Reinforcement Learning 2025
The Technical Doctoral School of IT and Design at Aalborg University
Description: An intelligent system is expected to generate policies autonomously to achieve a goal, which is mostly to maximize a given reward function or minimize a given cost 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 computers playing games (AlphaGo and TD-Gammon).
Developed independently from reinforcement learning, dynamic programming is a set of algorithms in optimal control theory 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, 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: TBA
Organizer: Rafal Wisniewski
Lecturers: Kim Guldstrand Larsen, Zheng-Hua Tan, Rafal Wisniewski, Marius Mikucionis, Abhijit Mazumdar
ECTS: 2.0
Time: 31 March to 4th April 2025
Place: Aalborg University (Room: TBA)
Zip code: 9220
City: Aalborg
Maximal number of participants: 40
Deadline: 10 March 2025