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Applications of Deep Reinforcement Learning in Communications and Networking: A Survey [PDF]
IEEE Communications Surveys and Tutorials, 2018 This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more ...
Nguyen Cong Luong +6 more
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Frontiers in Psychiatry, 2023 IntroductionMany American employers seek to alleviate employee mental health symptoms through resources like employee assistance programs (EAPs), yet these programs are often underutilized.
Ashley B. West +4 more
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A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Science, 2018 One program to rule them all Computers can beat humans at increasingly complex games, including chess and Go. However, these programs are typically constructed for a particular game, exploiting its properties, such as the symmetries of the board on which
David Silver +12 more
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The Baltic Journal of Road and Bridge Engineering, 2021 The local series system with typical common plate rubber support/pier in highway reinforced concrete girder bridge is the object of the current research.
Zhenhua Dong +3 more
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Random node reinforcement and $K$-core structure of complex networks [PDF]
Chaos, Solitons and Fractals 173, 113706 (2023), 2022 To enhance robustness of complex networked systems, a simple method is introducing reinforced nodes which always function during failure propagation. A random scheme of node reinforcement can be considered as a benchmark for finding an optimal reinforcement solution.
arxiv +1 more source
COMPOSITE REINFORCEMENT FOR REINFORCED SOIL APPLICATIONS
SOILS AND FOUNDATIONS, 2003 Composite reinforcements achieve the desired performance with a synergetic action from two components. In this paper, development of a composite reinforcement system, made of high tensile steel wire, encased in a cement mortar as a viable system for soil reinforcement applications is proposed.
Babu, Sivakumar GL +2 more
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Sample Efficient Reinforcement Learning with REINFORCE
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Policy gradient methods are among the most effective methods for large-scale reinforcement learning, and their empirical success has prompted several works that develop the foundation of their global convergence theory. However, prior works have either required exact gradients or state-action visitation measure based mini-batch stochastic gradients ...
Zhang, Junzi +3 more
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Frontiers in Artificial Intelligence, 2022 Within computational reinforcement learning, a growing body of work seeks to express an agent's knowledge of its world through large collections of predictions.
Alex Kearney +5 more
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Deep Reinforcement Learning: A Brief Survey
IEEE Signal Processing Magazine, 2017 Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higherlevel understanding of the visual world.
Kai Arulkumaran +3 more
semanticscholar +1 more source
Buildings, 2023 The corrugated steel plate shear wall (CSPSW) has high strength, ductility, and energy dissipation capacity and can be used as the lateral force-resisting system of multi-story and high-rise buildings. The vertical load transmitted by the upper floor and
Fangfang Li +4 more
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