بدائل البحث:
policy optimization » topology optimization (توسيع البحث), wolf optimization (توسيع البحث), process optimization (توسيع البحث)
design optimization » bayesian optimization (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
policy optimization » topology optimization (توسيع البحث), wolf optimization (توسيع البحث), process optimization (توسيع البحث)
design optimization » bayesian optimization (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
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Related Work Summary.
منشور في 2025"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. 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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Simulation parameters.
منشور في 2025"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. 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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Training losses for N = 10.
منشور في 2025"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. 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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Normalized computation rate for N = 10.
منشور في 2025"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. 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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Summary of Notations Used in this paper.
منشور في 2025"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. 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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a) Accuracy and b) selected feature size of algorithms on the COVID-19 dataset.
منشور في 2022الموضوعات: -
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Boxplots analysis of the tested algorithms using average error rate across 21 datasets.
منشور في 2022الموضوعات: -
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Triplet Matching for Estimating Causal Effects With Three Treatment Arms: A Comparative Study of Mortality by Trauma Center Level
منشور في 2021"…Few studies, however, have used matching designs with more than two groups, due to the complexity of matching algorithms. …"
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Mean fitness and standard deviation results of compared approaches on CEC2019 benchmark functions.
منشور في 2022الموضوعات: -
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