Results 31 to 40 of about 6,813,126 (333)
Adaptive Control of an Inverted Pendulum by a Reinforcement Learningbased LQR Method
Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2023 Inverted pendulums constitute one of the popular systems for benchmarking control algorithms. Several methods have been proposed for the control of this system, the majority of which rely on the availability of a mathematical model.
Uğur Yıldıran
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Proceedings of the AAAI Conference on Artificial Intelligence, 2021 The Q-learning algorithm is known to be affected by the maximization bias, i.e. the systematic overestimation of action values, an important issue that has recently received renewed attention. Double Q-learning has been proposed as an efficient algorithm to mitigate this bias.
Zhu, Rong, Rigotti, Mattia
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Frontiers in Robotics and AI, 2021 The current pandemic has highlighted the need for rapid construction of structures to treat patients and ensure manufacturing of health care products such as vaccines.
Alex Grimshaw, John Oyekan
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An Improved Q-Learning Algorithm and Its Application in Path Planning
Taiyuan Ligong Daxue xuebao, 2021 Traditional Q-Learning algorithm has the problems of too many random searches and slow convergence speed. Therefore, in this paper an improved ε-Q-Learning algorithm based on traditional Q-Learning algorithm was propased and applied to path planning. The
Guojun MAO, Shimin GU
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Multi-Source Multi-Destination Hybrid Infrastructure-Aided Traffic Aware Routing in V2V/I Networks
IEEE Access, 2022 The concept of the “connected car” offers the potential for safer, more enjoyable and more efficient driving and eventually autonomous driving.
Teodor Ivanescu +3 more
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Continuous-Action Q-Learning [PDF]
Machine Learning, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
del R. Millán, José +2 more
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IEEE Access, 2023 Distribution network reconfiguration (DNR) is one of the most important methods to cope with the increasing electricity demand due to the massive integration of electric vehicles.
Nastaran Gholizadeh +2 more
semanticscholar +1 more source
Frame Size Optimization for Dynamic Framed Slotted ALOHA in RFID Systems
Jisuanji kexue yu tansuo, 2021 In recent years, the State Grid has actively promoted the construction of ubiquitous power Internet of things, so as to realize the interconnection and optimized management of things in the power system. Specifically, radio frequency identification (RFID)
HE Jindong, BU Yanling, SHI Congcong, XIE Lei
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Sparse cooperative Q-learning [PDF]
Twenty-first international conference on Machine learning - ICML '04, 2004 Learning in multiagent systems suffers from the fact that both the state and the action space scale exponentially with the number of agents. In this paper we are interested in using Q-learning to learn the coordinated actions of a group of cooperative agents, using a sparse representation of the joint state-action space of the agents.
Kok, J.R., Vlassis, N.
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Q LEARNING REGRESSION NEURAL NETWORK [PDF]
Neural Network World, 2018 In this work, a Nadaraya-Watson kernel based learning system which owns general regression neural network topology is adapted to Q learning method to evaluate a quick and efficient action selection policy for reinforcement learning problems. By means of the proposed method Q value function is generalized and learning speed of Q agent is accelerated ...
Sangiil M., Ave M.
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