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- W4312431364 abstract "In this paper, we present Hyperdimensional Hybrid Learning (HDHL), which combines model-free and model-based Reinforcement Learning, to effectively reduce the computational cost and environment interaction for optimizing an intelligent cloud service. We first show that Hyperdimensional Q-Learning (QHD), the state-of-the-art Hyperdimensional Computing value-based Reinforcement Learning algorithm, is computationally faster than the Deep Q-Network (DQN) for this task. In addition, we demonstrate how HDHL reduces the number of environment interactions by 4.8× to learn the near optimal configuration. Our evaluation shows that HDHL is computationally more efficient than both Q-Learning algorithms, with the total time being reduced by 21.0× compared to DQN and 16.5× compared to QHD." @default.
- W4312431364 created "2023-01-04" @default.
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- W4312431364 date "2022-10-01" @default.
- W4312431364 modified "2023-09-27" @default.
- W4312431364 title "Hyperdimensional Hybrid Learning on End-Edge-Cloud Networks" @default.
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- W4312431364 doi "https://doi.org/10.1109/iccd56317.2022.00100" @default.
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