方法证据记录
Bayesian Dynamic Programming
Bayesian Dynamic Programming (BDP) combines Bellman's dynamic programming framework with Bayesian inference to optimize sequential decisions when transition probabilities or reward structures are unknown. At each stage, the agent updates beliefs about the environment using observed outcomes, then computes an optimal policy that explicitly accounts for both immediate rewards and the value of information gained through exploration.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Bayesian Dynamic Programming — Sequential decision optimization under uncertainty with Bayesian belief updating
分类方法记录 · process-pipeline / simulation
- 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. · URL
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