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Instructors: Prof. Ph. D. Jan Peters
Event type:
Lecture
Org-unit: Dept. 20 - Computer Science
Displayed in timetable as:
Robot Learning
Subject:
Crediting for:
Hours per week:
4
Language of instruction:
German and English
Min. | Max. participants:
- | -
Course Contents:
- Foundations from robotics and machine learning for robot learning
- Learning of forward models
- Representation of a policy, hierarchical abstraction wiith movement primitives
- Imitation learning
- Optimal control with learned forward models
- Reinforcement learning and policy search
- Inverse reinforcement learning
Literature:
Deisenroth, M. P.; Neumann, G.; Peters, J. (2013). A Survey on Policy Search for Robotics, Foundations and Trends in Robotics
Kober, J; Bagnell, D.; Peters, J. (2013). Reinforcement Learning in Robotics: A Survey, International Journal of Robotics Research
C.M. Bishop, Pattern Recognition and Machine Learning (2006),
R. Sutton, A. Barto. Reinforcement Learning - an Introduction
Nguyen-Tuong, D.; Peters, J. (2011). Model Learning in Robotics: a Survey
Preconditions:
Good programming in Matlab
Lecture Machine Learning 1 - Statistical Approaches is helpful but not mandatory.
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