HIGHLIGHTS
- who: Zhixin Ma and colleagues from the (UNIVERSITY) have published the Article: An Enhanced Proximal Policy Optimization-Based Reinforcement Learning Method with Random Forest for Hyperparameter Optimization, in the Journal: (JOURNAL)
- what: The authors propose a random optimization (RFEPPO) reinforcement learning algorithm to solve the HPO problem. The authors use the proposed method to optimize the hyperparameters of extreme gradient boosting (XGBoost) on nine tabular datasets and convolutional neural_network (CNN) on two image datasets. This experiment demonstrates that the method not only shortens the search time on large datasets, but also improves the accuracy and . . .
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