  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.
