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Efficient hyperparameter optimization through model-based reinforcement learning
بهینه سازی ابرپارامتر کارآمد از طریق یادگیری تقویتی مبتنی بر مدل-2020 Hyperparameter tuning is critical for the performance of machine learning algorithms. However, a noticeable
limitation is the high computational cost of algorithm evaluation for complex models or for large
datasets, which makes the tuning process highly inefficient. In this paper, we propose a novel modelbased
method for efficient hyperparameter optimization. Firstly, we frame this optimization process as
a reinforcement learning problem and then employ an agent to tune hyperparameters sequentially. In
addition, a model that learns how to evaluate an algorithm is used to speed up the training. However,
model inaccuracy is further exacerbated by long-term use, resulting in collapse performance. We propose
a novel method for controlling the model use by measuring the impact of the model on the policy and
limiting it to a proper range. Thus, the horizon of the model use can be dynamically adjusted. We apply
the proposed method to tune the hyperparameters of the extreme gradient boosting and convolutional
neural networks on 101 tasks. The experimental results verify that the proposed method achieves the
highest accuracy on 86.1% of the tasks, compared with other state-of-the-art methods and the average
ranking of runtime is significant lower than all methods by using the predictive model. Keywords: Hyperparameter optimization | Machine learning | Reinforcement learning |
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