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Machine Learning, 1992 \cal 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.
Christopher J. Watkins, Peter Dayan
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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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, 2003 Robot learning is a challenging – and somewhat unique – research domain. If a robot behavior is defined as a mapping between situations that occurred in the real world and actions to be accomplished, then the supervised learning of a robot behavior requires a set of representative examples (situation, desired action). In order to be able to gather such
Claude Touzet
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The Annals of Statistics, 2012 We develop methodology for a multistage-decision problem with flexible number of stages in which the rewards are survival times that are subject to censoring. We present a novel Q-learning algorithm that is adjusted for censored data and allows a flexible number of stages.
Yair Goldberg, Michael R. Kosorok
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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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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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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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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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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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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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