Introduction to Markov decision processes2003HT
Two lectures/exercises; approx 8 h ex each. Takes place in Örebro.
Potentially interesting for all CUGS students, but in particular relevant for those working with systems that need to interact with physical environments or other agents.
The course was last given
To obtain a basic understanding of what a Markov decision process is and how it can be used.
Basic knowledge in probability theory and optimization.
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 a stochastic element. MDPs 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.
Martin L. Puterman: Markov Decision Processes: Discrete Stochastic Dynamic
Programming, Wiley-Interscience, 1994. ISBN: 0471619779.
Lecture notes and articles.
AASS, Örebro university
Schedule: prel October-November, 2003. The course is given in Örebro.
Page responsible: Director of Graduate Studies
Last updated: 2012-05-03