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reinforcement learning

GeneTech 知识引擎共收录 293 篇相关科研实体，横跨 24 个垂直领域。数据来自 OpenAlex、arXiv、Crossref、PubMed、Europe PMC 等公开学术源，全部可溯源到原文。

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• Optimal management of unsteady water flow in large main canals using artificial intelligence: a case study of the Amu-Zang main canal
  2026 · AI Agent 生态 · Effective management of unsteady flow in large irrigation main canals is a fundamental challenge in arid-zone water resources engineering. The Amu-Zang Main Canal (AZMC), a 312 km gravity-fed system in southern Uzbekista…
• Optimal management of unsteady water flow in large main canals using artificial intelligence: a case study of the Amu-Zang main canal
  2026 · AI Agent 生态 · Effective management of unsteady flow in large irrigation main canals is a fundamental challenge in arid-zone water resources engineering. The Amu-Zang Main Canal (AZMC), a 312 km gravity-fed system in southern Uzbekista…
• An Evaluation of Synchronization and Optimization Techniques in Digital Twin Networks
  2026 · 数字孪生 · Digital Twin Network (DTN) technology has emerged as a promising approach for next-generation wireless and industrial systems by enabling real-time interaction between physical systems and their virtual counterparts. As …
• Digital Twin-Driven TD3-CPER Control for Continuous Casting
  2026 · 数字孪生 · Continuous casting is a key process in intelligent steel manufacturing, and mold level control directly affects slab quality and production stability. However, strong nonlinearity, multivariable coupling, time delays, an…
• Digital Twins for Thermal Comfort and Energy Efficiency in Buildings: A Systematic Review
  2026 · 数字孪生 · This systematic review builds upon 51 published empirical studies out of 354 studies that were published between 2020 and 2025 to assess the effectiveness of building-scale digital twins (DTs) in providing thermal comfor…
• Graph reinforcement learning for power grids: A comprehensive survey
  2026 · 新能源 · The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in traditional power grid methods. In this …
• Towards bridging the gap: Systematic sim-to-real transfer for diverse legged robots
  2026 · 机器人零部件 · Legged robots must achieve both robust locomotion and energy efficiency to be practical in real-world environments. Yet controllers trained in simulation often fail to transfer reliably, and most existing approaches negl…
• Overcoming Deceptive Landscapes in Continuous Bipedal Kinematics: A Comparative Study of SAC with Curriculum Learning and Evolutionary Reinforcement Learning
  2026 · 机器人零部件 · Bipedal locomotion presents a high-dimensional continuous control problem where gradient-based reinforcement learning frequently converges on degenerate local optima. We present a comparative study of two fundamentally d…
• Overcoming Deceptive Landscapes in Continuous Bipedal Kinematics: A Comparative Study of SAC with Curriculum Learning and Evolutionary Reinforcement Learning
  2026 · 机器人零部件 · Bipedal locomotion presents a high-dimensional continuous control problem where gradient-based reinforcement learning frequently converges on degenerate local optima. We present a comparative study of two fundamentally d…
• Power Control in Wireless Body Area Networks: A Review of Mechanisms, Challenges, and Future Directions.
