بدائل البحث:
required optimization » guided optimization (توسيع البحث), resource optimization (توسيع البحث), feature optimization (توسيع البحث)
based optimization » whale optimization (توسيع البحث)
task required » task requiring (توسيع البحث), time required (توسيع البحث), also required (توسيع البحث)
binary task » binary mask (توسيع البحث)
tasks based » task based (توسيع البحث), cases based (توسيع البحث)
required optimization » guided optimization (توسيع البحث), resource optimization (توسيع البحث), feature optimization (توسيع البحث)
based optimization » whale optimization (توسيع البحث)
task required » task requiring (توسيع البحث), time required (توسيع البحث), also required (توسيع البحث)
binary task » binary mask (توسيع البحث)
tasks based » task based (توسيع البحث), cases based (توسيع البحث)
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1
Proposed Algorithm.
منشور في 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.
منشور في 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.
منشور في 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.
منشور في 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.
منشور في 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.
منشور في 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.
منشور في 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.
منشور في 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. …"