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• W4226470542 abstract "AbstractMobile edge computing (MEC) has emerged as a new key technology to reduce time delay at the edge of wireless networks, which provides a new solution of distributed computing. But due to the heterogeneity and instability of wireless local area networks, how to obtain a generalized computing offloading strategy is still an unsolved problem. In this research, we deploy a real small-scale MEC system with one edge server and several smart mobile devices and propose a task offloading strategy for one subject device on optimizing time and energy consumption. We formulate the long-term offloading problem as an infinite Markov Decision Process (MDP). Then we use deep Q-learning algorithm to help the subject device to find its optimal offloading decision in the MDP model. Compared with a strategy with fixed parameters, our Q-learning agent shows better performance and higher robustness in a scenario with an unstable network condition. KeywordsMobile edge computingComputation offloadingMarkov Decision ProcessDeep reinforcement learning" @default.
• W4226470542 created "2022-05-05" @default.
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• W4226470542 date "2022-01-01" @default.
• W4226470542 modified "2023-09-25" @default.
• W4226470542 title "A Distributed Computation Offloading Strategy for Edge Computing Based on Deep Reinforcement Learning" @default.
• W4226470542 cites W2086139156 @default.
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• W4226470542 doi "https://doi.org/10.1007/978-3-030-94763-7_6" @default.
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