  2026 · 6G 与卫星互联网 · Wireless Body Area Networks (WBANs) enable real-time data collection for medical monitoring, sports tracking, and environmental sensing, driven by Internet of Things advancements. Their layered architecture supports effi…
• The Role of Artificial Intelligence in Next-Generation Handover Decision Techniques for UAVs over 6G Networks
  2026 · 6G 与卫星互联网 · The rapid integration of unmanned aerial vehicles (UAVs) into next-generation wireless systems demands seamless and reliable handover (HO) mechanisms to ensure continuous connectivity. However, frequent topology changes,…
• Multi-Agent Reinforcement Learning for Adaptive Resource Orchestration in Cloud-Native Clusters
  2025 · AI Agent 生态 · This paper addresses the challenges of high resource dynamism and scheduling complexity in cloud-native database systems. It proposes an adaptive resource orchestration method based on multi-agent reinforcement learning.…
• DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning
  2025 · AI 安全与对齐 · Abstract General reasoning represents a long-standing and formidable challenge in artificial intelligence (AI). Recent breakthroughs, exemplified by large language models (LLMs) 1,2 and chain-of-thought (CoT) prompting 3…
• Optimizing Impact Investment Portfolios with Reinforcement Learning: A Data-Driven Framework for Balancing Financial Returns and SDG Alignment
  2025 · AI 安全与对齐 · Impact investors face a complex multi-objective optimization challenge: balancing financial returns with sustainability outcomes, particularly alignment with the UN Sustainable Development Goals (SDGs). Traditional portf…
• LAMeTA : Intent-Aware Agentic Network Optimization via a Large AI Model-Empowered Two-Stage Approach
  2025 · AI 安全与对齐 · Nowadays, Generative AI (GenAI) reshapes numerous domains by enabling machines to create content across modalities. As GenAI evolves into autonomous agents capable of reasoning, collaboration, and interaction, they are i…
• A Dual Digital Twin Framework for Reinforcement Learning: Bridging Webots and MuJoCo with Generative AI and Alignment Strategies
  2025 · AI 安全与对齐 · Deep reinforcement learning (DRL) has shown potential for robotic training in virtual environments; however, challenges remain in bridging simulation and real-world deployment. This paper introduces an extended reinforce…
• SearchExpert: A GenAI-driven framework for reasoning-intensive multimedia information fusion through fine-tuning and reinforcement learning
  2025 · AI 安全与对齐 · The rapid advancement of Generative Artificial Intelligence (GenAI) has opened new frontiers in multimodal information fusion, yet current large language model (LLM)-driven search agents remain limited in their ability t…
• DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning
  2025 · 生物计算 · Abstract General reasoning represents a long-standing and formidable challenge in artificial intelligence (AI). Recent breakthroughs, exemplified by large language models (LLMs) 1,2 and chain-of-thought (CoT) prompting 3…
• Artificial intelligence-driven approaches for the rational design of peptides with predictable aggregation propensity
  2025 · 医疗人工智能 · The rapid development of artificial intelligence has enabled accurate and efficient de novo design of protein and peptide structures. However, applying AI to design sequences with specific aggregation tendencies remains …
• Reinforcement Learning in Medical Imaging: Taxonomy, LLMs, and Clinical Challenges
  2025 · 医疗人工智能 · Reinforcement learning (RL) is being used more in medical imaging for segmentation, detection, registration, and classification. This survey provides a comprehensive overview of RL techniques applied in this domain, cate…
• Hybrid brain-computer interface using error-related potential and reinforcement learning
  2025 · 仿生智能 · Brain-computer interfaces (BCIs) offer alternative communication methods for individuals with motor disabilities, aiming to improve their quality of life through external device control. However, non-invasive BCIs using …
• A survey of decision-making and planning methods for self-driving vehicles.
