Search Results - "deep reinforcement learning (DRL)"
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An air combat maneuver decision-making approach using coupled reward in deep reinforcement learning
Published 2025-06-01Subjects: Get full textAbstract In the domain of unmanned air combat, achieving efficient autonomous maneuvering decisions presents challenges. Deep Reinforcement learning(DRL) is one of the approaches to tackle this problem. The final performance of the DRL algorithm is directly affected by the design of the reward funct...
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Cooperative Drone and Water Supply Truck Scheduling for Wildfire Fighting Using Deep Reinforcement Learning
Published 2025-06-01Subjects: Get full textWildfires often spread rapidly and cause significant casualties and economic losses. Firefighting drones carrying water capsules provide an efficient way for wildfire extinguishing, but their operational capabilities are limited by their payloads. This weakness can be compensated by using ground veh...
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Hybrid Approach for WDM Network Restoration: Deep Reinforcement Learning and Graph Neural Networks
Published 2025-01-01Subjects: “…Deep Reinforcement learning (DRL)…”Ensuring robust and efficient service restoration in Wavelength Division Multiplexing (WDM) networks is crucial for maintaining network reliability amidst failures caused by disasters, equipment malfunctions, or power outages. This article presents a hybrid framework that integrates Deep Reinforceme...
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Age of Information Minimization in Vehicular Edge Computing Networks: A Mask-Assisted Hybrid PPO-Based Method
Published 2025-04-01Subjects: Get full textWith the widespread deployment of various emerging intelligent applications, information timeliness is crucial for intelligent decision-making in vehicular networks, where vehicular edge computing (VEC) has become an important paradigm to enhance computing capabilities by offloading tasks to edge no...
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DRL-based max-min fair RIS discrete phase shift optimization for MISO-OFDM systems
Published 2023-09-01Subjects: Get full textIn this paper, we investigate a reconfigurable intelligent surface (RIS) assisted downlink orthogonal frequency division multiplexing (OFDM) transmission system. Taking into account hardware constraint, the RIS is considered to be organized into several blocks, and each block of RIS share the same p...
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Optimizing Trajectories for Rechargeable Agricultural Robots in Greenhouse Climatic Sensing Using Deep Reinforcement Learning with Proximal Policy Optimization Algorithm
Published 2025-06-01Subjects: Get full textThe experimentation of agricultural robots has been increasing in recent years, both in greenhouses and open fields. While agricultural robots are inherently useful for automating various farming tasks, their presence can also be leveraged to collect measurements along their paths. This approach ena...
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Simulation-Based Analysis of DRL Exploitation Rate for Voltage and Frequency Stability in a Single-Inverter Microgrid Control System
Published 2025-01-01Subjects: “…Deep reinforcement learning (DRL)…”This paper discusses the development and performance evaluation of a single-inverter-based microgrid control system using a Deep Reinforcement Learning (DRL) agent trained with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The controller is specifically designed to address unp...
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EADRL: Efficiency-aware adaptive deep reinforcement learning for dynamic task scheduling in edge-cloud environments
Published 2025-09-01Subjects: Get full textIn dynamic edge–cloud environments, task scheduling must adapt to fluctuations in workload and resource conditions. This paper presents Efficiency-Aware Adaptive Deep Reinforcement Learning (EADRL), a framework that introduces two key mechanisms: an adaptive learning rate and a dynamic confidence-aw...
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A Comprehensive Study on Reinforcement Learning and Deep Reinforcement Learning Schemes
Published 2024-12-01Subjects: Get full textReinforcement learning (RL) has emerged as a powerful tool for creating artificial intelligence systems (AIS) and solving problems which require sequential decision-making. Reinforcement learning has achieved some impressive achievements in recent years, surpassing humans in a variety of areas. Acc...
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Delay Constrained Hybrid Task Offloading of Internet of Vehicle: A Deep Reinforcement Learning Method
Published 2022-01-01Subjects: Get full textThe rapid development of the Internet of Things (IoTs) has driven the progress of intelligent transportation systems (ITS), which provides basic elements, such as vehicles, traffic lights, cameras, roadside units (RSUs) and their interconnected 5G communications, to constitute the Internet of vehicl...
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Insight Thoughts for Intelligent Traffic Management-Based SDN
Published 2025-07-01Subjects: Get full textThe trend towards studying traffic management in software-defined networks (SDN) is increasing widely due to its great importance in enhancing the efficiency of networks and their abilities to adapt to the increasing demands on data and modern applications. What increases the importance of these st...
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Model-Data Hybrid-Driven Real-Time Optimal Power Flow: A Physics-Informed Reinforcement Learning Approach
Published 2025-07-01Subjects: “…deep reinforcement learning (DRL)…”With the rapid development of artificial intelligence technology, DRL has shown great potential in solving complex real-time optimal power flow problems of modern power systems. Nevertheless, traditional DRL methodologies confront dual bottlenecks: (a) suboptimal coordination between exploratory beh...
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Enhanced AUV Autonomy Through Fused Energy-Optimized Path Planning and Deep Reinforcement Learning for Integrated Navigation and Dynamic Obstacle Detection
Published 2025-06-01Subjects: Get full textAutonomous Underwater Vehicles (AUVs) operating in dynamic, constrained underwater environments demand sophisticated navigation and detection fusion capabilities that traditional methods often fail to provide. This paper introduces a novel hybrid framework that synergistically fuses a Multithreaded...
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Deep reinforcement learning-based routing framework for bidirectional communication in UAV-UGV networks
Published 2025-01-01Subjects: “…Deep Reinforcement Learning (DRL)…”Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) performs crucial function in many applications like military operations, disaster management, hazardous operations and surveillance. Efficient bidirectional communication between UAVs and UGVs is necessary for effective coordination...
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