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

arXiv 2112.04084 ↗First posted 8 Dec 2021cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 24 references that are in the map. First posted on arXiv 8 Dec 2021.

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16 of 24 references.

References · 16

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4 more
  1. 2020Improving Generalization in Reinforcement Learning with Mixture Regularizationcited by 81
  2. 2019Hyp-RL : Hyperparameter Optimization by Reinforcement Learningcited by 36
  3. 2017Reinforcement Learning for Learning Rate Controlcited by 16
  4. 2016Using Deep Q-Learning to Control Optimization Hyperparameterscited by 4

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