Robust MAML: Prioritization task buffer with adaptive learning process for model-agnostic meta-learning

• URL: http://arxiv.org/abs/2103.08233v1
• Date: Mon, 15 Mar 2021 09:34:34 GMT
• Title: Robust MAML: Prioritization task buffer with adaptive learning process for model-agnostic meta-learning
• Authors: Thanh Nguyen, Tung Luu, Trung Pham, Sanzhar Rakhimkul, Chang D. Yoo
• Abstract summary: Model agnostic meta-learning (MAML) is a popular state-of-the-art meta-learning algorithm. This paper proposes a more robust MAML based on an adaptive learning scheme and a prioritization task buffer. Experimental results on meta reinforcement learning environments demonstrate a substantial performance gain.
• Score: 15.894925018423665
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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