"Mathematical Reinforcement Learning for AI"

Winter Term 2026/2027

Lecturer: Dr. Dave Jacobi

• Time and place: Block seminar on 

Prerequisites: Stochastics I and II. Desirable: Stochastics III.
Target Group: BMS Students, Master students of Mathematics and advanced Bachelor students of Mathematics.

• FU-Students only need to register via Campus Management.
• Non-FU-students are required to register via Whiteboard.

Contents: The seminar covers advanced topics of stochastics.

Reinforcement learning lies at the core of many state-of-the-art artificial intelligence algorithms, enabling agents to solve complex optimal control tasks in robotics, finance, physical AI, drug discovery, computer games and many other applications.
This seminar offers a rigorous treatment of reinforcement learning, focusing on the mathematical principles that make reinforcement learning algorithms work. We will develop a mathematically sound understanding of Markov decision processes, value function based methods and their connections to stochastic optimal control, policy gradient methods, emphasizing convergence properties of classical reinforcement learning algorithms through stochastic approximation and stochastic gradient descent. We will also explore continuous time reinforcement learning in the framework of stochastic differential equations.
The seminar aims to provide a rigorous foundational perspective for students interested in current research related to reinforcement learning and artificial intelligence. Participants should have a strong background in mathematics specifically in probability theory.

Talks

Literature: will be announced in the preliminary discussion