Abstract
In this chapter we will establish the theory of Markov Decision Processes with a finite time horizon and with general state and action spaces. Optimization problems of this kind can be solved by a backward induction algorithm. Since state and action space are arbitrary, we will impose a structure assumption on the problem in order to prove the validity of the backward induction and the existence of optimal policies. The chapter is organized as follows.
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© 2011 Springer-Verlag Berlin Heidelberg
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Bäuerle, N., Rieder, U. (2011). Theory of Finite Horizon Markov Decision Processes. In: Markov Decision Processes with Applications to Finance. Universitext. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18324-9_2
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DOI: https://doi.org/10.1007/978-3-642-18324-9_2
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-18323-2
Online ISBN: 978-3-642-18324-9
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