An Enhanced Proximal Policy Optimization-Based Reinforcement Learning Method with Random Forest for Hyperparameter Optimization

An Enhanced Proximal Policy Optimization-Based Reinforcement Learning Method with Random Forest for Hyperparameter Optimization

For most machine learning and deep learning models, the selection of hyperparameters has a significant impact on the performance of the model. Therefore, deep learning and data analysis experts have to spend a lot of time on hyperparameter tuning when building a model for accomplishing a task. Altho...

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Bibliographic Details
Main Authors: Zhixin Ma, Shengmin Cui, Inwhee Joe
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Applied Sciences
Subjects:
hyperparameter optimization (HPO)
proximal policy optimization
random forest
reinforcement learning
Online Access:https://www.mdpi.com/2076-3417/12/14/7006

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