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arXiv · 1706.09529

Learning to Learn: Meta-Critic Networks for Sample Efficient Learning

Flood Sung·Li Zhang·Tao Xiang·Timothy Hospedales·Yongxin Yang
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Abstract

We propose a novel and flexible approach to meta-learning for learning-to-learn from only a few examples. Our framework is motivated by actor-critic reinforcement learning, but can be applied to both reinforcement and supervised learning. The key idea is to learn a meta-critic: an action-value function neural network that learns to criticise any actor trying to solve any specified task. For supervised learning, this corresponds to the novel idea of a trainable task-parametrised loss generator. This meta-critic approach provides a route to knowledge transfer that can flexibly deal with few-shot and semi-supervised conditions for both reinforcement and supervised learning. Promising results are shown on both reinforcement and supervised learning problems.

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Flood Sung, Li Zhang, Tao Xiang, Timothy Hospedales, Yongxin Yang. 2017-06-29. Learning to Learn: Meta-Critic Networks for Sample Efficient Learning. https://arxiv.org/abs/1706.09529

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