论文标题
MAML目标何时有良性景观？
When Does MAML Objective Have Benign Landscape?
论文作者
论文摘要
该论文研究了模型敏锐的元学习（MAML）算法背后优化问题的复杂性。该研究的目的是确定MAML在具有共同结构的顺序决策任务上的全球收敛性。我们很想知道，如果有的话，基本任务的良性景观何时会导致相应的MAML目标的良性景观。为了说明，我们在LQR任务上分析了MAML目标的格局，以确定其结构中哪些类型的相似性使算法能够收敛到全球最佳解决方案。
The paper studies the complexity of the optimization problem behind the Model-Agnostic Meta-Learning (MAML) algorithm. The goal of the study is to determine the global convergence of MAML on sequential decision-making tasks possessing a common structure. We are curious to know when, if at all, the benign landscape of the underlying tasks results in a benign landscape of the corresponding MAML objective. For illustration, we analyze the landscape of the MAML objective on LQR tasks to determine what types of similarities in their structures enable the algorithm to converge to the globally optimal solution.
