# DQNAS: Neural Architecture Search using Reinforcement Learning

arXiv: https://arxiv.org/abs/2301.06687 · HTML: https://mlsanity.com/paper/2301.06687/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 57 references that are in the map.
- First posted on arXiv 17 Jan 2023.

## Builds on (most cited first, 25 of 25)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [Progressive Neural Architecture Search](https://mlsanity.com/paper/1712.00559/) (2017)
- [Designing Neural Network Architectures using Reinforcement Learning](https://mlsanity.com/paper/1611.02167/) (2016)
- [Net2Net: Accelerating Learning via Knowledge Transfer](https://mlsanity.com/paper/1511.05641/) (2015)
- [Neural Architecture Search with Bayesian Optimisation and Optimal Transport](https://mlsanity.com/paper/1802.07191/) (2018)
- [Auto-Keras: An Efficient Neural Architecture Search System](https://mlsanity.com/paper/1806.10282/) (2018)
- [Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets](https://mlsanity.com/paper/1605.07079/) (2016)
- [Genetic CNN](https://mlsanity.com/paper/1703.01513/) (2017)
- [A Deeper Look at Experience Replay](https://mlsanity.com/paper/1712.01275/) (2017)
- [Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS](https://mlsanity.com/paper/1912.06059/) (2019)
- [A Genetic Programming Approach to Designing Convolutional Neural Network Architectures](https://mlsanity.com/paper/1704.00764/) (2017)
- [Path-Level Network Transformation for Efficient Architecture Search](https://mlsanity.com/paper/1806.02639/) (2018)
- [IRLAS: Inverse Reinforcement Learning for Architecture Search](https://mlsanity.com/paper/1812.05285/) (2018)
- [EffNet: An Efficient Structure for Convolutional Neural Networks](https://mlsanity.com/paper/1801.06434/) (2018)
- [Automatically Designing CNN Architectures for Medical Image Segmentation](https://mlsanity.com/paper/1807.07663/) (2018)
- [EDEN: Evolutionary Deep Networks for Efficient Machine Learning](https://mlsanity.com/paper/1709.09161/) (2017)
- [Proximal Policy Gradient: PPO with Policy Gradient](https://mlsanity.com/paper/2010.09933/) (2020)
- [A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search](https://mlsanity.com/paper/1812.07995/) (2018)
- [Automatically Evolving CNN Architectures Based on Blocks](https://mlsanity.com/paper/1810.11875/) (2018)

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

- [Reinforcement Learning for Generative AI: A Survey](https://mlsanity.com/paper/2308.14328/) (2023)
- [TinyTNAS: GPU-Free, Time-Bound, Hardware-Aware Neural Architecture Search for TinyML Time Series Classification](https://mlsanity.com/paper/2408.16535/) (2024)
- [An Integrated Approach to Neural Architecture Search for Deep Q-Networks](https://mlsanity.com/paper/2510.19872/) (2025)
- [Distributional Reinforcement Learning with Information Bottleneck for Uncertainty-Aware DRAM Equalization](https://mlsanity.com/paper/2603.04768/) (2026)

## Abstract (arXiv)

Convolutional Neural Networks have been used in a variety of image related applications after their rise in popularity due to ImageNet competition. Convolutional Neural Networks have shown remarkable results in applications including face recognition, moving target detection and tracking, classification of food based on the calorie content and many more. Designing of Convolutional Neural Networks requires experts having a cross domain knowledge and it is laborious, which requires a lot of time for testing different values for different hyperparameter along with the consideration of different configurations of existing architectures. Neural Architecture Search is an automated way of generating Neural Network architectures which saves researchers from all the brute-force testing trouble, but with the drawback of consuming a lot of computational resources for a prolonged period. In this paper, we propose an automated Neural Architecture Search framework DQNAS, guided by the principles of Reinforcement Learning along with One-shot Training which aims to generate neural network architectures that show superior performance and have minimum scalability problem.