Process / pipelineSimulation / optimization
贝叶斯动态规划 — 采用贝叶斯信念更新的序贯决策优化
贝叶斯动态规划（Bayesian Dynamic Programming, BDP）将贝尔曼的动态规划框架与贝叶斯推理相结合，用于在转移概率或奖励结构未知的情况下优化序贯决策。在每个阶段，智能体利用观测到的结果更新对环境的信念，然后计算一个最优策略，该策略明确地考虑了即时奖励和通过探索获得的信息价值。
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来源
- Bertsekas, D. P. (1995). Dynamic Programming and Optimal Control. Athena Scientific, Belmont, MA. ISBN: 9781886529267
- Duff, M. O. (2002). Optimal Learning: Computational procedures for Bayes-adaptive Markov decision processes. PhD Dissertation, University of Massachusetts Amherst. link ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Dynamic Programming — Sequential decision optimization under uncertainty with Bayesian belief updating. ScholarGate. https://scholargate.app/zh/simulation/bayesian-dynamic-programming
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