ES-MAML: Simple Hessian-Free Meta Learning
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Publication:6326439
arXiv1910.01215MaRDI QIDQ6326439
Author name not available (Why is that?)
Publication date: 25 September 2019
Abstract: We introduce ES-MAML, a new framework for solving the model agnostic meta learning (MAML) problem based on Evolution Strategies (ES). Existing algorithms for MAML are based on policy gradients, and incur significant difficulties when attempting to estimate second derivatives using backpropagation on stochastic policies. We show how ES can be applied to MAML to obtain an algorithm which avoids the problem of estimating second derivatives, and is also conceptually simple and easy to implement. Moreover, ES-MAML can handle new types of nonsmooth adaptation operators, and other techniques for improving performance and estimation of ES methods become applicable. We show empirically that ES-MAML is competitive with existing methods and often yields better adaptation with fewer queries.
Has companion code repository: https://github.com/google-research/google-research/tree/master/es_maml
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