Von Grundlagen zu den tiefen Ansätzen
Course Number: 20-00-1047-iv
Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. RL differs from supervised learning in not needing labelled input/output pairs be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).
General Information: 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 about 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.
Communication: Lectures will be held in English, in-person. For Q&As, please use the Moodle. (We will not record lectures this year. We will upload the slides and additional reading material per lecture and added the recorded lectures of last year.)
List of topics:
- 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
Course Format:
The course is split in two parts: a theoretical and a practical one.
- Every Monday 11:30-13:10, there will be a lecture introducing theory of RL
- On Moodle, you can find practical sessions covering the topics
Requirements:
- Basic knowledge of linear algebra, statistics and probabilities
- Basic programming skills in Python
- Previous registration for the Statistical Machine Learning lecture is helpful, but not mandatory