Results 11 to 20 of about 212,805 (267)
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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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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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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Generalized Speedy Q-Learning [PDF]
IEEE Control Systems Letters, 2020 In this paper, we derive a generalization of the Speedy Q-learning (SQL) algorithm that was proposed in the Reinforcement Learning (RL) literature to handle slow convergence of Watkins' Q-learning. In most RL algorithms such as Q-learning, the Bellman equation and the Bellman operator play an important role.
Indu John +2 more
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Frame Size Optimization for Dynamic Framed Slotted ALOHA in RFID Systems
Jisuanji kexue yu tansuo, 2021 In recent years, the State Grid has actively promoted the construction of ubiquitous power Internet of things, so as to realize the interconnection and optimized management of things in the power system. Specifically, radio frequency identification (RFID)
HE Jindong, BU Yanling, SHI Congcong, XIE Lei
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, 2020 Reinforcement Learning allows us to acquire knowledge without any training data. However, for learning it takes time. In this work, we propose a method to perform Reverse action by using Retrospective Kalman Filter that estimates the state one step before. We show an experience by a Hunter Prey problem. And discuss the usefulness of our proposed method.
Kei Takahata, Takao Miura
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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.
Rong Zhu, Mattia Rigotti
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Cybernetics and Information Technologies, 2023 Fog computing is one of the emerging forms of cloud computing which aims to satisfy the ever-increasing computation demands of the mobile applications. Effective offloading of tasks leads to increased efficiency of the fog network, but at the same time ...
Bhargavi K. +2 more
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CoRR, 2023 In Reinforcement Learning the Q-learning algorithm provably converges to the optimal solution. However, as others have demonstrated, Q-learning can also overestimate the values and thereby spend too long exploring unhelpful states. Double Q-learning is a provably convergent alternative that mitigates some of the overestimation issues, though sometimes ...
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