  2025 · 仿生智能 · Autonomous driving technology has garnered significant attention due to its potential to revolutionize transportation through advanced robotic systems. Despite optimistic projections for commercial deployment, the develo…
• Deep Reinforcement Learning of Mobile Robot Navigation in Dynamic Environment: A Review
  2025 · 深海科技 · Deep reinforcement learning (DRL), a vital branch of artificial intelligence, has shown great promise in mobile robot navigation within dynamic environments. However, existing studies mainly focus on simplified dynamic s…
• Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
  2025 · 深海科技 · Common challenges in the area of robotics include issues such as sensor modeling, dynamic operating environments, and limited on-broad computational resources. To improve decision making, robots need a dependable framewo…
• Digital Twins Paradigm: A Systematic Review from the Reinforcement Learning Perspective
  2025 · 数字孪生 · The Digital Twins (DT) paradigm has emerged as a powerful tool for simulating and analyzing complex systems in various domains. A DT is a virtual representation of a real-world object(s) whose goal is to accurately emula…
• A Deep Reinforcement Learning-Based Concurrency Control of Federated Digital Twin for Software-Defined Manufacturing Systems
  2025 · 数字孪生 · Modern manufacturing demands real-time, scalable coordination that legacy manufacturing management systems cannot provide. Digital transformation encompasses the entire manufacturing infrastructure, which can be represen…
• Digital Twin Synchronization: Bridging the Sim-RL Agent to a Real-Time Robotic Additive Manufacturing Control
  2025 · 数字孪生 · With the rapid development of deep reinforcement learning technology, it gradually demonstrates excellent potential and is becoming the most promising solution in the robotics. However, in the smart manufacturing domain,…
• A Dual Digital Twin Framework for Reinforcement Learning: Bridging Webots and MuJoCo with Generative AI and Alignment Strategies
  2025 · 数字孪生 · Deep reinforcement learning (DRL) has shown potential for robotic training in virtual environments; however, challenges remain in bridging simulation and real-world deployment. This paper introduces an extended reinforce…
• Asynchronous Deep Reinforcement Learning for Semantic Communication and Digital-Twin Deployment in Transportation Networks
  2025 · 数字孪生 · The dynamically evolving and technologically-driven hybrid landscape of transportation networks integrated with advanced edge computing capabilities has demonstrated efficient communication and computation techniques to …
• Digital Twin-Based Reinforcement Learning for Energy Exchange Among Electric Vehicles and Base Stations in a Disaster-Affected Region
  2025 · 数字孪生 · The cellular base stations (BSs) have backup batteries to maintain uninterrupted power supply. Recent studies have shown that a backup battery may have some spare energy to act as a flexible resource. Similarly, electric…
• Guided Reinforcement Learning with Twin Delayed Deep Deterministic Policy Gradient for a Rotary Flexible-Link System
  2025 · 数字孪生 · This study proposes a robust methodology for vibration suppression and trajectory tracking in rotary flexible-link systems by leveraging guided reinforcement learning (GRL). The approach integrates the twin delayed deep …
• Deep reinforcement learning for optimal planning of production line maintenance with deterioration
  2025 · 数字孪生 · Manufacturing companies often contend with deteriorating production systems that can significantly hinder overall performance. Therefore, optimizing maintenance planning strategies plays a crucial role in enhancing produ…
• Explainable AI for reinforcement learning based dynamic scheduling solutions in semiconductor manufacturing
  2025 · 数字孪生 · Abstract The scheduling of complex job shop manufacturing environments is a difficult and NP-hard optimization problem. It is tackled increasingly with AI-based methods, often by reinforcement learning approaches. So far…
• Improving Vision-Language-Action Model with Online Reinforcement Learning
  2025 · 具身智能 · Recent studies have successfully integrated large vision-language models (VLMs) into low-level robotic control by supervised fine-tuning (SFT) with expert robotic datasets, resulting in what we term vision-language-actio…
• MoRE: Unlocking Scalability in Reinforcement Learning for Quadruped Vision-Language-Action Models
