Deep Reinforcement Learning
Winter semester 2025-26:
| Lecture (English): | Tuesday, | 9:15 - 10:45, | A10.208, | (O. Maith) |
| Exercise (English): | Friday, | 13:45 - 15:15 | A11.202, | (O. Maith) |
First lecture: Thuesday 14.10.
First exercise: Friday 17.10.
General Information
Suggested prerequisites: Mathematics I to IV, Neurocomputing, basic knowledge in Python.
Exam: written exam (90 minutes), English & German, 5 ECTS.
Contact: oliver.maith@…
Language: English. The exam is in English & German.
Registration (for receiving emails): OPAL
Registration (for exercise computer pool): OPAL
Exam Information
Date: 13.02., 08:00-09:30
Location: Room A10.201 (old 1/201)
Research Paper: doi.org/10.52202/079017-1582
Mock exam: Download PDF
Instructions:
- Abstract: Write an abstract for the provided paper (max. 200 words).
- Print Materials: You must bring the printed abstract and the research paper (main text only, first 11 pages) to the exam.
- Notes: You are allowed to take notes (also printed) on the back of the printed research paper pages.
Backup Plan: As a precaution, please email me your abstract before the exam.
Content
The course will dive into the field of deep reinforcement learning. It starts with the basics of reinforcement learning (Sutton and Barto, 2017) before explaining modern model-free (DQN, DDPG, PPO) and model-based (I2A, World models, AlphaGo) architectures making use of deep neural networks for function approximation. More "exotic" forms of RL are then presented (successor representations, hierarchical RL, inverse RL, etc).
The different algorithms presented during the lectures will be studied in more details during the exercises, through implementations in Python.
Materials
All materials are available at: https://www.tu-chemnitz.de/informatik/KI/edu/deeprl/materials
