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DSPVR: dynamic SFC placement with VNF reuse in Fog-Cloud Computing using Deep Reinforcement Learning
The advent of Network Function Virtualization (NFV) has enabled the flexible provisioning of services on Fog-Cloud Computing-based Networks (CFCN)...
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Portfolio Management of SET50 Stocks Using Deep Reinforcement Learning Methods
In this article, we propose a reinforcement learning method for develo** a stock trading strategy while optimizing investment return. Using the...
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Energy efficiency performance in RIS-based integrated satellite–aerial–terrestrial relay networks with deep reinforcement learning
Integrated satellite–aerial–terrestrial relay networks (ISATRNs) play a vital role in next-gen networks, particularly those with high-altitude...
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Enhancing Network Intrusion Detection Using Deep Reinforcement Learning: An Adaptive Learning Approach
In recent years due to the emergence of a wide variety of technologies like the Internet of Things and Cloud-based platforms the network become more...
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Deep Reinforcement Learning to Solve Stochastic Vehicle Routing Problems
In recent years, Artificial Intelligence techniques, like Deep Reinforcement Learning (DRL), have been used to propose solutions to complex...
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A Deep Reinforcement Learning-based DDoS Attack Mitigation Scheme for Securing Big Data in Fog-Assisted Cloud Environment
Cloud computing is supported by Fog computing paradigm for achieving local data investigation at edge devices by offering storage support,...
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A deep reinforcement learning approach incorporating genetic algorithm for missile path planning
The flight path planning of the missile is important in long-range air-to-ground strike missions. Constraints about missile guidance and guidance...
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Deep Reinforcement Learning for autonomous pre-failure tool life improvement
This paper develops an approach to improve a CNC machine’s tool performance and slow down its degradation rate automatically in the Pre-Failure...
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Optimum splitting computing for DNN training through next generation smart networks: a multi-tier deep reinforcement learning approach
Deep neural networks (DNNs) involving massive neural nodes grouped into different neural layers have been a promising innovation for function...
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Deep reinforcement learning-based task scheduling and resource allocation for NOMA-MEC in Industrial Internet of Things
Mobile Edge Computing (MEC) and Non-Orthogonal Multiple Access (NOMA) have been treated as promising technologies to process the delay-sensitive...
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Twin attentive deep reinforcement learning for multi-agent defensive convoy
Multi-agent defensive convoy helps provide critical safety for a leader agent. Escort agents work by coordinating their actions to protect the leader...
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Multi-path Following for Underactuated USV Based on Deep Reinforcement Learning
Recently, deep reinforcement learning (DRL) has attracted much attention in learning control policies for unmanned surface vehicles (USV) path...
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Robot Subgoal-guided Navigation in Dynamic Crowded Environments with Hierarchical Deep Reinforcement Learning
Although deep reinforcement learning has recently achieved some successes in robot navigation, there are still unsolved problems. Particularly, a...
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An adversarial environment reinforcement learning-driven intrusion detection algorithm for Internet of Things
The increasing prevalence of Internet of Things (IoT) systems has made them attractive targets for malicious actors. To address the evolving threats...
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Resource Management in Cloud Computing Using Deep Reinforcement Learning: A Survey
Next generation aircrafts will not only require high-performance and intelligent computing capabilities, but also a fast...
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VEC Collaborative Task Offloading and Resource Allocation Based on Deep Reinforcement Learning Under Parking Assistance
With the emergence of autonomous vehicles, meeting the vehicle’s computing needs for computationally intensive and latency-sensitive tasks has become...
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Medley deep reinforcement learning-based workload offloading and cache placement decision in UAV-enabled MEC networks
Internet of Things devices generate a large number of heterogeneous workloads in real-time that require specific application to tackle, and the...
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Decision-making for Connected and Automated Vehicles in Chanllenging Traffic Conditions Using Imitation and Deep Reinforcement Learning
Decision-making is the “brain” of connected and automated vehicles (CAVs) and is vitally critical to the safety of CAVs. The most of driving data...
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Multi-agent Deep Reinforcement Learning for Countering Uncrewed Aerial Systems
The proliferation of small uncrewed aerial systems (UAS) poses many threats to airspace systems and critical infrastructures. In this paper, we apply...
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Intelligent Traffic Light via Policy-based Deep Reinforcement Learning
Intelligent traffic lights in smart cities can optimally reduce traffic congestion. In this study, we employ reinforcement learning to train the...
