# A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search

arXiv: https://arxiv.org/abs/1812.07995 · HTML: https://mlsanity.com/paper/1812.07995/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2018.

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Survey on Automated Machine Learning for Recommendations](https://mlsanity.com/paper/2204.01390/) (2022)
- [DQNAS: Neural Architecture Search using Reinforcement Learning](https://mlsanity.com/paper/2301.06687/) (2023)
- [Multifidelity Reinforcement Learning with Control Variates](https://mlsanity.com/paper/2206.05165/) (2022)
- [CRC-RL: A Novel Visual Feature Representation Architecture for Unsupervised Reinforcement Learning](https://mlsanity.com/paper/2301.13473/) (2023)

## Abstract (arXiv)

Deep Neural networks are efficient and flexible models that perform well for a variety of tasks such as image, speech recognition and natural language understanding. In particular, convolutional neural networks (CNN) generate a keen interest among researchers in computer vision and more specifically in classification tasks. CNN architecture and related hyperparameters are generally correlated to the nature of the processed task as the network extracts complex and relevant characteristics allowing the optimal convergence. Designing such architectures requires significant human expertise, substantial computation time and doesn't always lead to the optimal network. Model configuration topic has been extensively studied in machine learning without leading to a standard automatic method. This survey focuses on reviewing and discussing the current progress in automating CNN architecture search.