Search alternatives:
required optimization » guided optimization (Expand Search), resource optimization (Expand Search), feature optimization (Expand Search)
based optimization » whale optimization (Expand Search)
task required » task requiring (Expand Search), time required (Expand Search), also required (Expand Search)
binary task » binary mask (Expand Search)
tasks based » task based (Expand Search), cases based (Expand Search)
required optimization » guided optimization (Expand Search), resource optimization (Expand Search), feature optimization (Expand Search)
based optimization » whale optimization (Expand Search)
task required » task requiring (Expand Search), time required (Expand Search), also required (Expand Search)
binary task » binary mask (Expand Search)
tasks based » task based (Expand Search), cases based (Expand Search)
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1
Proposed Algorithm.
Published 2025“…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”
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2
Comparisons between ADAM and NADAM optimizers.
Published 2025“…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”
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3
An Example of a WPT-MEC Network.
Published 2025“…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”
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4
Related Work Summary.
Published 2025“…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”
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5
Simulation parameters.
Published 2025“…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”
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6
Training losses for N = 10.
Published 2025“…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”
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7
Normalized computation rate for N = 10.
Published 2025“…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”
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8
Summary of Notations Used in this paper.
Published 2025“…EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”