Introduction: Deep reinforcement learning (deep RL) integrates the principles of reinforcement learning with deep neural networks, enabling agents to excel in diverse tasks ranging from playing board games such as Go and Chess to controlling robotic...
Bionic robots possess inherent advantages for underwater operations, and research on motion control and intelligent decision making has expanded their application scope. In recent years, the application of reinforcement learning algorithms in the fie...
With the rapid development of modern industrialization in our country and the continuous improvement of people’s living standards, the changing market has put forward new requirements for traditional manufacturing enterprises. The order product...
This paper proposes a reinforcement learning-based approach that optimises bus and line control methods to solve the problem of short circuit currents in power systems. Expansion of power grids leads to concentrated power output and more lines for la...
This study presents a novel approach to a mixed-integer linear programming (MILP) model for periodic inventory management that combines reinforcement learning algorithms. The rolling horizon method (RHM) is a multi-period optimization approach that i...
Due to the uncertainty and randomness of clean energy, microgrid operation is often prone to instability, which requires the implementation of a robust and adaptive optimization scheduling method. In this paper, a model-based reinforcement learning a...
In the developing landscape of photovoltaic (PV) technology, accuracy in simulating PV cell behaviour is dominant for enhancing energy conversion efficiency. This study introduces a new approach for parameter estimation in the three-diode PV model, a...
The popularity of deep reinforcement learning (DRL) applications in economics has increased exponentially. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophis...
Extensive research has been carried out on reinforcement learning methods. The core idea of reinforcement learning is to learn methods by means of trial and error, and it has been successfully applied to robotics, autonomous driving, gaming, healthca...
Welding induced distortion causes dimensional inaccuracies in parts being produced and assembly fit-up problems during manufacturing. In this study, a framework is proposed to mitigate weld distortion at the design stage. A sequential approach is ado...
Reinforcement learning (RL), a branch of artificial intelligence (AI), is becoming more popular in a variety of application fields such as games, workplaces, and behavioral analysis, due to its ability to model complex decision-making through interac...
Unmanned aerial vehicles (UAV), also known as drones have been used for a variety of reasons and the commercial drone market growth is expected to reach remarkable levels in the near future. However, some drone users can mistakenly or intentionally f...
In recent years, inverse reinforcement learning algorithms have garnered substantial attention and demonstrated remarkable success across various control domains, including autonomous driving, intelligent gaming, robotic manipulation, and automated i...
Automated driving (AD) is a new technology that aims to mitigate traffic accidents and enhance driving efficiency. This study presents a deep reinforcement learning (DRL) method for autonomous vehicles that can safely and efficiently handle highway o...
Multi-agent deep reinforcement learning (MARL) extends deep reinforcement learning (DRL) to environments involving multiple interacting agents and has enabled applications in domains such as autonomous vehicles, robotics, unmanned aerial vehicles (UA...
Variable Speed Limit (VSL) control systems are widely studied as solutions for improving safety and throughput on urban motorways. Machine learning techniques, specifically Reinforcement Learning (RL) methods, are a promising alternative for setting...
Residual strength of corroded textile-reinforced concrete (TRC) is evaluated using the deep learning-based method, whose feasibility is demonstrated by experiment. Compared to the traditional method, the proposed method does not need to know the clim...
Steel fiber reinforced concrete (SFRC) exhibits excellent material enhancement and toughening properties. It is widely used in applications such as airport runways, highway pavements, and bridge deck overlays. In order to predict the compressive stre...
To improve the operating efficiency and economic benefits, this article proposes a modified rainbow-based deep reinforcement learning (DRL) strategy to realize the charging station (CS) optimal scheduling. As the charging process is a real-time match...
Over the past few years, the Bitcoin-based financial trading system (BFTS) has created new challenges for the power system due to the high-risk consumption of mining devices. Briefly, such a problem would be a compelling incentive for cyber-attackers...
The challenge of optimally controlling energy storage systems under uncertainty conditions, whether due to uncertain storage device dynamics or load signal variability, is well established. Recent research works tackle this problem using two primary...
Deep reinforcement learning (DRL) enables autonomous vehicles to perform complex decision making using neural networks. However, previous DRL networks only output decisions, so there is no way to determine whether the decision is proper. Reinforcemen...
One popular method for optimizing systems, referred to as ANN-PSO, uses an artificial neural network (ANN) to approximate the system and an optimization method like particle swarm optimization (PSO) to select inputs. However, with reinforcement learn...
Theory of mind (ToM) is the psychological construct by which we model another’s internal mental states. Through ToM, we adjust our own behaviour to best suit a social context, and therefore it is essential to our everyday interactions with othe...
Reinforcement learning (RL) represents an effective approach for developing autonomous navigation and obstacle avoidance capabilities in hospital automated guided vehicles (AGVs). However, real-world adoption is challenged by the need for carefully d...
Corporate human capital is a critical driver of sustainable economic growth, which is becoming increasingly important in the changing nature of work. Due to the expansion of various areas of human activity, the employee’s profile becomes multifaceted...
This study proposes a method named Hybrid Heuristic Proximal Policy Optimization (HHPPO) to implement online 3D bin-packing tasks. Some heuristic algorithms for bin-packing and the Proximal Policy Optimization (PPO) algorithm of deep reinforcement le...
In urban areas, where car congestion is increasing daily, improving traffic-signal control is a key area of study that directly affects people’s quality of life. It is expected that such improvement will reduce environmental traffic load and in...
Analog integrated circuit sizing is notoriously difficult to automate due to its complexity and scale; thus, it continues to heavily rely on human expert knowledge. This work presents a machine learning-based design automation methodology comprising...
When a policy trained with deep reinforcement learning (DRL) in simulation is deployed in the real world, its performance often deteriorates due to the Sim2Real gap. This study addresses this problem for Autonomous Surface Vessels (ASVs) by developin...
Ship path planning is a central challenge in autonomous navigation for unmanned surface vehicles and maritime autonomous surface ships. It is not simply a shortest-path problem, but a constrained sequential decision process that must reconcile collis...
This article presents two new chemical plume tracing (CPT) algorithms for using on autonomous underwater vehicles (AUVs) to locate hydrothermal vents. We aim to design effective CPT navigation algorithms that direct AUVs to trace emitted hydrothermal...
This paper considers the problem of controlling the motion of an autonomous underwater vehicle (AUV) following a leader in a leader–follower scheme based on visual information about the leader’s position. It is assumed that the leader is...
Complex question decomposition is an important research topic in the field of natural language processing (NLP). It refers to the decomposition of a compound question containing multiple ontologies and classes into a simple question containing only a...
This paper refers to a machine learning method for solving NP-hard discrete optimization problems, especially planning and scheduling. The method utilizes a special multistage decision process modeling paradigm referred to as the Algebraic Logical Me...
Temporal difference (TD) learning is a powerful framework for value function approximation in reinforcement learning. However, standard TD methods often struggle with feature representation and off-policy learning challenges. In this paper, we propos...
Online learning platforms provide diverse course resources, but this often results in the issue of information overload. Learners always want to learn courses that are appropriate for their knowledge level and preferences quickly and accurately. Effe...
Addressing the exceptional properties of aluminium alloy composites reinforced with graphene, this study presents an interpretable machine learning approach to aid in the rapid and efficient design of such materials. Initially, data on these composit...
Increasing connection rates of rooftop photovoltaic (PV) systems to electricity distribution networks has become a major concern for the distribution network service providers (DNSPs) due to the inability of existing network infrastructure to accommo...
Optimizing transportation routes to improve delivery efficiency and resource utilization in dynamic supply chain scenarios is a challenging task. Traditional route optimization methods often struggle with complex supply chain network structures and d...
Currently, one of the topical areas of application of artificial intelligence methods in industrial production is neural networks, which allow for predicting the performance properties of products and structures that depend on the characteristics of...
Reinforcement learning has shown success in solving complex control problems, yet safety remains paramount in engineering applications like energy management systems (EMS), particularly in hybrid electric vehicles (HEVs). An effective EMS is crucial...
Nowadays, Artificial Intelligence systems have expanded their competence field from research to industry and daily life, so understanding how they make decisions is becoming fundamental to reducing the lack of trust between users and machines and inc...
We consider the problem of dimensionality reduction of state space in the variational approach to the optimal control problem, in particular, in the reinforcement learning method. The control problem is described by differential algebraic equations c...
The lateral thrust device is a typical high-pressure sealed cavity structure with dual O-rings. Because the O-ring is easily damaged during the assembly process, the product quality is unqualified. To achieve high-precision assembly for this structur...
Maximum entropy adversarial inverse reinforcement learning (ME-AIRL) has garnered widespread attention for its ability to learn rewards and optimize policies from expert demonstrations. In complex multi-task environments, applying meta-learning ME-AI...
Crystallization plays a crucial role in defining the quality and functionality of products across various industries, including pharmaceutical, food and beverage, and chemical manufacturing. The process’s efficiency and outcome are significantl...
This paper introduces an approach to enhance the efficiency of urban intersections by integrating Visible Light Communication (VLC) into a multi-intersection traffic control system. The main objectives include the reduction in waiting times for vehic...
In top-coal caving, the window control of hydraulic support is a key issue to the perfect economic benefit. The window is driven by the electro-hydraulic control system whose command is produced by the control model and the corresponding algorithm. H...
To address the difficulty of obtaining the optimal driving strategy under the condition of a complex environment and changeable tasks of vehicle autonomous driving, this paper proposes an end-to-end autonomous driving strategy learning method based o...
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