101.789 AKNUM Reinforcement Learning
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2024S, VU, 4.0h, 6.0EC


  • Semester hours: 4.0
  • Credits: 6.0
  • Type: VU Lecture and Exercise
  • Format: Hybrid

Learning outcomes

After successful completion of the course, students are able to not only understand, to explain and to apply the theory and the methods of reinforcement learning including the latest developments, but also to implement the most important algorithms.

Subject of course

This year (2022) with updated lecture notes!

Reinforcement learning is a field of artificial intelligence and is concerned with the development of strategies that an agent uses to maximize its reward in a random environment in a model-free manner.

Applications include robotics (OpenAI gym), computer vision, games (such as Go, chess, Atari 2600, or Dota 2) at the human level or above and many more.  Furthermore, RL is instrumental for the working of ChatGPT.

Theory and algorithms of reinforcement learning:

  • Introduction
  • Bandit problems
  • Markov decision problems
  • Bellman equations
  • Hamilton-Jacobi-Bellman equation
  • Dynamic programming
  • Monte-Carlo learning
  • Temporal-difference learning
  • Tabular methods
  • Function approximation and deep learning
  • On-policy vs. off-policy
  • Eligibility traces
  • Policy gradients and actor-critic
  • RL with human feedback: InstructGPT and ChatGPT
  • Applications

In the tutorial, the theory will be repeated and extended and the algorithms will be implemented.

Teaching methods

Presentation, lecture notes, tutorial.

Mode of examination


Additional information

Time for first meeting will be announced.

The class will be taught in presence, streamed, and recorded.



Examination modalities

Continuously in tutorials; written tests.

Course registration

Begin End Deregistration end
04.03.2024 00:00 10.04.2024 00:00 10.04.2024 00:00


Study CodeObligationSemesterPrecon.Info
066 645 Data Science Mandatory elective
860 GW Optional Courses - Technical Mathematics Not specified


 Lecture notes (in English) will be handed out.

Previous knowledge

The theoretical aspects will be explained in the lectures in a self-contained manner so that the course can be taken during or after the fourth semester.



if required in English