部分可观测决策过程建模为概率正则语言生成元的无穷时域最优控制
Optimal Control of Infinite Horizon Partially Observable Decision Processes Modeled As Generators of Probabilistic Regular Languages
摘要：
具有不完全状态反馈的决策过程传统上被建模为部分可观测马尔可夫决策过程。在本文中，我们提出了一个替代配方的基础上概率正则语言。所提出的方法概括了最近报道的工作语言测量理论最优控制完全可观察的情况下，并表明这样的框架是更容易计算的经典替代。特别是，我们表明，部分观察下的无限地平线判定问题，在建议的框架中建模，是$ε$ -近似的，一般来说，是不难解决的完全可观察的情况下相比。通过两个简单的例子来说明这种方法。
英文摘要：
Decision processes with incomplete state feedback have been traditionally modeled as Partially Observable Markov Decision Processes. In this paper, we present an alternative formulation based on probabilistic regular languages. The proposed approach generalizes the recently reported work on language measure theoretic optimal control for perfectly observable situations and shows that such a framework is far more computationally tractable to the classical alternative. In particular, we show that the infinite horizon decision problem under partial observation, modeled in the proposed framework, is $ε$-approximable and, in general, is no harder to solve compared to the fully observable case. The approach is illustrated via two simple examples.
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