mlsanityData to 14 Oct 2026

A Survey on Neural Architecture Search Based on Reinforcement Learning

arXiv 2409.18163 ↗First posted 26 Sep 2024cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

The automation of feature extraction of machine learning has been successfully realized by the explosive development of deep learning. However, the structures and hyperparameters of deep neural network architectures also make huge difference on the performance in different tasks. The process of exploring optimal structures and hyperparameters often involves a lot of tedious human intervene. As a result, a legitimate question is to ask for the automation of searching for optimal network structures and hyperparameters. The work of automation of exploring optimal hyperparameters is done by Hyperparameter Optimization. Neural Architecture Search is aimed to automatically find the best network structure given specific tasks. In this paper, we firstly introduced the overall development of Neural Architecture Search and then focus mainly on providing an overall and understandable survey about Neural Architecture Search works that are relevant with reinforcement learning, including improvements and variants based on the hope of satisfying more complex structures and resource-insufficient environment.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 16 of its 26 references that are in the map. First posted on arXiv 26 Sep 2024.

Builds on

16 of 26 references.

References · 16

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018DARTS: Differentiable Architecture Searchcited by 1,798
  3. 2016Neural Architecture Search with Reinforcement Learningcited by 1,499
  4. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,101
  5. 2018MnasNet: Platform-Aware Neural Architecture Search for Mobilecited by 943
  6. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 837
  7. 2017Progressive Neural Architecture Searchcited by 487
  8. 2017Large-Scale Evolution of Image Classifierscited by 348
  9. 2016Designing Neural Network Architectures using Reinforcement Learningcited by 279
  10. 2019Random Search and Reproducibility for Neural Architecture Searchcited by 254
  11. 2015Net2Net: Accelerating Learning via Knowledge Transfercited by 250
  12. 2018Path-Level Network Transformation for Efficient Architecture Searchcited by 44
4 more
  1. 2018BlockQNN: Efficient Block-wise Neural Network Architecture Generationcited by 31
  2. 2017DeepArchitect: Automatically Designing and Training Deep Architecturescited by 28
  3. 2018Resource-Efficient Neural Architectcited by 14
  4. 2021Improving the sample-efficiency of neural architecture search with reinforcement learningcited by 2

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  1. 2024Scalable Reinforcement Learning-based Neural Architecture Search
  2. 2024Evolution and Efficiency in Neural Architecture Search: Bridging the Gap Between Expert Design and Automated Optimization
  3. 2025The M-factor: A Novel Metric for Evaluating Neural Architecture Search in Resource-Constrained Environments
  4. 2024Combining Automated Optimisation of Hyperparameters and Reward Shape
  5. 2025An Integrated Approach to Neural Architecture Search for Deep Q-Networks
  6. 2021AUTOKD: Automatic Knowledge Distillation Into A Student Architecture Family
  7. 2026Bilevel Optimization for Neural Architecture Search
  8. 2018IRLAS: Inverse Reinforcement Learning for Architecture Search