Dit vak wordt in het Engels aangeboden. Omschrijvingen kunnen daardoor mogelijk alleen in het Engels worden weergegeven.
Doel vakThis course has a threefold objective:
1) Understand the models and methods for sequental decision-theoretic
planning and reinforcement learning (Knowledge and understanding)
2) To understand how to model sequential decision problems using these
models. (Knowledge and understanding)
3) To gain a thorough understaning of the algorithms, and gain hands-on
experience, by performing computational experiments with planning and
reinforcement learning algorithms. (Apply knowledge and understanding)
Inhoud vakThis course will cover basic as well as advanced concepts of
Reinforcement Learning and Decision-Theoretic Planning (also known as
planning under uncertainty).
This course is an introduction to the basic concepts that underlie the
design of autonomous agents in modern artificial intelligence. It
addresses fundamental challenges such as how such agents can maximise
their utility by planning sequences of actions, and learn by trial and
error. In order to do so, these agents must cope with uncertainty about
their environment, and may have to cooperate and/or compete with other
Specific topics covered include: Introduction to Autonomous Agents and
sequential decision problems, Multi-armed Bandits, dynamic programming
Monte Carlo methods (planning and learning), temporal difference
methods, Model-based reinforcement learning, (RL) Learning Theory,
Cooperative Multi-agent planning and learning, Self-interested
Multi-objective planning and learning, and least but not least, Deep
OnderwijsvormOral lectures and compulsory programming assignment (in teams of 3 or
ToetsvormWritten exam and programming assignment (weighted average). To pass the
course as a whole, you must pass both the exam and the programming
Vereiste voorkennisProgramming skills are necessary to do the practical assignment.
LiteratuurSutton and Barto – Introduction to Reinforcement Learning (available
both online, and as printed book)
The slides of the course
Additional papers (TBA during the course)
DoelgroepMSc Business Analytics
MSc Computer Science
MSc Artificial Intelligence
|Faculteit||Faculteit der Bètawetenschappen|
|Vakcoördinator||dr. M. Hoogendoorn|
|Examinator||dr. M. Hoogendoorn|
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