Multi-Agent Systems


Course Objective

After successfully completing this course, the student

- has a solid understanding of concepts from elementary and intermediate
game theory,
such as minimax regret, Nash and correlated equilibria, and the Shapley
- understands various principled approaches to balance exploration and
- has a solid understanding of the tabular solution methods for (single
agent) reinforcement learning;
- is able to explore and digest current research on deep reinforcement
learning and multi-agent reinforcement learning.

Dublin descriptors:
1 Knowledge and Understanding,
2. Applying Knowledge and Understanding:

Course Content

In Multi-agent systems (MAS) one studies collections of interacting,
intelligent agents.
These agents typically can sense both other agents and their
environment, reason about what they perceive, and plan and carry out
actions to achieve specific goals. In this course we introduce a number
of fundamental scientific and engineering concepts that underpin the
theoretical study of such multi-agent systems. In particular, we will
cover the following topics:

- Beliefs, desires, and intentions (BDI)
- Introduction to non-cooperative game theory
- Introduction to coalitional game theory for teams of selfish agents
- Principles of Mechanism Design
- Exploration versus Exploitation
- Markov Decision Processes
- Reinforcement learning for a single agent
- Introduction to multi-agent reinforcement learning

Teaching Methods

Two lectures (1h45) and one recitation class (1h45) per week.

Method of Assessment

There will be weekly homework assignments that will be graded. In
addition, there will be a final exam that will test the student's
ability to apply the course material to new and concrete problems.
The final grade will be a weighted average of the grades for the
homeworks (30%) and the final exam (70%).


Recommended reading:

Yoav Shoham, Kevin Leyton-Brown: Multiagent Systems
Publisher: Cambridge University Press (15 Dec. 2008)
ISBN-10: 0521899435
ISBN-13: 978-0521899437

R.S. Sutton, A.G. Barto, F. Bach: Reinforcement Learning: An
Publisher: MIT Press; second edition edition (23 Nov. 2018)
Language: English
ISBN-10: 0262039249
ISBN-13: 978-0262039246

Recommended background knowledge

Basic probability theory and linear algebra. Fluency in a programming

General Information

Course Code XM_0052
Credits 6 EC
Period P2
Course Level 400
Language of Tuition English
Faculty Faculty of Science
Course Coordinator dr. E.J.E. Pauwels
Examiner dr. E.J.E. Pauwels
Teaching Staff dr. M.C.A. Klein
dr. E.J.E. Pauwels

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Seminar, Lecture
Target audiences

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