Robustness Assessment of Asynchronous Advantage Actor-Critic Based on Dynamic Skewness and Sparseness Computation: A Parallel Computing View

Reinforcement learning as autonomous learning is greatly driving artificial intelligence (AI) development to practical applications. Having demonstrated the potential to significantly improve synchronously parallel learning, the parallel computing based asynchronous advantage actor-critic (A3C) open...

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Vydané v:	Journal of computer science and technology Ročník 36; číslo 5; s. 1002 - 1021
Hlavní autori:	Chen, Tong, Liu, Ji-Qiang, Li, He, Wang, Shuo-Ru, Niu, Wen-Jia, Tong, En-Dong, Chang, Liang, Chen, Qi Alfred, Li, Gang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Singapore Springer Singapore 01.10.2021 Springer Springer Nature B.V Beijing Key Laboratory of Security and Privacy in Intelligent Transportation,Beijing Jiaotong University Beijing 100044,China%Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin 541004,China%Donald Bren School of Information and Computer Sciences,University of California,Irvine 92697,U.S.A.%Centre for Cyber Security Research and Innovation,Deakin University,Geelong,VIC 3216,Australia
Predmet:	Algorithms Artificial Intelligence Computer Science Data Structures and Information Theory Information Systems Applications (incl.Internet) Machine learning Regular Paper Robustness (mathematics) Skewness Software Engineering Theory of Computation sparseness skewness robustness assessment asynchronous advantage actor-critic reinforcement learning
ISSN:	1000-9000, 1860-4749
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