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

• URL: http://arxiv.org/abs/2112.04084v1
• Date: Wed, 8 Dec 2021 02:34:43 GMT
• Title: Hyper-parameter optimization based on soft actor critic and hierarchical mixture regularization
• Authors: Chaoyue Liu, Yulai Zhang
• Abstract summary: 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.
• Score: 5.063728016437489
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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