Finite-state controllers (FSCs), such as plans with loops, are powerful and compact representations of action selection widely used in robotics, video games and logistics. There has been steady progress on synthesizing FSCs in deterministic environments, but the algorithmic machinery needed for lifting such techniques to stochastic environments is not yet fully understood. While the derivation of FSCs has received some attention in the context of discounted expected reward measures, they are often solved approximately and/or without correctness guarantees. In essence, that makes it difficult to analyze fundamental concerns such as: do all paths terminate, and do the majority of paths reach a goal state? In this paper, we present new theoretical results on a generic technique for synthesizing FSCs in stochastic environments, allowing for highly granular specifications on termination and goal satisfaction.

中文翻译：

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在随机域中用循环合成计划的正确性结果

有限状态控制器 (FSC)，例如带有循环的计划，是广泛用于机器人、视频游戏和物流的动作选择的强大而紧凑的表示。在确定性环境中合成 FSC 方面取得了稳步进展，但尚未完全了解将此类技术提升到随机环境所需的算法机制。虽然 FSC 的推导在折扣预期奖励措施的背景下受到了一些关注，但它们通常可以近似解决和/或没有正确性保证。从本质上讲，这使得分析基本问题变得困难，例如：是否所有路径都终止，以及大多数路径是否达到目标状态？在本文中，我们提出了关于在随机环境中合成 FSC 的通用技术的新理论结果，