mlsanityData to 14 Oct 2026

DQNAS: Neural Architecture Search using Reinforcement Learning

arXiv 2301.06687 ↗First posted 17 Jan 2023cs.LG cs.AIcited by 4

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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.

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

25 of 57 references.

References · 25

  1. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  2. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,628
  3. 2016Neural Architecture Search with Reinforcement Learningcited by 1,499
  4. 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,136
  5. 2015Prioritized Experience Replaycited by 1,099
  6. 2018Regularized Evolution for Image Classifier Architecture Searchcited by 909
  7. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 837
  8. 2017Progressive Neural Architecture Searchcited by 487
  9. 2016Designing Neural Network Architectures using Reinforcement Learningcited by 279
  10. 2015Net2Net: Accelerating Learning via Knowledge Transfercited by 250
  11. 2018Neural Architecture Search with Bayesian Optimisation and Optimal Transportcited by 195
  12. 2018Auto-Keras: An Efficient Neural Architecture Search Systemcited by 171
13 more
  1. 2016Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasetscited by 132
  2. 2017Genetic CNNcited by 124
  3. 2017A Deeper Look at Experience Replaycited by 105
  4. 2019Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAScited by 83
  5. 2017A Genetic Programming Approach to Designing Convolutional Neural Network Architecturescited by 59
  6. 2018Path-Level Network Transformation for Efficient Architecture Searchcited by 44
  7. 2018IRLAS: Inverse Reinforcement Learning for Architecture Searchcited by 10
  8. 2018EffNet: An Efficient Structure for Convolutional Neural Networkscited by 9
  9. 2018Automatically Designing CNN Architectures for Medical Image Segmentationcited by 8
  10. 2017EDEN: Evolutionary Deep Networks for Efficient Machine Learningcited by 6
  11. 2020Proximal Policy Gradient: PPO with Policy Gradientcited by 5
  12. 2018A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Searchcited by 4
  13. 2018Automatically Evolving CNN Architectures Based on Blockscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2023Reinforcement Learning for Generative AI: A Surveycited by 11
  2. 2024TinyTNAS: GPU-Free, Time-Bound, Hardware-Aware Neural Architecture Search for TinyML Time Series Classificationcited by 1
  3. 2025An Integrated Approach to Neural Architecture Search for Deep Q-Networkscited by 0
  4. 2026Distributional Reinforcement Learning with Information Bottleneck for Uncertainty-Aware DRAM Equalizationcited by 0