Relative Entropy of Correct Proximal Policy Optimization Algorithms with Modified Penalty Factor in Complex Environment

In the field of reinforcement learning, we propose a Correct Proximal Policy Optimization (CPPO) algorithm based on the modified penalty factor β and relative entropy in order to solve the robustness and stationarity of traditional algorithms. Firstly, In the process of reinforcement learning, this...

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Veröffentlicht in:	Entropy (Basel, Switzerland) Jg. 24; H. 4; S. 440
Hauptverfasser:	Chen, Weimin, Wong, Kelvin Kian Loong, Long, Sifan, Sun, Zhili
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 22.03.2022 MDPI
Schlagworte:	Algorithms Approximation approximation theory Complexity Convergence correct proximal policy optimization Deep learning Distribution functions Entropy Information theory Iterative methods Kernel functions Machine learning Mathematical models Neural networks Optimization Optimization algorithms policy gradient Random variables reinforcement learning Research methodology Teaching methods approximation theory correct proximal policy optimization entropy optimization policy gradient reinforcement learning
ISSN:	1099-4300, 1099-4300
Online-Zugang:	Volltext
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