Hyperparameter optimization of neural networks based on Q-learning

Xin Qi, Bing Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


Machine learning algorithms are sensitive to hyperparameters, and hyperparameter optimization techniques are often computationally expensive, especially for complex deep neural networks. In this paper, we use Q-learning algorithm to search for good hyperparameter configurations for neural networks, where the learning agent searches for the optimal hyperparameter configuration by continuously updating the Q-table to optimize hyperparameter tuning strategy. We modify the initial states and termination conditions of Q-learning to improve search efficiency. The experimental results on hyperparameter optimization of a convolutional neural network and a bidirectional long short-term memory network show that our method has higher search efficiency compared with tree of Parzen estimators, random search and genetic algorithm and can find out the optimal or near-optimal hyperparameter configuration of neural network models with minimum number of trials.

Original languageEnglish
JournalSignal, Image and Video Processing
Publication statusAccepted/In press - 2022


  • Hyperparameter optimization
  • Markov decision process
  • Neural networks
  • Q-learning

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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