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Probabilistic Automata-Based Method for Enhancing Performance of Deep Reinforcement Learning Systems

Min Yang Guanjun Liu Ziyuan Zhou Jiacun Wang

自动化学报(英文版)2024,Vol.11Issue(11):2327-2339,13.
自动化学报(英文版)2024,Vol.11Issue(11):2327-2339,13.DOI:10.1109/JAS.2024.124818

Probabilistic Automata-Based Method for Enhancing Performance of Deep Reinforcement Learning Systems

Probabilistic Automata-Based Method for Enhancing Performance of Deep Reinforcement Learning Systems

Min Yang 1Guanjun Liu 1Ziyuan Zhou 1Jiacun Wang2

作者信息

  • 1. Department of Computer Science,Tongji University,Shanghai 201804,China
  • 2. Computer Science and Software Engineering Department,Monmouth University,West Long Branch,NJ 07764 USA
  • 折叠

摘要

关键词

Deep reinforcement learning(DRL)/performan-ce improvement framework/probabilistic automata/real-time moni-toring/the key probabilistic decision-making units(PDMU)-action pair

Key words

Deep reinforcement learning(DRL)/performan-ce improvement framework/probabilistic automata/real-time moni-toring/the key probabilistic decision-making units(PDMU)-action pair

引用本文复制引用

Min Yang,Guanjun Liu,Ziyuan Zhou,Jiacun Wang..Probabilistic Automata-Based Method for Enhancing Performance of Deep Reinforcement Learning Systems[J].自动化学报(英文版),2024,11(11):2327-2339,13.

基金项目

This work was supported by the Shanghai Science and Technology Committee(22511105500),the National Nature Science Found-ation of China(62172299,62032019),the Space Optoelectronic Measurement and Perception Laboratory,Beijing Institute of Control Engineering(LabSOMP-2023-03),and the Central Universities of China(2023-4-YB-05). (22511105500)

自动化学报(英文版)

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