Hyper-parameter optimization based on soft actor critic and hierarchical mixture regularization

Hyper-parameter optimization is a crucial problem in machine learning as it aims to achieve the state-of-the-art performance in any model. Great efforts have been made in this field, such as random search, grid search, Bayesian optimization. In this paper, we model hyper-parameter optimization proce...

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Bibliographic Details
Main Authors Liu, Chaoyue, Zhang, Yulai
Format Journal Article
LanguageEnglish
Published 07.12.2021
Subjects
Computer Science - Artificial Intelligence
Computer Science - Learning
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Summary:Hyper-parameter optimization is a crucial problem in machine learning as it aims to achieve the state-of-the-art performance in any model. Great efforts have been made in this field, such as random search, grid search, Bayesian optimization. In this paper, we model hyper-parameter optimization process as a Markov decision process, and tackle it with reinforcement learning. A novel hyper-parameter optimization method based on soft actor critic and hierarchical mixture regularization has been proposed. Experiments show that the proposed method can obtain better hyper-parameters in a shorter time.
DOI:10.48550/arxiv.2112.04084