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Instructors: Prof. Ph.D. Georgia Chalvatzaki; Ph.D. Davide Tateo
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:
• Markov Decision Process
• Value Functions, Bellman Operator, Policies
• Dynamic Programming
• Monte-Carlo Reinforcement Learning
• Temporal Difference Learning
• Tabular Reinforcement Learning
• Reinforcement Learning with Function Approximation
• Deep Q-Learning
• On-policy and off-policy deep actor-critic
• Model-based Reinforcement Learning
• Intrinsic Motivation
Literature:
Reinforcement Learning: An Introduction, Sutton and Barto, 2nd Edition: http://incompleteideas.net/book/RLbook2018.pdf
Preconditions:
Good programming in Python.
Lecture Statistical Machine Learning is helpful but not mandatory.
Official Course Description:
Motivation:
"The fundamental challenge in artificial intelligence and machine learning is learning to make good decisions under uncertainty," -- Emma Brunskill.
Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents should take actions in an environment to maximize the cumulative rewards. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. RL differs from supervised learning in not needing labeled input/output pairs to be presented and not needing sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (uncharted territory) and exploitation (of current knowledge).
About this course:
This course will take you through the foundation of reinforcement learning methods till recent deep reinforcement learning advances. By the end of this course, you will have a solid knowledge of the field, and you will be able to solve problems with different reinforcement learning algorithms. This course serves as an excellent background for people wanting to carry out reinforcement learning research independently, e.g., within the scope of a Bachelor's or Master's thesis.
Additional Information:
The in-person lecture will be accompanied by an online problem-solving solving session (coding exercises) and Q&A session.
Online Offerings:
moodle
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