Partially Observable Markov Decision Processes (POMDPs) are a fundamental framework for decision-making under uncertainty and partial observability. Since in general optimal policies may require infinite memory, they are hard to implement and often render most problems undecidable. Consequently, finite-memory policies are mostly considered instead. However, the algorithms for computing them are typically very complex, and so are the resulting policies. Facing the need for their explainability, we provide a representation of such policies, both (i) in an interpretable formalism and (ii) typically of smaller size, together yielding higher explainability. To that end, we combine models of Mealy machines and decision trees; the latter describing simple, stationary parts of the policies and the former describing how to switch among them. We design a translation for policies of the finite-state-controller (FSC) form from standard literature and show how our method smoothly generalizes to other variants of finite-memory policies. Further, we identify specific properties of recently used "attractor-based" policies, which allow us to construct yet simpler and smaller representations. Finally, we illustrate the higher explainability in a few case studies.

翻译：部分可观测马尔可夫决策过程（POMDP）是处理不确定性与部分可观测性下决策制定的基础框架。由于最优策略通常可能需要无限记忆，其实现难度高，且常导致多数问题不可判定。因此，实践中多采用有限记忆策略。然而，计算此类策略的算法通常极为复杂，所得策略亦然。面对策略可解释性的需求，我们提出了一种策略表示方法，其兼具（i）可解释的形式化描述与（ii）通常更小的规模，从而共同实现更高的可解释性。为此，我们结合了米利机模型与决策树模型：后者描述策略中简单、静态的部分，前者描述如何在它们之间切换。我们为标准文献中有限状态控制器（FSC）形式的策略设计了一种转换方法，并展示了我们的方法如何自然地推广到其他有限记忆策略的变体。此外，我们识别了近期使用的“吸引子基”策略的特定性质，这使得我们能构建更简洁、更紧凑的表示形式。最后，我们通过若干案例研究展示了更高的可解释性。