Inertia-Constrained Reinforcement Learning to Enhance Human Motor Control Modeling [PDF]
Sensors, 2023 Locomotor impairment is a highly prevalent and significant source of disability and significantly impacts the quality of life of a large portion of the population.
Soroush Korivand +2 more
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Customer Baseline Credibility in Constrained Reinforcement Learning for Incentive-Based Demand Response [PDF]
SensorsIncentive-based demand response is an important flexibility resource for power systems with high-renewable energy penetration. However, practical incentive allocation depends not only on flexible capacity and user response uncertainty, but also on the ...
Jiyong Li, Kaiyue Wang
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Deep Reinforcement Learning Based on Similarity Constrained Dual Policy Distillation [PDF]
Jisuanji kexue, 2023 Policy distillation,a method of transferring knowledge from one policy to another,has achieved great success in challenging reinforcement learning tasks.The typical policy distillation approach uses a teacher-student policy model,where know-ledge is ...
XU Ping'an, LIU Quan
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Safe Model-Based Reinforcement Learning for Systems With Parametric Uncertainties
Frontiers in Robotics and AI, 2021 Reinforcement learning has been established over the past decade as an effective tool to find optimal control policies for dynamical systems, with recent focus on approaches that guarantee safety during the learning and/or execution phases.
S. M. Nahid Mahmud +3 more
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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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Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages
Sensors, 2021 The use of neural networks and reinforcement learning has become increasingly popular in autonomous vehicle control. However, the opaqueness of the resulting control policies presents a significant barrier to deploying neural network-based control in ...
Sampo Kuutti +2 more
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Reinforcement Learning Made Affordable for Hardware Verification Engineers
Micromachines, 2022 Constrained random stimulus generation is no longer sufficient to fully simulate the functionality of a digital design. The increasing complexity of today’s hardware devices must be supported by powerful development and simulation environments, powerful ...
Alexandru Dinu, Petre Lucian Ogrutan
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Optimizing Reinforcement Learning-Based Visual Navigation for Resource-Constrained Devices
IEEE Access, 2023 Existing work on Deep reinforcement learning-based visual navigation mainly focuses on autonomous agents with ample power and compute resources. However, Reinforcement learning for visual navigation on resource-constrained devices remains an under ...
U. Vijetha, V. Geetha
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Koopman Operator–Based Knowledge-Guided Reinforcement Learning for Safe Human–Robot Interaction
Frontiers in Robotics and AI, 2022 We developed a novel framework for deep reinforcement learning (DRL) algorithms in task constrained path generation problems of robotic manipulators leveraging human demonstrated trajectories.
Anirban Sinha, Yue Wang
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Intelligent and Converged Networks, 2023 The adoption of the Fifth Generation (5G) and beyond 5G networks is driving the demand for learning approaches that enable users to co-exist harmoniously in a multi-user distributed environment. Although resource-constrained, the Cognitive Radio (CR) has
Kagiso Rapetswa, Ling Cheng
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