Deep Reinforcement Learning

Winter semester 2026/27:

First lecture: Tuesday 13.10., 09:15, room A10.375

First exercise: Thursday 15.10., 07:30, room A11.202
Please note: the exercise time differs from the course catalog. The exercise has been moved to 07:30.

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): to be announced

Registration (for exercise computer pool): to be announced

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