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。
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Method map
The neighbourhood of related methods — select a node to explore.
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来源
- Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093
- 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.
- 动态规划优化↔ compare
- 马尔可夫模型仿真↔ compare
- 蒙特卡洛模拟决策↔ compare
- 随机线性规划仿真↔ compare
- 随机混合整数规划仿真↔ compare
- 随机多目标优化仿真↔ compare