贝叶斯控制规则的收敛性
Convergence of Bayesian Control Rule
摘要：
最近，自适应控制的新方法试图将问题重新表述为相对熵准则的最小化，以获得易于处理的解决方案。特别是，它已被证明，最大限度地减少预期偏差的因果输入-输出依赖的真实工厂导致一个新的有前途的随机控制规则称为贝叶斯控制规则。这项工作证明了收敛的贝叶斯控制规则下两个充分的假设：有界性，这是一个遍历性条件;和一致性，这是一个实例的肯定的事情原则。
英文摘要：
Recently, new approaches to adaptive control have sought to reformulate the problem as a minimization of a relative entropy criterion to obtain tractable solutions. In particular, it has been shown that minimizing the expected deviation from the causal input-output dependencies of the true plant leads to a new promising stochastic control rule called the Bayesian control rule. This work proves the convergence of the Bayesian control rule under two sufficient assumptions: boundedness, which is an ergodicity condition; and consistency, which is an instantiation of the sure-thing principle.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
