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Introduction to Markov decision processesDF17100, 2009VT
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Course plan
Lectures
Two lectures/exercises; approx 3h lec + 2 h ex each.
Recommended for
Interesting for all CUGS students, but in particular relevant for those working with systems that need to interact with physical environment or other agents.
The course was last given
Fall 2004
Goals
To obtain a basic understanding of what a Markov decision process is and how it can be used.
Prerequisites
Basic knowledge in probability theory and optimization.
Contents
The course gives an introduction to Markov decision processes, which is a tool
for optimal decision making in situations where the outcomes of the
decision-making system's actions have an element of randomness, MPDs are used
in computer science, control, biology, economics and a number of other fields.
More specifically, the course adresses:
* Markov Chains
* Markov Decision Processes
* Solving MDPs: value iteration and policy iteration
* Partially Observable Markov Decision Processes (POMDPs)
* Applications: CS (planning, learning etc), control, biology, economy, and so
on.
Organization
Intensive course
Literature
Martin L. Puterman: Markov Decision Processes: Discrete Stochastic Dynamic
Programming, Wiley-Interscience, 1994. ISBN: 0471619779.
Lecture notes and articles.
Lecturers
Lars Karlsson
Examiner
Lars Karlsson
Examination
Home assignment.
Credit
4 hp
Organized by
AASS, Örebro university
Comments
Schedule: prel February-April, 2009.
The course will be given in Örebro, and scheduled to suit students that need to
travel.
Page responsible: Director of Graduate Studies
