  Many (but not all) approaches self-qualifying as "meta-learning" in deep
learning and reinforcement learning fit a common pattern of approximating the
solution to a nested optimization problem. In this paper, we give a
formalization of this shared pattern, which we call GIMLI, prove its general
requirements, and derive a general-purpose algorithm for implementing similar
approaches. Based on this analysis and algorithm, we describe a library of our
design, higher, which we share with the community to assist and enable future
research into these kinds of meta-learning approaches. We end the paper by
showcasing the practical applications of this framework and library through
illustrative experiments and ablation studies which they facilitate.
