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Course MDP: Markov Decision Processes
Important informationParticipants of this course: please see the lecturers' websiteCourse descriptionnThe theory of Markov decision processes (MDPs) - also known under the names sequential decision theory, stochastic control or stochastic dynamic programming - studies sequential optimization of stochastic systems by controlling their transition mechanism over time. Each control policy defines a stochastic process and values of objective functions associated with this process. The goal is to select a control policy that optimizes a function of the values generated by the utility functions. In real life, decisions that are made usually have two types of impact. Firstly, they cost or save resources, such as money or time. Secondly, by influencing the dynamics of the system they have an impact on the future as well. Therefore, the decision with the largest immediate profit may not be good in view of future rewards in many situations. MDPs model this paradigm and can be used to model many important applications in practice. In this course we provide results on the structure and existence of good policies, on methods for the computation of optimal policies, and illustrate them by applications. Detailed contents
Literature:Lecture notes will be provided.Prerequisites
ExaminationTake home problems and obligatory presentations in the last two weeks of the course (4 and 11 November).Address of the lecturers:
Dr. Aleida Braaksma
Prof. Dr. F.M. Spieksma |