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

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arxiv 1706.09529 v1 pith:FP57WBZ3 submitted 2017-06-29 cs.LG

Flood Sung , Li Zhang , Tao Xiang , Timothy Hospedales , Yongxin Yang This is my paper · ORCID
classification cs.LG
keywords learningreinforcementsupervisedmeta-criticapproachidealearnnovel
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving

    cs.CL 2025-04 conditional novelty 6.0 of 10

    A self-training method that makes an LLM write an abstract plan before each answer improves average test accuracy by about 3 points over three self-training baselines on four reasoning benchmarks.

  2. Relation-Aware Meta-Learning for Zero-shot Sketch-Based Image Retrieval

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  3. Adaptive Policy Backbone via Shared Network

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    Adapting only linear layers before and after a frozen shared backbone is enough to transfer to out-of-distribution continuous-control tasks, with a theoretical argument and MuJoCo experiments.

  4. Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review

    cs.LG 2025-05 conditional novelty 4.0 of 10

    A survey that organizes combinations of Bayesian inference and reinforcement learning, rates them on four properties, and raises ten open questions.

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