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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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Constraints Penalized Q-Learning for Safe Offline Reinforcement Learning [PDF]
AAAI Conference on Artificial Intelligence, 2021 We study the problem of safe offline reinforcement learning (RL), the goal is to learn a policy that maximizes long-term reward while satisfying safety constraints given only offline data, without further interaction with the environment. This problem is
Haoran Xu, Xianyuan Zhan, Xiangyu Zhu
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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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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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Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis [PDF]
Operational Research, 2021 This paper investigates a model-free algorithm of broad interest in reinforcement learning, namely, Q-learning. Whereas substantial progress had been made toward understanding the sample efficiency of Q-learning in recent years, it remained largely ...
Gen Li, Ee, Changxiao Cai, Yuting Wei
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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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Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes [PDF]
International Conference on Learning Representations, 2022 The potential of offline reinforcement learning (RL) is that high-capacity models trained on large, heterogeneous datasets can lead to agents that generalize broadly, analogously to similar advances in vision and NLP.
Aviral Kumar +4 more
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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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