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Dynamic deployment method based on double deep Q-network in UAV-assisted MEC systems
The unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) system leverages the high maneuverability of UAVs to provide efficient...
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Traffic signal optimization control method based on adaptive weighted averaged double deep Q network
As a critical node and major bottleneck of the urban traffic networks, the control of traffic signals at road intersections has an essential impact...
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Dueling Double Deep Q Network Strategy in MEC for Smart Internet of Vehicles Edge Computing Networks
Advancing in communication systems requires nearby devices to act as networks when devices are not in use. Such technology is mobile edge computing,...
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Double deep Q-network-based self-adaptive scheduling approach for smart shop floor
In the field of smart manufacturing, the data-driven scheduling approach has become an effective way to solve the smart shop floor scheduling problem...
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Advanced State-Aware Traffic Light Optimization Control with Deep Q-Network
The former traffic light control (TLC) system cannot effectively regulate the traffic conditions dynamically in real time due to urban growth. The...
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Double Deep Q-Network-Based Time and Energy-Efficient Mobility-Aware Workflow Migration Approach
With the emergence of the Fog paradigm, the relocation of computational capabilities to the network’s edge has become imperative to support the...
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Work Scheduling in Cloud Network Based on Deep Q-LSTM Models for Efficient Resource Utilization
Edge computing has emerged as an innovative paradigm, bringing cloud service resources closer to mobile consumers at the network's edge. This...
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Reinforced Event-Driven Evolutionary Algorithm Based on Double Deep Q-network
The real-world optimization task has long been viewed as a noteworthy challenge owing to its enormous search space. To deal with this challenge,...
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Weighted double deep Q-network based reinforcement learning for bi-objective multi-workflow scheduling in the cloud
As a promising distributed paradigm, cloud computing provides a cost-effective deploying environment for hosting scientific applications due to its...
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Session-aware recommender system using double deep reinforcement learning
Session-aware recommender systems capture user-specific preferences that emerge within multiple user sessions by leveraging the sequential nature of...
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A Heuristic Deep Q Learning for Offloading in Edge Devices in 5 g Networks
The 5G Wireless Environments have huge data transmission; therefore, there is an increase in the requests for computational tasks from Intelligent...
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A new deep neural network for forecasting: Deep dendritic artificial neural network
Deep artificial neural networks have become a good alternative to classical forecasting methods in solving forecasting problems. Popular deep neural...
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Chronological Dingo Optimizer-based Deep Maxout Network for skin cancer detection and skin lesion segmentation using Double U-Net
Skin cancer is a dreadful disease, which is mainly caused due to the heavy exposure of the human body to the ultraviolet rays emitted from the sun....
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Privacy-Preserving in Double Deep-Q-Network with Differential Privacy in Continuous Spaces
With extensive applications and remarkable performance, deep reinforcement learning is becoming one of the most important technologies that...
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A Survey on Deep Recurrent Q Networks
Reinforcement learning (RL), one of the branches of machine learning, enables a system to learn through trial and error. RL helps in solving control...
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A deep Q-learning network based active object detection model with a novel training algorithm for service robots
This paper focuses on the problem of active object detection (AOD). AOD is important for service robots to complete tasks in the family environment,...
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Deep Recurrent Q-Network for Cloud Manufacturing Scheduling Problems
As a new manufacturing mode, cloud manufacturing integrates distributed manufacturing resources and capabilities into services, providing services to...
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Deep recurrent Q-learning for energy-constrained coverage with a mobile robot
In this paper, we study the problem of coverage of an environment with an energy-constrained robot in the presence of multiple charging stations. As...
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A multi-robot deep Q-learning framework for priority-based sanitization of railway stations
Sanitizing railway stations is a relevant issue, primarily due to the recent evolution of the Covid-19 pandemic. In this work, we propose a...
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DLAReID: double-layer attention network for object re-identification
Improving feature representations is a crucial task in object re-identification (Re-ID). Enhancement of discriminative features and suppression of...
