Course detail

Welcome to Reinforcement Learning

Description:

An intelligent system is expected to generate policies autonomously to achieve a goal, which is mostly to maximize a given reward function. Reinforcement learning is a set of methods in machine learning that can produce such policies. To learn optimal actions in an environment that is not fully comprehensible to itself, an intelligent system can use reinforcement algorithms to leverage its experience to figure out optimal policies. Nowadays, reinforcement learning techniques are successfully applied in various engineering fields, including robotics (DeepMind’s walking robot) and computer-games (AlphaGo and TD-Gammon).

Developed independently from reinforcement learning, dynamic programming and related stochastic optimisation is a set of algorithms that generate policies assuming that the environment is fully comprehensible to the intelligent system. Therefore, dynamic programming provides an essential base to learn reinforcement learning. The course aims at building a fundamental understanding of both methods based on their relations to each other and on their applications to similar problems.

The course consists of the following topics:

Markov decision processes, dynamic programming for infinite time and stopping time, reinforcement learning, safe learning and verification tools for reinforcement learning.

Prerequisites: Basic knowledge of mathematics: calculus and probability

Learning objectives:

- General Introduction to machine learning herein Reinforcement Learning

- Markov Decision Processes and Dynamic Programming

- Reinforcement Learning with Temporal-Difference Learning

- Policy prediction with approximation

- Verification tool UPPAAL for model-based reinforcement learning

Key literature: Richard S. Sutton, Andrew G. Barto, Reinforcement Learning

Organizer: Rafal Wisniewski

Lecturers: Kim Guldstrand Larsen, Zheng-Hua Tan, Rafal Wisniewski, Marius Mikucionis, Abhijit Mazumdar, Rahul Misra

ECTS: 3

Date: 23, 24, 25, 26, 27 March 2026 

Place: TBA

City: Aalborg

Number of seats: 50

Deadline: 2 March 2026

Important information concerning PhD courses:

There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.

We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at phdcourses@adm.aau.dk When contacting us please state the course title and course period. Thank you.