Using Randomization to Break the Curse of Dimensionality
为有限和无限期界的马尔可夫决策问题引入随机版本的逐次逼近法和多重网格算法，并证明这些算法能打破一类离散决策过程的维度诅咒。
This paper introduces random versions of successive approximations and multigrid algorithms for computing approximate solutions to a class of finite and infinite horizon Markovian decision problems (MDPs). We prove that these algorithms succeed in breaking the curse of dimensionality for a subclass of MDPs known as discrete decision processes (DDPs).