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An advanced Q-learning algorithm is proposed to address both task offloading and processing challenges, enabling optimal offloading decisions. Simulation ...
Simulation outcomes indicate that the enhanced Q-learning algorithm successfully lowers overall system costs and boosts the service quality in Vehicle Edge ...
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In this paper, to tackle the above challenges, we investigate the problem of computational requests offloading under different vehicular networking scenarios.
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In this paper, a total cost minimization problem with task delay requirement and total computation resources constraints is formulated. Due to the original ...
Reinforcement learning-based computation offloading in edge ...
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We provide a comprehensive survey of RL-based computation offloading fundamental principles and theories in MEC, including mechanisms for finding optimal ...
Vehicular edge computing (VEC) effectively reduces the computational burden on vehicles by offloading tasks from resource-constrained vehicles to edge nodes ...
A deep reinforcement learning (DRL) based offloading method is proposed, which approximates the offloading policy (OP) by a deep neural network (DNN) and ...
Aug 28, 2024 · We design a deep reinforcement learning (DRL) approach to minimize the total cost by applying deep Q-learning algorithm to address the issues of ...
A Deep-Reinforcement-Learning-based computation offloading scheme (DRL-COMV) is proposed, in which some vehicles are deployed and considered as the MESs ...
Article "Q-Learning Based Computation Offloading with Minimizing the Cost in Vehicle Edge Computing" Detailed information of the J-GLOBAL is an information ...