  2025 · 具身智能 · Developing versatile quadruped robots that can smoothly perform various actions and tasks in real-world environments remains a significant challenge. This paper introduces a novel vision-language-action (VLA) model, mixt…
• AAV Swarm Cooperative Search Based on Scalable Multiagent Deep Reinforcement Learning With Digital Twin-Enabled Sim-to-Real Transfer
  2025 · 具身智能 · Cooperative target search (CTS) technology is highly desirable in various multi-autonomous aerial vehicle (AAV) applications. However, searching for unknown targets in a dynamic threatening environment is a challenging p…
• RL-based Trajectory Optimization of UAV-enabled Joint Communication, Sensing and Power Transfer
  2025 · 低空经济 · In this work, an integrated communication, sensing and power transfer (ICSPT) system is investigated, which is assisted by the employment of an unmanned aerial vehicle (UAV) acting as a base station. Under this system, t…
• RL-based Trajectory Optimization of UAV-enabled Joint Communication, Sensing and Power Transfer
  2025 · 低空经济 · In this work, an integrated communication, sensing and power transfer (ICSPT) system is investigated, which is assisted by the employment of an unmanned aerial vehicle (UAV) acting as a base station. Under this system, t…
• Quantum-Driven Reinforcement Learning for Spectral Energy Optimization in Massive MIMO Hybrid Beamforming for 6G
  2025 · 隐私计算与数据要素 · Abstract The evolution of 6G wireless networks demands highly efficient beamforming strategies to optimize spectral and energy efficiency in massive MIMO systems. This study introduces a Quantum-Driven Reinforcement Lear…
• Federated Deep Reinforcement Learning for Privacy-Preserving Robotic-Assisted Surgery
  2025 · 隐私计算与数据要素 · The integration of Reinforcement Learning (RL) into robotic-assisted surgery (RAS) holds significant promise for advancing surgical precision, adaptability, and autonomous decision-making. However, the development of rob…
• Collision avoidance in UAV swarms: A learning-centric perspective on collaborative intelligence
  2025 · 隐私计算与数据要素 · As UAV swarm deployments become more prevalent in mission critical domains, collision avoidance remains a key challenge in ensuring safety, coordination, and autonomy at scale. This survey investigates the state of the a…
• Quantum-Driven Reinforcement Learning for Spectral Energy Optimization in Massive MIMO Hybrid Beamforming for 6G
  2025 · 量子计算 · Abstract The evolution of 6G wireless networks demands highly efficient beamforming strategies to optimize spectral and energy efficiency in massive MIMO systems. This study introduces a Quantum-Driven Reinforcement Lear…
• QHRMOF: A Quantum-Inspired hybrid Multi-Objective framework for Energy-Efficient task scheduling and load balancing in cloud computing
  2025 · 量子计算 · The swift expansion of cloud computing services has resulted in a notable increase in energy consumption, presenting challenges for sustainability and effective resource management in cloud data centres. This paper intro…
• Quantum noise modeling through reinforcement learning
  2025 · 量子材料 · Abstract In the current era of quantum computing, robust and efficient tools are essential to bridge the gap between simulations and quantum hardware execution. In this work, we introduce a machine learning approach to c…
• Robust quantum control using reinforcement learning from demonstration
  2025 · 量子材料 · Quantum control requires high-precision and robust control pulses to ensure optimal system performance. However, control sequences generated with a system model may suffer from model bias, leading to low fidelity. While …
• RL-based Trajectory Optimization of UAV-enabled Joint Communication, Sensing and Power Transfer
  2025 · 6G 与卫星互联网 · In this work, an integrated communication, sensing and power transfer (ICSPT) system is investigated, which is assisted by the employment of an unmanned aerial vehicle (UAV) acting as a base station. Under this system, t…
• RL-based Trajectory Optimization of UAV-enabled Joint Communication, Sensing and Power Transfer
  2025 · 6G 与卫星互联网 · In this work, an integrated communication, sensing and power transfer (ICSPT) system is investigated, which is assisted by the employment of an unmanned aerial vehicle (UAV) acting as a base station. Under this system, t…
• A Multi-Objective Genetic Algorithm-Deep Reinforcement Learning Framework for Spectrum Sharing in 6G Cognitive Radio Networks
