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 |
prof. dr. K.V. Hindriks dr. E.J.E. Pauwels |
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Part of programme(s)
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Practical Information
You need to register for this course yourself. The (de)registration deadlines can be found on VU.nl
| Teaching Methods | Seminar, Lecture |
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Target audiences
This course is also available as:
Course Objective
After successfully completing this course, the student
- Has a solid understanding of concepts from elementary and intermediate game theory, such as Nash equilibria for simultaneous games, backward induction and subgame perfect equilibria for sequential games and the Shapley value in cooperative games.
- Understands various principled approaches to balance exploration and exploitation;
- 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, strategic and 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:
- Agents: Typology and examples, strategic versus learning agents
- 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
Additional Information 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 (5 of which are done in groups and 1 is done individually). 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 homework assignments (50%) and the final exam (50%).
There is a resit offered for the final exam and the individual homework assignment. There is no resit for the group homework assignments.
Literature
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 Introduction
Publisher: MIT Press; second edition edition (23 Nov. 2018)
Language: English
ISBN-10: 0262039249
ISBN-13: 978-0262039246
Additional Information Target Audience
Master Artificial Intelligence
Master Business Analytics
Recommended background knowledge
Basic calculus, probability theory and linear algebra. Fluency in a programming language.