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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Vydané v:	Entropy (Basel, Switzerland) Ročník 24; číslo 4; s. 440
Hlavní autori:	Chen, Weimin, Wong, Kelvin Kian Loong, Long, Sifan, Sun, Zhili
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 22.03.2022 MDPI
Predmet:	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
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