  2025 · 6G 与卫星互联网 · The exponential growth in wireless communication demands intelligent and adaptive spectrum-sharing solutions, especially within dynamic and densely populated 6G cognitive radio networks (CRNs). This paper introduces a no…
• Multi-Agent Deep Reinforcement Learning Framework for Renewable Energy-Aware Workflow Scheduling on Distributed Cloud Data Centers
  2024 · AI Agent 生态 · The ever-increasing demand for the cloud computing paradigm has resulted in the widespread deployment of multiple datacenters, the operations of which consume very high levels of energy. The carbon footprint resulting fr…
• AGILE: A Novel Reinforcement Learning Framework of LLM Agents
  2024 · AI Agent 生态 · We introduce a novel reinforcement learning framework of LLM agents named AGILE (AGent that Interacts and Learns from Environments) designed to perform complex conversational tasks with users, leveraging LLMs, memory, to…
• Hydrogen-electricity coupling energy storage systems: Models, applications, and deep reinforcement learning algorithms
  2024 · 碳中和与 CCUS · With the maturity of hydrogen storage technologies, hydrogen-electricity coupling energy storage in green electricity and green hydrogen modes is an ideal energy system. The construction of hydrogen-electricity coupling …
• Deep reinforcement learning-based non-causal control for wave energy conversion
  2024 · 深海科技 · As one of the most promising renewable energy resources, ocean wave energy has not been widely commercialized compared to wind energy and solar energy due to its high Levelized Cost of Electricity (LCoE). It has been lon…
• Deep Reinforcement Learning for Mobile Robot Path Planning
  2024 · 深海科技 · Path planning is an important problem with the the applications in many aspects, such as video games, robotics etc. This paper proposes a novel method to address the problem of Deep Reinforcement Learning (DRL) based pat…
• Mixed-Reality Digital Twins: Leveraging the Physical and Virtual Worlds for Hybrid Sim2Real Transition of Multi-Agent Reinforcement Learning Policies
  2024 · 数字孪生 · Multi-agent reinforcement learning (MARL) for cyber-physical vehicle systems usually requires a significantly long training time due to their inherent complexity. Furthermore, deploying the trained policies in the real w…
• Maximizing throughput in NOMA-enable industrial IoT networks using digital twin and reinforcement learning
  2024 · 数字孪生 · INTRODUCTION: Increased deployment of heterogeneous and complex Industrial Internet of Things (IIoT) applications such as predictive maintenance and asset tracking places a substantial strain on the limited computational…
• Two-Timescale Synchronization and Migration for Digital Twin Networks: A Multi-Agent Deep Reinforcement Learning Approach
  2024 · 数字孪生 · Digital twins (DTs) have emerged as a promising enabler for representing the real-time states of physical worlds and realizing self-sustaining systems. In practice, DTs of physical devices, such as mobile users (MUs), ar…
• Mobility-Aware Dependent Task Offloading in Edge Computing: A Digital Twin-Assisted Reinforcement Learning Approach
  2024 · 数字孪生 · Collaborative edge computing (CEC) has emerged as a promising paradigm, enabling edge nodes to collaborate and execute tasks from end devices. Task offloading is a fundamental problem in CEC that decides when and where t…
• AI in Energy Digital Twining: A Reinforcement Learning-Based Adaptive Digital Twin Model for Green Cities
  2024 · 数字孪生 · Digital Twins (DT) have become crucial to achieve sustainable and effective smart urban solutions. However, current DT modelling techniques cannot support the dynamicity of these smart city environments. This is caused b…
• A Hybrid Task Offloading and Resource Allocation Approach for Digital Twin-Empowered UAV-Assisted MEC Network Using Federated Reinforcement Learning for Future Wireless Network
  2024 · 数字孪生 · Federated learning (FL) is proposed as a different approach for distributed learning on the edges while maintaining privacy. Existing FL methods, are mainly focused on learning deep classifying and clustering models, wit…
• Emerging Trends in Machine Learning assisted Optimization Techniques Across Intelligent Transportation systems
  2024 · 边缘智能 · Artificial intelligence (AI) plays a critical role in Intelligent Transport Systems (ITS) as urban areas grow by processing data for safety enhancements, predictive analysis, and traffic management. This results in bette…

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