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Hyper-parameter optimization based on soft actor critic and hierarchical mixture regularization
[article]
2021
arXiv
pre-print
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
arXiv:2112.04084v1
fatcat:u3hgnr3vgrabhobuxoco6hj424