Simulation parameters. by Hend Bayoumi (22693738)

“…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. …”

Training losses for N = 10. by Hend Bayoumi (22693738)

“…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. …”

Normalized computation rate for N = 10. by Hend Bayoumi (22693738)

“…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. …”

Summary of Notations Used in this paper. by Hend Bayoumi (22693738)

“…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. …”

Video_1_Simulating human walking: a model-based reinforcement learning approach with musculoskeletal modeling.MP4 by Binbin Su (640741)

Pmd analysis of the SST-CRN model with several CR users. by Saraswathi M. (19353597)

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Probability of detection analysis of SST-CRN model under non-fading channels. by Saraswathi M. (19353597)

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A summary of related work in CRN. by Saraswathi M. (19353597)

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Analysis of SST-CRN model results for spectral hole exploitation probability. by Saraswathi M. (19353597)

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Illustration for the SST-CRN model. by Saraswathi M. (19353597)

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Pe investigation of the SST-CRN under fading channels. by Saraswathi M. (19353597)

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Pnf analysis of the SST-CRN model. by Saraswathi M. (19353597)

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Flowchart of CSA. by Saraswathi M. (19353597)

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Pe analysis of the SST-CRN model under non-fading channels. by Saraswathi M. (19353597)

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Probability of detection analysis of SST-CRN model under fading channels. by Saraswathi M. (19353597)

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Pd analysis of the SST-CRN model under fading channels. by Saraswathi M. (19353597)

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Probability of error analysis of SST-CRN under fading channels. by Saraswathi M. (19353597)

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Pmd analysis of the SST-CRN model. by Saraswathi M. (19353597)

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Pd analysis of the SST-CRN model under non-fading channels. by Saraswathi M. (19353597)

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Structure of DBN model. by Saraswathi M. (19353597)

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