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Introduction to Markov decision processes

DF17100, 2009VT

Status Archive
School National Graduate School in Computer Science (CUGS)
Division TECH-OU
Owner Lars Karlsson

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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.


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Last updated: 2012-05-03