Panda: Reinforcement Learning-Based Priority Assignment for Multi-Processor Real-Time Scheduling

Recently, deep reinforcement learning (RL) technologies have been considered as a feasible solution for tackling combinatorial problems in various research and engineering areas. Motivated by this recent success of RL-based approaches, in this paper, we focus on how to utilize RL technologies in the...

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Vydané v:	IEEE access Ročník 8; s. 185570 - 185583
Hlavní autori:	Lee, Hyunsung, Lee, Jinkyu, Yeom, Ikjun, Woo, Honguk
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Artificial neural networks Coders Combinatorial analysis encoder-decoder neural network Encoders-Decoders Feature extraction global fixed priority scheduling Heuristic algorithms Heuristic task scheduling Machine learning Microprocessors Optimization Priority assignment Processor scheduling Program processors Real time real-time system Real-time systems reinforcement learning Response time Scheduling Task analysis
ISSN:	2169-3536, 2169-3536
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