  Model agnostic meta-learning (MAML) is a popular state-of-the-art
meta-learning algorithm that provides good weight initialization of a model
given a variety of learning tasks. The model initialized by provided weight can
be fine-tuned to an unseen task despite only using a small amount of samples
and within a few adaptation steps. MAML is simple and versatile but requires
costly learning rate tuning and careful design of the task distribution which
affects its scalability and generalization. This paper proposes a more robust
MAML based on an adaptive learning scheme and a prioritization task buffer(PTB)
referred to as Robust MAML (RMAML) for improving scalability of training
process and alleviating the problem of distribution mismatch. RMAML uses
gradient-based hyper-parameter optimization to automatically find the optimal
learning rate and uses the PTB to gradually adjust train-ing task distribution
toward testing task distribution over the course of training. Experimental
results on meta reinforcement learning environments demonstrate a substantial
performance gain as well as being less sensitive to hyper-parameter choice and
robust to distribution mismatch.
