Search alternatives:
resources optimization » resource optimization (Expand Search), resource utilization (Expand Search), resource utilisation (Expand Search)
based optimization » whale optimization (Expand Search)
data resources » data sources (Expand Search), water resources (Expand Search), data source (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
binary task » binary mask (Expand Search)
task based » risk based (Expand Search)
resources optimization » resource optimization (Expand Search), resource utilization (Expand Search), resource utilisation (Expand Search)
based optimization » whale optimization (Expand Search)
data resources » data sources (Expand Search), water resources (Expand Search), data source (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
binary task » binary mask (Expand Search)
task based » risk 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. …”