Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Learn: Meta-Critic Networks for Sample Efficient Learning   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.

36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph