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

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