基于策略迭代算法的逆方法
An Inverse Method for Policy-Iteration Based Algorithms
摘要：
我们提出了一个扩展的两个策略迭代算法的加权图（即，马尔可夫决策问题和最大加代数）。这种扩展允许我们解决以下逆问题：考虑图的权重是未知的常数或参数，我们假设这些权重的参考实例，我们的目标是计算参数的约束，在该约束下，参考实例的最优策略仍然是最优的。因此，保证原始算法在引用实例化周围表现良好，这为我们提供了一些鲁棒性标准。我们将这两种方法应用于简单的示例。原型实现已经完成。
英文摘要：
We present an extension of two policy-iteration based algorithms on weighted graphs (viz., Markov Decision Problems and Max-Plus Algebras). This extension allows us to solve the following inverse problem: considering the weights of the graph to be unknown constants or parameters, we suppose that a reference instantiation of those weights is given, and we aim at computing a constraint on the parameters under which an optimal policy for the reference instantiation is still optimal. The original algorithm is thus guaranteed to behave well around the reference instantiation, which provides us with some criteria of robustness. We present an application of both methods to simple examples. A prototype implementation has been done.
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。
