Planning and Reinforcement Learning


Course Objective

This course has a threefold objective:
1) Understand the models and methods for sequental decision-theoretic
planning and reinforcement learning
2) To understand how to model sequential decision problems using these
3) To gain a thorough understaning of the algorithms, and gain hands-on
experience, by performing computational experiments with planning and
reinforcement learning algorithms.

Course Content

This 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, temporal difference methods, Model-based learning,
Prioritised Sweeping, (RL) Learning Theory, Partial observability,
Cooperative Multi-agent learning, Self-interested Multi-agent learning,
Multi-objective planning and learning, and least but not least, Deep
Reinforcement Learning.

Teaching Methods

Oral lectures and compulsory programming assignment (in teams of 3 or
4). There will be two types of assignments: one geared towards AI and CS
students with a focus on performance, and one geared towards BA with a
focus on mathematics.

Highly motivated students can replace the assignment by a special
research track under the personal supervision of the lecturer(s). These
research projects aim at publications.

Method of Assessment

Written exam and programming assignment (weighted average). To pass the
course as a whole, you must pass both the exam and the programming

Entry Requirements

Programming skills are necessary to do the practical assignment.


Sutton and Barto – Introduction to Reinforcement Learning (available
both online, and as printed book)
Additional papers (TBA)

Target Audience

mBA, mAI, mCS

Custom Course Registration

Unfortunately we have to reschedule this course from period 2 to period 5, due to availability of the lecturer. We apologise for any inconvenience. You need to register again, if you want to attend this course in period 5.

General Information

Course Code XM_0055
Credits 6 EC
Period P5
Course Level 400
Language of Tuition English
Faculty Faculty of Science
Course Coordinator dr. D.M. Roijers
Examiner dr. D.M. Roijers
Teaching Staff dr. D.M. Roijers

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Seminar, Lecture
Target audiences

This course is also available as: