As an essential branch of artificial intelligence, recommendation systems have gradually penetrated people’s daily lives. It is the active recommendation of goods or services of potential interest to users based on their preferences. Many recom...
Artificial intelligence (AI) techniques in power grid control and energy management in building automation require both deep Q-networks (DQNs) and deep deterministic policy gradients (DDPGs) in deep reinforcement learning (DRL) as off-policy algorith...
This paper proposes a reinforcement learning-based power allocation for underwater acoustic communication networks (UACNs). The objective function is formulated as maximizing channel capacity under constraints of maximum power and minimum channel cap...
Next-generation mobile networks, such as those beyond the 5th generation (B5G) and 6th generation (6G), have diverse network resource demands. Network slicing (NS) and device-to-device (D2D) communication have emerged as promising solutions for netwo...
The dynamic reconfiguration of active distribution networks (ADNDR) essentially belongs to a complex high-dimensional mixed-integer nonlinear stochastic optimization problem. Traditional mathematical optimization algorithms tend to encounter issues l...
The unmanned aerial vehicle (UAV) trajectory tracking control algorithm based on deep reinforcement learning is generally inefficient for training in an unknown environment, and the convergence is unstable. Aiming at this situation, a Markov decision...
In terms of deep reinforcement learning (RL), exploration is highly significant in achieving better generalization. In benchmark studies, ε-greedy random actions have been used to encourage exploration and prevent over-fitting, thereby improv...
The quantity of wind and photovoltaic power-based distributed generators (DGs) is continually rising within the distribution network, presenting obstacles to its safe, steady, and cost-effective functioning. Active distribution network dynamic reconf...
Several approaches have applied Deep Reinforcement Learning (DRL) to Unmanned Aerial Vehicles (UAVs) to do autonomous object tracking. These methods, however, are resource intensive and require prior knowledge of the environment, making them difficul...
Smart energy networks provide an effective means to accommodate high penetrations of variable renewable energy sources like solar and wind, which are key for the deep decarbonisation of energy production. However, given the variability of the renewab...
Deep neural networks (DNNs) have achieved great success in the field of computer vision. The high requirements for memory and storage by DNNs make it difficult to apply them to mobile or embedded devices. Therefore, compression and structure optimiza...
This paper focuses on the economic power dispatch (EPD) operation of a microgrid in an OPAL-RT environment. First, a long short-term memory (LSTM) network is proposed to forecast the load information of a microgrid to determine the output of a power...
Software-defined networking (SDN) has become one of the critical technologies for data center networks, as it can improve network performance from a global perspective using artificial intelligence algorithms. Due to the strong decision-making and ge...
The autonomous shape decision-making problem of a morphing aircraft (MA) with a variable wingspan and sweep angle is studied in this paper. Considering the continuity of state space and action space, a more practical autonomous decision-making algori...
Recently, many artificial intelligence applications in smart cars have been utilized in real life. Making an unmanned ground vehicle (UGV) capable of moving autonomously has become a critical topic. Hence, in this work, a novel method for a UGV to re...
In order to enhance the trajectory tracking accuracy of distributed-driven intelligent vehicles, this paper formulates the tasks of torque output control for longitudinal dynamics and steering angle output control for lateral dynamics as Markov decis...
By incorporating opportunistic coding, network throughput is enhanced, resulting in improved overall performance. However, applying this paradigm to Flying Ad-hoc Networks (FANETS) faces significant challenges due to the highly dynamic topology cause...
As network environments become increasingly dynamic and users’ Quality of Service (QoS) demands grow more diverse, efficient and adaptive routing strategies are urgently needed. However, traditional routing strategies suffer from limitations su...
With the vigorous development of the Internet, the network traffic of data centers has exploded, and at the same time, the network energy consumption of data centers has also increased rapidly. Existing routing algorithms only realize routing optimiz...
Ubiquitous connectivity is envisaged for the space–air–ground-integrated network (SAGIN) of future communication to meet the needs of quality of service (QoS), green communication, and “dual carbon” targeting. However, the off...
With the vigorous development of the network functions virtualization (NFV), service function chain (SFC) resource management, which aims to provide users with diversified customized services of network functions, has gradually become a research hots...
AVs are also bound to capitalize on 5G networks, which creates crucial challenges in the adaptable management of resources because they need very low latency, a high-speed connection, and energy-efficient functionality. Older approaches to optimizing...
In this article, a deep reinforcement learning-based path-following control scheme is established for an under-actuated autonomous marine vehicle (AMV) in the presence of model uncertainties and unknown marine environment disturbances is presented. B...
High-dimensional high-frequency continuous-vibration control problems often have very complex dynamic behaviors. It is difficult for the conventional control methods to obtain appropriate control laws from such complex systems to suppress the vibrati...
On the issues of global environment protection, the renewable energy systems have been widely considered. The photovoltaic (PV) system converts solar power into electricity and significantly reduces the consumption of fossil fuels from environment po...
Under the background of energy transition, the Integrated Energy System (IES) of the park has become a key carrier for enhancing the consumption capacity of renewable energy due to its multi-energy complementary characteristics. However, the high pro...
Urban traffic congestion adversely impacts vehicle mobility, fuel consumption, and environmental sustainability. Classical traffic-control schemes—fixed-time and actuated methods—suffer from rigidity and limited responsiveness, failing to...
With the rising demand for maritime surveys of infrastructure, energy resources, and environmental conditions, autonomous robotic fish have emerged as a promising solution with their biomimetic propulsion, agile motion, efficiency, and capacity for u...
The aim of this research is to investigate and reduce the torque ripple in Switched Reluctance Motor (SRM) drives, which is one of the major barriers to their acceptance for electric vehicle propulsion applications despite the advantages of robustnes...
To improve the adaptability of the hand prosthesis, we propose a new smart control for a physiological finger prosthesis using the advantages of the deep deterministic policy gradient (DDPG) algorithm. A rigid body model was developed to represent th...
Accurate trajectory tracking of redundant manipulators is difficult because the controller must simultaneously model local couplings between adjacent joints and global dependencies across the whole kinematic chain. Existing reinforcement learning met...
With the continuous development of intelligent transportation system technology, vehicle users have higher and higher requirements for low latency and high service quality of task computing. The computing offloading technology of mobile edge computin...
A heat pipe reactor (HPR) is a kind of modular small reactor with broad application prospects, and its dynamic characteristics and nuclear power control are essential to the safe and stable operation of nuclear power plants. Taking the MegaPower HPR...
In the Energy-Harvesting (EH) Cognitive Internet of Things (EH-CIoT) network, due to the broadcast nature of wireless communication, the EH-CIoT network is susceptible to jamming attacks, which leads to a serious decrease in throughput. Therefore, th...
This paper presents two forecast-driven energy trading methodologies for a grid-connected photovoltaic-battery system participating in the day-ahead electricity market. Both methodologies use bidirectional long short-term memory neural networks with...
Reliability quantification of deep reinforcement learning (DRL)-based control is a significant challenge for the practical application of artificial intelligence (AI) in safety-critical systems. This study proposes a method for quantifying the reliab...
Prioritized experience replay (PER) is an important technique in deep reinforcement learning (DRL). It improves the sampling efficiency of data in various DRL algorithms and achieves great performance. PER uses temporal difference error (TD-error) to...
Air traffic flow management (ATFM) is of crucial importance to the European Air Traffic Control System due to two factors: first, the impact of ATFM, including safety implications on ATC operations; second, the possible consequences of ATFM measures...
In order to improve the horizontal transportation efficiency of the terminal Automated Guided Vehicles (AGVs), it is necessary to focus on coordinating the time and space synchronization operation of the loading and unloading of equipment, the transp...
Urban road traffic congestion poses significant challenges to sustainable mobility in smart cities. Traditional traffic light systems, reliant on static or semi-fixed timers, fail to adapt to dynamic traffic conditions, exacerbating congestion and li...
In recent years, there has been a lot of research using reinforcement learning algorithms to train 2-legged robots to move, but there are still many challenges. The authors propose the GCTD3 method, which takes the idea of using Graph Convolutional N...
This paper proposes a vehicle-parking trajectory planning method that addresses the issues of a long trajectory planning time and difficult training convergence during automatic parking. The process involves two stages: finding a parking space and pa...
Deep Reinforcement Learning (DRL) has been an active research area in view of its capability in solving large-scale control problems. Until presently, many algorithms have been developed, such as Deep Deterministic Policy Gradient (DDPG), Twin-Delaye...
The increasing integration of distributed electricity–heat adjustable resources into distribution networks poses significant challenges for virtual power plant (VPP) dispatch, as conventional aggregation models often neglect network constraints...
As a research hotspot in the field of artificial intelligence, the application of deep reinforcement learning to the learning of the motion ability of a manipulator can help to improve the learning of the motion ability of a manipulator without a kin...
This study investigates a novel approach to robotic arm control through integrating spiking neural networks with the twin delayed deep deterministic policy gradient reinforcement learning algorithm. Specifically, it presents the first application of...
As the centralized unit (CU)—distributed unit (DU) separation in the fifth generation mobile network (5G), the multi-slice and multi-scenario, can be better applied in wireless communication. The development of the 5G network to vertical indust...
We propose a tri-fold learning algorithm, called Neuroevolution of Hybrid Neural Networks in a Robotic Agent (acronym in Spanish, NRNH-AR), based on deep reinforcement learning (DRL), with self-supervised learning (SSL) and unsupervised learning (USL...
Hydrogen energy represents an ideal medium for energy storage. By integrating hydrogen power conversion, utilization, and storage technologies with distributed wind and photovoltaic power generation techniques, it is possible to achieve complementary...
The demand for low-latency computing from the Internet of Things (IoT) and emerging applications challenges traditional cloud computing. Mobile Edge Computing (MEC) offers a solution by deploying resources at the network edge, yet terrestrial deploym...
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