Results 1 to 10 of about 9,337 (116)
Adaptive Cruise Control Based on Safe Deep Reinforcement Learning. [PDF]
Sensors (Basel)Adaptive cruise control (ACC) enables efficient, safe, and intelligent vehicle control by autonomously adjusting speed and ensuring a safe following distance from the vehicle in front. This paper proposes a novel adaptive cruise system, namely the Safety-
Zhao R +5 more
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Local and Remote Digital Pre-Distortion for 5G Power Amplifiers with Safe Deep Reinforcement Learning. [PDF]
Sensors (Basel)The demand for higher data rates and energy efficiency in wireless communication systems drives power amplifiers (PAs) into nonlinear operation, causing signal distortions that hinder performance. Digital Pre-Distortion (DPD) addresses these distortions,
Spano C +3 more
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Sensors, 2023 Autonomous driving systems are crucial complicated cyber–physical systems that combine physical environment awareness with cognitive computing. Deep reinforcement learning is currently commonly used in the decision-making of such systems.
Hongyi Chen +3 more
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Safe deep reinforcement learning in diesel engine emission control. [PDF]
Proc Inst Mech Eng Part I J Syst Control Eng, 2023 Norouzi A +4 more
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Spacecraft Rendezvous Guidance Method Based on Safe Reinforcement Learning [PDF]
Jisuanji kexue, 2023 With the increasing complexity of spacecraft rendezvous and docking tasks,the requirements for its efficiency,autonomy and reliability are highly demanded.In recent years,the introduction of reinforcement learning technology to solve the problem of ...
XING Linquan, XIAO Yingmin, YANG Zhibin, WEI Zhengmin, ZHOU Yong, GAO Saijun
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Action Constrained Deep Reinforcement Learning Based Safe Automatic Driving Method [PDF]
Jisuanji kexue, 2021 With the development of artificial intelligence,the field of autonomous driving is also growing.The deep reinforcement learning (DRL) method is one of the main research methods in this field.DRL algorithms have been reported to achieve excellent ...
DAI Shan-shan, LIU Quan
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Deep Reinforcement Learning Task Assignment Based on Domain Knowledge
IEEE Access, 2022 Deep Reinforcement Learning (DRL) methods are inefficient in the initial strategy exploration process due to the huge state space and action space in large-scale complex scenarios.
Jiayi Liu +4 more
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Journal of Modern Power Systems and Clean Energy, 2022 This paper develops deep reinforcement learning (DRL) algorithms for optimizing the operation of home energy system which consists of photovoltaic (PV) panels, battery energy storage system, and household appliances.
Chao Huang +4 more
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A Deep Reinforcement Learning Approach for Efficient, Safe and Comfortable Driving
Applied Sciences, 2023 Sensing, computing, and communication advancements allow vehicles to generate and collect massive amounts of data on their state and surroundings. Such richness of information fosters data-driven decision-making model development that considers the ...
Dinesh Cyril Selvaraj +4 more
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Sim-to-Real Deep Reinforcement Learning for Safe End-to-End Planning of Aerial Robots
Robotics, 2022 In this study, a novel end-to-end path planning algorithm based on deep reinforcement learning is proposed for aerial robots deployed in dense environments.
Halil Ibrahim Ugurlu +2 more
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