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: [url]http://incompleteideas.net/book/RLbook2018.pdf[/url]
Preconditions
Good programming in Python.
Lecture Statistical Machine Learning is helpful but not mandatory.
Official Course Description
[b]Motivation:[/b]
"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).
[b]About this course:[/b]
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
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: [url]http://incompleteideas.net/book/RLbook2018.pdf[/url]
Preconditions
Good programming in Python.
Lecture Statistical Machine Learning is helpful but not mandatory.
Official Course Description
[b]Motivation:[/b]
"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).
[b]About this course:[/b]
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
- Lehrende: Georgia Chalvatzaki
- Lehrende: Davide Tateo
Semester: ST 2023