When Does MAML Objective Have Benign Landscape?

TL;DR

This paper investigates the conditions under which the MAML optimization landscape is benign, focusing on the global convergence of MAML in structured decision-making tasks like LQR.

Contribution

It provides an analysis of the MAML landscape on LQR tasks, identifying structural similarities that ensure convergence to the global optimum.

Findings

Identifies conditions for benign MAML landscapes in LQR tasks

Analyzes how task structure affects MAML convergence

Provides insights into when MAML can reliably find global optima

Abstract

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.