At present, designing convolutional neural network (CNN) architectures
requires both human expertise and labor. New architectures are handcrafted by
careful experimentation or modified from a handful of existing networks. We
introduce MetaQNN, a meta-modeling algorithm based on reinforcement learning to
automatically generate high-performing CNN architectures for a given learning
task. The learning agent is trained to sequentially choose CNN layers using
$Q$-learning with an $\epsilon$-greedy exploration strategy and experience
replay. The agent explores a large but finite space of possible architectures
and iteratively discovers designs with improved performance on the learning
task. On image classification benchmarks, the agent-designed networks
(consisting of only standard convolution, pooling, and fully-connected layers)
beat existing networks designed with the same layer types and are competitive
against the state-of-the-art methods that use more complex layer types. We also
outperform existing meta-modeling approaches for network design on image
classification tasks.

使用元建模算法 MetaQNN 根据强化学习自动生成高性能卷积神经网络 (CNN) 结构，可以在图像分类基准测试中击败同层级别的现有网络，并且相比其他网络设计元建模方法具有更好的效果.