论文标题
大约针对随机规划的分类空间推断
Approximate Inference for Stochastic Planning in Factored Spaces
论文作者
论文摘要
在大型离散图形模型中，随机规划可以简化为概率推断，但是推理的硬度需要使用近似方案。在本文中，我们认为可以沿着两个维度解开此类应用程序。第一个是理想化的精确优化目标中信息流的方向，即向后推理。第二个是用于计算这一目标的近似类型，例如，信念传播（BP）与平均场变异推理（MFVI）。这种新的分类使我们能够在先前的工作中统一大量孤立的努力，以解释其联系和差异以及潜在的改进。对大型随机计划问题进行广泛的实验评估表明，BP比基于MFVI的几种算法的优势。对MFVI的实际局限性的分析激发了一种新颖的算法，崩溃的状态变异推理（CSVI），该算法提供了更严格的近似值，并通过前向BP实现了可比的计划性能。
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.
