Stochastic planning can be reduced to probabilistic inference in large discrete graphical models, but hardness of inference requires approximation schemes to be used. In this paper we argue that such applications can be disentangled along two dimensions. The first is the direction of information flow in the idealized exact optimization objective, i.e., forward vs backward inference. The second is the type of approximation used to compute this objective, e.g., Belief Propagation (BP) vs mean field variational inference (MFVI). This new categorization allows us to unify a large amount of isolated efforts in prior work explaining their connections and differences as well as potential improvements. An extensive experimental evaluation over large stochastic planning problems shows the advantage of forward BP over several algorithms based on MFVI. An analysis of practical limitations of MFVI motivates a novel algorithm, collapsed state variational inference (CSVI), which provides a tighter approximation and achieves comparable planning performance with forward BP.

翻译：在大型离散图形模型中,可以将斯托孔规划缩减为概率推断,但推断的严谨性要求使用近似方法。在本文中,我们争论说,这种应用可以分解为两个方面。首先,在理想化精确优化目标中的信息流动方向,即前向和后向推断;第二,用来计算这个目标的近似类型,例如,信仰促进(BP)相对于平均外地变异推断值。这一新分类使我们能够在解释其联系和差异以及潜在改进的先前工作中,将大量孤立的努力统一起来。对大型模拟规划问题进行的广泛实验评估表明,远方BP相对于基于MFVI的几种算法的优势。对MFVI的实际局限性的分析可以激发一种新的算法、崩溃的状态变异推法(CSVI),这种算法提供更近似的近似率,并实现与前方BP的可比的规划性。