文章基本信息

标题：How to Play in Infinite MDPs (Invited Talk)
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作者：Stefan Kiefer ; Richard Mayr ; Mahsa Shirmohammadi 等
期刊名称：LIPIcs : Leibniz International Proceedings in Informatics
电子版ISSN：1868-8969
出版年度：2020
卷号：168
页码：3:1-3:18
DOI：10.4230/LIPIcs.ICALP.2020.3
出版社：Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
摘要：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 Îµ-optimal strategies may require (possibly infinite) memory. In this paper we consider qualitative objectives: reachability, safety, (co-)BÃ¼chi, 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.
关键词：Markov decision processes