可行性确定的动态抽样

2018 IEEE 14th International Conference on Automation Science and Engineering (CASE) Pub Date : 2018-08-01 DOI:10.1109/COASE.2018.8560526

Yijie Peng, Jie Song, Jie Xu, E. Chong

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引用次数: 1

摘要

我们将可行性确定的抽样分配决策表述为贝叶斯环境下的动态策略。这种新的配方解决了以前静态优化配方的局限性。在近似动态规划范例中，我们提出了一种近似最优分配策略，该策略使价值函数的单个特征提前一步最大化。数值结果表明了该方法的有效性。

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Dynamic Sampling for Feasibility Determination

We formulate the sampling allocation decision for feasibility determination as a dynamic policy in a Bayesian setting. This new formulation addresses the limitations of previous static optimization formulation. In an approximate dynamic programming paradigm, we propose an approximately optimal allocation policy that maximizes a single-feature of the value function one-step ahead. Numerical results demonstrate the efficiency of the proposed method.

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2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)

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