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Finite Horizon Markov Decision Problems

  • Thomas Ondra
Chapter
Part of the BestMasters book series (BEST)

Abstract

In this chapter we solve finite horizon Markov decision problems. We are describing a policy evaluation algorithm and the Bellman equations, which are necessary and sufficient optimality conditions for Markov decision problems. Then we are constructing optimal policies out of the solution of the Bellman equations. We will see that the class of Markov deterministic policies —that are easier to handle—contain, under assumptions which are often satisfied in practise, optimal policies. Finally, we describe how optimal policies can be calculated, based on a backward induction algorithm. This chapter is based on [Put94], [Whi93], and [Der70].

Keywords

Optimal Policy Bellman Equation Induction Algorithm Card Game Total Reward 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Fachmedien Wiesbaden 2015

Authors and Affiliations

  1. 1.Medical University of ViennaViennaAustria

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