多环境MDP中几乎确定Rabin目标的PSPACE算法
A PSPACE Algorithm for Almost-Sure Rabin Objectives in Multi-Environment MDPs
AI总结：
针对多环境MDP中寻找满足所有关联MDP目标的单一策略问题，证明了几乎确定Rabin目标问题为PSPACE完全，厘清其复杂度并与POMDP相关结果形成对比。
摘要：
马尔可夫决策过程（MDPs）用于建模具有不确定转移动力学的系统。多环境MDP（MEMDPs）是MDP的扩展，直观上代表共享相同状态和动作空间、但转移动力学不同的有限MDP集合。MEMDP的核心目标是找到单一策略，使其在所有关联的MDP中都满足给定目标。本文的主要结果是证明MEMDP中几乎确定Rabin目标的问题是PSPACE完全的。该结果厘清了MEMDP的复杂度图景，且与更通用的部分可观测MDP（POMDPs）的结果形成对比：在POMDP中，几乎确定可达性已是EXPTIME完全，而几乎确定Rabin目标是不可判定的。
英文摘要：
Markov Decision Processes (MDPs) model systems with uncertain transition dynamics. Multiple-environment MDPs (MEMDPs) extend MDPs. They intuitively reflect finite sets of MDPs that share the same state and action spaces but differ in the transition dynamics. The key objective in MEMDPs is to find a single policy that satisfies a given objective in every associated MDP. The main result of this paper is PSPACE-completeness for almost-sure Rabin objectives in MEMDPs. This result clarifies the complexity landscape for MEMDPs and contrasts with results for the more general class of partially observable MDPs (POMDPs), where almost-sure reachability is already EXPTIME-complete, and almost-sure Rabin objectives are undecidable.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
