Deep Reinforcement Learning

News

09.11.2026: Teaching evaluation on 24.11. and 26.11. The teaching evaluation of this course is held in the lecture on Tuesday 24.11. (for the lecture) and in the exercise on Thursday 26.11. (for the exercise). Each time, it takes 15–20 minutes of the session. You answer an online questionnaire via a link or QR code shown in the session, so please bring a smartphone, tablet or laptop. Taking part is voluntary. In the free-text fields, please write nothing that could identify you and nothing about anyone other than the lecturer: the faculty checks the answers and blacks out such details before the lecturer sees the results.

Winter semester 2026/27:

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

First exercise: Thursday 15.10., 07:30, room A11.202

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

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