Results 21 to 30 of about 241,763 (280)
QIBMRMN: Design of a Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks [PDF]
International Journal of Electronics and Telecommunications, 2023 Multimedia networks utilize low-power scalar nodes to modify wakeup cycles of high-performance multimedia nodes, which assists in optimizing the power-toperformance ratios.
Minaxi Doorwar, P Malathi
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Methods and software for solar power plant cluster management
Adaptivni Sistemi Avtomatičnogo Upravlinnâ, 2022 Object is solar power plant management software. Nowadays, solar panel production technologies are developing rapidly, investments in solar energy are growing, so users are interested in increasing energy production for faster return on investment.
А. Мокрий, І. Баклан
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Journal of the American Statistical Association, 2020 Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy. Finite dimensional working models are typically used to estimate certain nuisance parameters, and misspecification of these working models can result in residual confounding and/or efficiency loss.
Ashkan Ertefaie +3 more
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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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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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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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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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Machine Learning, 1992 Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian domains. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states.
Watkins, C., Dayan, P.
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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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