ScholarGate
助手
Process / pipelineSimulation / optimization

随机动态规划 — 不确定性下的序贯决策

随机动态规划(Stochastic Dynamic Programming, SDP)是一种用于解决序贯决策问题的数学优化框架,其中结果具有部分随机性。它将贝尔曼最优性原理扩展到随机环境,将问题表示为马尔可夫决策过程(Markov Decision Process, MDP),并通过求解状态和时间段上的递归价值方程来计算最优策略。

在 MethodMind 中打开即将推出视频即将推出Download slides

阅读完整方法

仅限会员

使用免费账户登录即可阅读本节。

登录

Method map

The neighbourhood of related methods — select a node to explore.

+5 more

来源

  1. Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093
  2. Puterman, M. L. (1994). Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, New York. ISBN: 9780471619772

如何引用本页

ScholarGate. (2026, June 3). Stochastic Dynamic Programming (SDP) — Sequential decision-making under uncertainty via Markov decision processes. ScholarGate. https://scholargate.app/zh/simulation/stochastic-dynamic-programming

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

Compare side by side

被引用于

ScholarGateStochastic Dynamic Programming (Stochastic Dynamic Programming (SDP) — Sequential decision-making under uncertainty via Markov decision processes). 于 2026-06-15 检索自 https://scholargate.app/zh/simulation/stochastic-dynamic-programming · 数据集: https://doi.org/10.5281/zenodo.20539026