Accuracy-Time Efficient Hyperparameter Optimization Using Actor-Critic-based Reinforcement Learning and Early Stopping in OpenAI Gym Environment

Albert Budi Christian, Chih Yu Lin, Yu Chee Tseng, Lan Da Van, Wan Hsun Hu, Chia Hsuan Yu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this paper, we present accuracy-time efficient hyperparameter optimization (HPO) using advantage actor-critic (A2C)-based reinforcement learning (RL) and early stopping in OpenAI Gym environment. The A2C RL can improve the hyperparameter selection such that the resulting accuracy of machine learning (ML) algorithms including XGBoost, support vector classifier (SVC), random forest shows comparable. According to the specified accuracy of the ML algorithms, the early stopping scheme can save the computation cost. Ten standard datasets are used to valid the accuracy-time efficient HPO. Experimental results show that the presented accuracy-efficient HPO architecture can improve 0.77% accuracy on average compared with default hyperparameter for random forest. The early stopping can save 64% computation cost on average compared to without early stopping for random forest.

Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages230-234
Number of pages5
ISBN (Electronic)9798350396454
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022 - Virtual, Online, Indonesia
Duration: 24 Nov 202226 Nov 2022

Publication series

NameProceedings of the 2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022

Conference

Conference2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022
Country/TerritoryIndonesia
CityVirtual, Online
Period24/11/2226/11/22

Keywords

  • Accuracy-time efficiency
  • Actor-Critic
  • early stopping
  • Hyperparameter optimization
  • Reinforcement learning

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