MDP modeling for multi-stage stochastic programs
研究了一类融合马氏决策过程特征的多阶段随机规划模型，扩展了策略图以处理决策依赖的不确定性和统计学习，并提出了随机对偶动态规划的新变体作为求解方法。
Abstract We study a class of multi-stage stochastic programs, which incorporate modeling features from Markov decision processes (MDPs). This class includes structured MDPs with continuous action and state spaces. We extend policy graphs to include decision-dependent uncertainty for one-step transition probabilities as well as a limited form of statistical learning. We focus on the expressiveness of our modeling approach, illustrating ideas with a series of examples of increasing complexity. As a solution method, we develop new variants of stochastic dual dynamic programming, including approximations to handle non-convexities.