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Constrained Deep Q-Learning Gradually Approaching Ordinary Q-Learning [PDF]
Frontiers in Neurorobotics, 2019 A deep Q network (DQN) (Mnih et al., 2013) is an extension of Q learning, which is a typical deep reinforcement learning method. In DQN, a Q function expresses all action values under all states, and it is approximated using a convolutional neural ...
Shota Ohnishi +6 more
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Using reinforcement learning in genome assembly: in-depth analysis of a Q-learning assembler [PDF]
Frontiers in BioinformaticsGenome assembly remains an unsolved problem, and de novo strategies (i.e., those run without a reference) are relevant but computationally complex tasks in genomics.
Kleber Padovani +7 more
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Reducing the Prevalence of Coronavirus (COVID-19) in Airlines Based on and the Reinforcement Artificial Intelligence [PDF]
فناوری در مهندسی هوافضا, 2022 This paper proposes a method based on the artificial intelligence reinforcement Q-learning algorithm and paired comparison technique to solve the problem of health monitoring devices shortage in airlines.
Iman Shafieenejad +3 more
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BMC Medical Imaging, 2022 Background Supervised deep learning in radiology suffers from notorious inherent limitations: 1) It requires large, hand-annotated data sets; (2) It is non-generalizable; and (3) It lacks explainability and intuition.
J. N. Stember, H. Shalu
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Promet (Zagreb), 2022 Vehicular and flying ad hoc networks (VANETs and FANETs) are becoming increasingly important with the development of smart cities and intelligent transportation systems (ITSs).
Pavle Bugarčić +2 more
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QSPCA: A two-stage efficient power control approach in D2D communication for 5G networks
Intelligent and Converged Networks, 2021 The existing literature on device-to-device (D2D) architecture suffers from a dearth of analysis under imperfect channel conditions. There is a need for rigorous analyses on the policy improvement and evaluation of network performance. Accordingly, a two-
Saurabh Chandra +4 more
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Cooperative Output Regulation By Q-learning For Discrete Multi-agent Systems In Finite-time
Journal of Applied Science and Engineering, 2022 This article studies the output regulation of discrete-time multi-agent systems with an unknown model by a finite-time optimal control algorithm based on Q-learning that uses the method of the linear quadratic regulator (LQR).
Wenjun Wei, Jingyuan Tang
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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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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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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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