Reinforcement Learning | Neuro-Mechanistic Modeling

Special Topics in Deep Reinforcement Learning

Wed, 01 Apr 2026 00:00:00 +0000

Team

Prof. Dr. Verena Wolf, Timo P. Gros (lecturers)
Magnus Cunow (organization)
Julius Gabelmann, Joshua Meyer, Nicola J. Müller (assistants)

Content

Reinforcement Learning (RL) is a popular sub-discipline of Machine Learning for problems that require strategies to solve complex tasks such as board games, scheduling problems, or other discrete optimization problems. Deep Reinforcement Learning (DRL) is the combination of Reinforcement Learning with Neural Networks.

In this seminar, we first covered the most important algorithms of Deep Reinforcement Learning. Afterwards, we looked at special topics that our group is conducting research on: exploration techniques, agentic RL, and AI planning.

Format

After a kick-off in April, each participant was assigned a paper and presented it in one of two seminar blocks in June, wrote a summary paper about it, and worked on a practical project in a team. The seminar ended with a closing session on the project results, with prizes, and short presentations of the group’s current research for students interested in a Master’s thesis or a HiWi position.

Topics

Deep Reinforcement Learning

Value-based DRL Evolution (core: Rainbow)
Actor-Critic: Structure & Estimation (core: A2C/A3C)
Policy Optimization Stability (core: TRPO & PPO)
Continuous Control in DRL (core: DDPG & SAC)

Planning & RL

Exploration

Agentic RL

Organization

All information and materials are available in the CMS.

Reinforcement Learning

Wed, 01 Oct 2025 00:00:00 +0000

Team

Prof. Dr. Verena Wolf (lecturer)
Lisa Dargasz, Yannic Muskalla (assistants)

Reinforcement Learning

Reinforcement learning is an area of machine learning where the goal is to develop (near-)optimal policies for solving sequential decision-making problems. The policy is typically represented by an agent who learns to achieve a goal by interacting with the environment. RL is often seen as the third area of machine learning (in addition to supervised and unsupervised learning), in which training samples are generated as a result of the agent’s actions and interaction with the environment. In recent years, there have been remarkable successes in reinforcement learning research in both theoretical and applied fields. These successes are mostly the result of a new development in the field: representing policies by artificial neural networks allows us to solve much more complex decision problems.

Course Content

This course provides a broad introduction to reinforcement learning and its applications. You learn about Markov Decision Processes as the underlying formal framework for decision-making problems, as well as popular reinforcement learning algorithms such as Monte-Carlo methods, temporal difference methods, and different “deep” reinforcement learning approaches. We use the open-source Python library Gymnasium to train RL agents in different pre-built environments.

Recommended prerequisites are Programming 1, Programming 2 and basic knowledge of probability theory, as taught for example in Mathematics for Computer Scientists 3.

Format

The course is a 6 ECTS advanced lecture with weekly lectures, bi-weekly assignment sheets with theoretical and programming exercises, tutorials, office hours and a final exam. Submitting the assignment sheets is voluntary. Admission to the final exam requires passing a mid-term exam.

Literature

Richard S. Sutton and Andrew G. Barto: Reinforcement Learning: An Introduction
Csaba Szepesvári: Algorithms for Reinforcement Learning

Organization

All information and materials are available in the CMS.

Special Topics in Deep Reinforcement Learning

Mon, 01 Apr 2024 00:00:00 +0000

Team

Timo P. Gros, Prof. Dr. Verena Wolf (lecturers)
Joschka Groß, Felix Jahn, Nicola J. Müller (assistants)

Content

Topics

Deep Reinforcement Learning

Rainbow (paper)
A2C & A3C (paper)
PPO (paper)
Model-based RL & MCTS (book, especially chapters 8.1, 8.10 and 8.11; original MCTS paper; paper with more details)
Alpha(Go) Zero (paper, paper 2)

Safe Reinforcement Learning

Conservative Safety Critics for Exploration (paper)
Constrained Policy Optimization (paper)
Safe Reinforcement Learning via Shielding (paper)

Causal Reinforcement Learning

Invariant Causal Prediction for Block MDP (paper)
Causal Reinforcement Learning Using Observational and Interventional Data (paper)
Can Humans Be Out of the Loop? (paper)

Exploration Techniques

Random Network Distillation (paper)
Go-Explore (paper)

Organization

All information and materials are available in the CMS.

Reinforcement Learning

Sun, 01 Oct 2023 00:00:00 +0000

Team

Prof. Dr. Verena Wolf (lecturer)
Felix Jahn, Joschka Groß (assistants)

Reinforcement Learning

Course Content

Recommended prerequisites are Programming 1, Programming 2 and basic knowledge of probability theory, as taught for example in Mathematics for Computer Scientists 3.

Format

The course is a 6 ECTS advanced lecture with weekly lectures, bi-weekly assignment sheets with theoretical and programming exercises, bi-weekly tutorials and office hours, and a 90-minute final exam. Admission to the exam requires 50% of the points of the assignment sheets.

Literature

Richard S. Sutton and Andrew G. Barto: Reinforcement Learning: An Introduction
Csaba Szepesvári: Algorithms for Reinforcement Learning

The lecture ran again in Winter 2025/26.

Deep Reinforcement Learning

Sat, 01 Oct 2022 00:00:00 +0000

For details, have a look at the CMS.

MoGym: Using Formal Models for Training and Verifying Decision-making Agents

Sat, 01 Jan 2022 00:00:00 +0000

Deep Reinforcement Learning

Thu, 01 Apr 2021 00:00:00 +0000

Team

Prof. Dr. Verena Wolf
Timo P. Gros, Joschka Groß (assistants)

The course pages of this seminar were on the group’s former CMS, which is no longer online. The seminar ran again in Winter 2022/23.

Rein4cement Learning

Tue, 01 Oct 2019 00:00:00 +0000

Organisation: Prof. Dr. Verena Wolf, Timo P. Gros.

Topic

Reinforcement Learning is — besides Supervised and Unsupervised Learning — one of the outstanding variants of Machine Learning. It has evolved to an effective tool, being able to create AI systems that are able to beat humans in almost every game, e.g. the famous strategy board game Go [2].

This seminar will try to do both: teach the basics of Reinforcement Learning in theory and also provide the ability to apply it. We will therefore first have a look at the state-of-the-art algorithms of Reinforcement Learning and afterwards apply these to the well-known game Connect 4.

Image taken from [1].

The seminar will include a short talk, a programming project that will be solved in small groups, as well as a write-up about both: the topics presented in the talk and the implementation.

Course page

Find all materials and the registration details here.

References

Ahmad Hammoudeh, A Concise Introduction to Reinforcement Learning
David Silver, Julian Schrittwieser, Karen Simonyan et al., Mastering the game of Go without human knowledge