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Instructors: Prof. Ph. D. Jan Peters
Event type:
Integrated Course
Org-unit: Dept. 20 - Computer Science
Displayed in timetable as:
RL
Subject:
Crediting for:
Hours per week:
4
Language of instruction:
German and English
Min. | Max. participants:
- | -
Course Contents:
• Review of machine learning background
• Black box Reinforcement Learning
• Modeling as bandit, Markov Decision Processes and Partially Observable Markov Decision Processes
• Optimal control
• System identification
• Learning value functions
• Policy search
• Deep value functions methods
• Deep policy search methods
• Exploration vs exploitation
• Hierarchical reinforcement learning
• Intrinsic motivation
Preconditions:
Good programming in Python.
Lecture Statistical Machine Learning is helpful but not mandatory.
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