论文标题
贝叶斯优化和顺序估计的推出算法和近似动态编程
Rollout Algorithms and Approximate Dynamic Programming for Bayesian Optimization and Sequential Estimation
论文作者
论文摘要
我们提供了一个统一的近似动态编程框架，该框架适用于涉及顺序估计的各种问题。我们首先考虑出于优化的目的构建代孕成本功能，并使用推出算法及其某些变体专注于贝叶斯优化的特殊情况。然后，我们使用最佳测量选择及其在随机和自适应控制问题上的应用，讨论随机矢量进行顺序估算的更一般情况。我们区分确定性和随机系统的自适应控制：前者更适合使用推出，而后者非常适合使用肯定的等效性近似值的推出。作为确定性案例的一个例子，我们讨论了顺序解码问题，以及用于文字和策划拼图的近似解决方案的推出算法，该算法最近在本文[BBB22]中开发。
We provide a unifying approximate dynamic programming framework that applies to a broad variety of problems involving sequential estimation. We consider first the construction of surrogate cost functions for the purposes of optimization, and we focus on the special case of Bayesian optimization, using the rollout algorithm and some of its variations. We then discuss the more general case of sequential estimation of a random vector using optimal measurement selection, and its application to problems of stochastic and adaptive control. We distinguish between adaptive control of deterministic and stochastic systems: the former are better suited for the use of rollout, while the latter are well suited for the use of rollout with certainty equivalence approximations. As an example of the deterministic case, we discuss sequential decoding problems, and a rollout algorithm for the approximate solution of the Wordle and Mastermind puzzles, recently developed in the paper [BBB22].
