Abstract
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.
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Acknowledgements
The authors are grateful for the funding by the Federal Ministry of Education and Research of Germany (BMBF), project number 05M16UKD.
Activations of the Hill’s muscle models
State of one trajectory executed after the training
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Gottschalk, S., Burger, M. (2019). Reinforcement Learning in Order to Control Biomechanical Models. In: Faragó, I., Izsák, F., Simon, P. (eds) Progress in Industrial Mathematics at ECMI 2018. Mathematics in Industry(), vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-27550-1_66
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