMI 2018 : 20th European Conference on Mathematics for Industry, ECMI 2018, Budapest, 18th to 22nd June 2018
Cham: Springer Nature, 2019 (The European Consortium for Mathematics in Industry 30)
ISBN: 978-3-030-27549-5 (Print)
ISBN: 978-3-030-27550-1 (Online)
European Conference on Mathematics for Industry (ECMI) <20, 2018, Budapest>
Fraunhofer ITWM ()

These days, techniques belonging to the research field of Artificial Intelligence (AI) are widely applied and used. Researchers increasingly understand the possibilities and advantages of those techniques for new types of tasks as well as for solving problems which are studied for years and solved by well known solution techniques so far. We focus on Reinforcement Learning (RL) [14] in the context of optimal control problems. We point out the similarities and differences between RL and classical optimal control systems and stress advantages of RL applied to biomechanical systems.