Reinforcement Learning @FAU2023

Reinforcement Learning (RL) is an area of Machine Learning that has recently made large advances and has been publicly visible by reaching and surpassing human skill levels in games like Go and Starcraft. These successes show that RL has the potential to transform many areas of research and industry by automatizing the development of processes that once needed to be engineered explicitly.

In contrast to other machine learning paradigms, which require the presence of (labeled or unlabeled) data, RL considers an agent that takes actions in an environment and learns from resulting feedback. The agent maximizes a reward signal that it receives for desirable outcomes, while at the same time trying to explore the world in which it operates to find yet unknown, potentially more rewarding action sequences–a dilemma known as the exploration-exploitation tradeoff. Recent advances in machine learning based on deep learning have made RL methods particularly powerful since they allow for agents with particularly well performing models of the world.

Lecturers & Course Instructors

Christopher Mutschler
(course instructor)
Sebastian Rietsch
(teaching assistant / exercises)
Nico Meyer
(teaching assistant / exercises)

Course Material

We changed the lecture content a bit in this year. Please refer to previous version here (SS2021) and here (SS2022). The lecture will take place on Wednesdays from 8:15 to 9:45 in 11401.00.116 (H14 Bernhard-Ilschner-Hörsaal (0.61), Martensstraße 5-7, 91058 Erlangen, ER-Südgelände).


Week Date Topic Material
1 19.04. Introduction to RL, Markov Decision Processes 01 Intro RL, MDPs.pdf
2 26.04. Dynamic Programming 02 Dynamic Programming.pdf
3 03.05. Model-free Prediction 03 Model-free Prediction.pdf
4 10.05. Model-free Control 04 Model-free Control.pdf
5 17.05. Value Function Approximation, DQNs 05 Value Function Approximation.pdf
6 24.05. Policy-based RL #1 06 Policy-based RL 1.pdf
7 31.05. Policy-based RL #2 07 Policy-based RL 2.pdf
8 07.06. Guest Lecture: Quantum Reinforcement Learning (Nico Meyer, Fraunhofer IIS) 08 Quantum Reinforcement Learning.pdf
9 14.06. Model-based RL #1 (Discrete Actions) 09 Model-based RL 1.pdf
10 21.06. Model-based RL #2 (Continuous Actions) 10 Model-based RL 2.pdf
11 28.06. Exploration-Exploitation, Regret, Bandits 11 Exploration-Exploitation.pdf
12 05.07. Exploration in Deep RL, Intrinsic Motivation (2:07:34)
12.01 Count-based Exploration
12.02 Prediction-based Exploration
12.03 Memory-based Exploration
12 Exploration in Deep RL.pdf
video (32:32)
video (56:25)
video (38:46)
13 12.07. Offline Reinforcement Learning (1:55:22)
13.01 Intro to Offline RL
13.02 Challenges of Offline RL
13.03 Policy-constrained Offline RL
13.04 BEAR
13.05 Conservative Policy Evaluation
13 Offline RL.pdf
video (16:13)
video (32:26)
video (33:37)
video (17:21)
video (15:45)
14 19.07. Guest Lecture: ChatGPT (Georgios Kontes, Fraunhofer IIS)
Course Wrap-Up, Discussion of Evaluation Results, Discussion of latest HW
14 WrapUp.pdf
14 ChatGPT_RL.pdf



The exercises will take place on Fridays from 10:15 to 11:45 in 11401.00.116 (H14 Bernhard-Ilschner-Hörsaal (0.61), Martensstraße 5-7, 91058 Erlangen, ER-Südgelände).

Week Date Topic Material Due Date (discussion of solution on…)
1 no exercises
2 28.04. MDPs (slides) ex1.pdf 28.04.
  28.04. Dynamic Programming (slides) ex2.pdf, 05.05.
3 05.05. OpenAI Gym, TD-Learning (slides) ex3.pdf, 12.05.
4 12.05. TD-Control (slides) ex4.pdf, 19.05.
5 19.05. PyTorch, DQNs (slides) ex5.pdf, (live)

6 26.05. PyTorch, DQNs (slides)
7 02.06. VPG, A2C, PPO (slides) ex7.pdf, 16.06.
8 09.06. VPG, A2C, PPO (slides)
9 16.06. MCTS (slides) ex8.pdf, 23.06.
10 23.06. CEM (slides) ex9.pdf, 30.06.
11 30.06. Multi-armed Bandits (slides) ex10.pdf, 07.07.
12 07.07. RND/ICM (slides) ex11.pdf, 14.07.
13 14.07. BCQ (Nico) ex12.pdf, 19.07. (lecture slot)


Course Evaluation

The evaluation of the lecture and the exercises will be made available here.


  • Richard S. Sutton and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. A Bradford Book, Cambridge, MA, USA. [link]
  • Bellman, R.E. 1957. Dynamic Programming. Princeton University Press, Princeton, NJ. Republished 2003: Dover, ISBN 0-486-42809-5.
  • UC Berkeley CS188: Intro to AI [link]
  • University College London Course on RL [link]
  • Advanced Deep Learning and Reinforcement Learning (UCL + DeepMind) [link]

Interesting talks, articles, and blog-posts:

  • Joelle Pineau: Reproducible, Reusable, and Robust Reinforcement Learning [youtube]
  • David Silver: AlphaGo, AlphaZero, and Deep Reinforcement Learning | AI Podcast #86 with Lex Fridman [youtube]
  • Technion Research: Why does reinforcement learning not work (for you)? [link]
  • RL algorithms quick overview [link]

Code examples and exercises:

  • GitHub Repo of Denny Britz: