This paper addresses a new method for analyzing gradient-based meta-learning in the online setting, where the average regret-upper-bound analysis was presented. All of reviewers agree that a new theoretical framework in this paper has valuable contributions that can be applied to a variety of setting. While there were no further comments in the discussion period, I believe that the paper is deserved to be presented at NeurIPS.