  Model-agnostic meta-learning (MAML) is a meta-learning technique to train a
model on a multitude of learning tasks in a way that primes the model for
few-shot learning of new tasks. The MAML algorithm performs well on few-shot
learning problems in classification, regression, and fine-tuning of policy
gradients in reinforcement learning, but comes with the need for costly
hyperparameter tuning for training stability. We address this shortcoming by
introducing an extension to MAML, called Alpha MAML, to incorporate an online
hyperparameter adaptation scheme that eliminates the need to tune meta-learning
and learning rates. Our results with the Omniglot database demonstrate a
substantial reduction in the need to tune MAML training hyperparameters and
improvement to training stability with less sensitivity to hyperparameter
choice.
