Kiefer, Stefan
[Author];
Mayr, Richard
[Author];
Shirmohammadi, Mahsa
[Author];
Totzke, Patrick
[Author];
Wojtczak, Dominik
[Author]
;
Stefan Kiefer and Richard Mayr and Mahsa Shirmohammadi and Patrick Totzke and Dominik Wojtczak
[Contributor]
Footnote:
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Description:
Markov decision processes (MDPs) are a standard model for dynamic systems that exhibit both stochastic and nondeterministic behavior. For MDPs with finite state space it is known that for a wide range of objectives there exist optimal strategies that are memoryless and deterministic. In contrast, if the state space is infinite, optimal strategies may not exist, and optimal or ε-optimal strategies may require (possibly infinite) memory. In this paper we consider qualitative objectives: reachability, safety, (co-)Büchi, and other parity objectives. We aim at giving an introduction to a collection of techniques that allow for the construction of strategies with little or no memory in countably infinite MDPs.