论文标题
最佳学习者逆学习的最大似然方法
Maximum Likelihood Methods for Inverse Learning of Optimal Controllers
论文作者
论文摘要
本文提出了一个基于Karush-Kuhn-Tucker（KKT）条件的约束最佳控制问题的目标函数的反向学习框架。我们讨论了与不同模型假设和计算复杂性相对应的三种变体。第一种方法使用KKT条件的凸松弛，并用作基准。本文的主要贡献是将KKT条件与最大似然估计相结合的两种学习方法的命题。这种组合的关键优势是使用可能性参数从分支和结合算法中从嘈杂数据中学习的约束进行系统处理。本文讨论了学习方法的理论特性，并提出了仿真结果，这些结果突出了将最大似然表述用于学习目标函数的优势。
This paper presents a framework for inverse learning of objective functions for constrained optimal control problems, which is based on the Karush-Kuhn-Tucker (KKT) conditions. We discuss three variants corresponding to different model assumptions and computational complexities. The first method uses a convex relaxation of the KKT conditions and serves as the benchmark. The main contribution of this paper is the proposition of two learning methods that combine the KKT conditions with maximum likelihood estimation. The key benefit of this combination is the systematic treatment of constraints for learning from noisy data with a branch-and-bound algorithm using likelihood arguments. This paper discusses theoretic properties of the learning methods and presents simulation results that highlight the advantages of using the maximum likelihood formulation for learning objective functions.
