Searched for: author%3A%22Koryakovskiy%2C+I.%22
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document
Koryakovskiy, I. (author)
Reinforcement learning is an active research area in the fields of artificial intelligence and machine learning, with applications in control. The most important feature of reinforcement learning is its ability to learn without prior knowledge about the system. However, in the real world, reinforcement learning actions may lead to serious damage...
doctoral thesis 2018
document
Kudruss, Manuel (author), Koryakovskiy, I. (author), Vallery, H. (author), Mombaur, Katja (author), Kirches, Christian (author)
Today’s humanoid robots are complex mechanical systems with many degrees of freedom that are built to achieve locomotion skills comparable to humans. In order to synthesize whole-body motions, real-tme capable direct methods of optimal control are a subject of contemporary research. To this end, Nonlinear Model Predictive Control is the method...
report 2018
document
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
document
Koryakovskiy, I. (author), Vallery, H. (author), Babuska, R. (author), Caarls, W. (author)
Reinforcement learning techniques enable robots to deal with their own dynamics and with unknown environments without using explicit models or preprogrammed behaviors. However, reinforcement learning relies on intrinsically risky exploration, which is often damaging for physical systems. In the case of the bipedal walking robot Leo, which is...
journal article 2017
document
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
document
Feirstein (student), D.S. (author), Koryakovskiy, I. (author), Kober, J. (author), Vallery, H. (author)
Reinforcement learning is a powerful tool to derive controllers for systems where no models are available. Particularly policy search algorithms are suitable for complex systems, to keep learning time manageable and account for continuous state and action spaces. However, these algorithms demand more insight into the system to choose a...
conference paper 2016
Searched for: author%3A%22Koryakovskiy%2C+I.%22
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