Fractional deep reinforcement learning for age-minimal mobile edge computing

L Jin, M Tang, M Zhang, H Wang - … of the AAAI Conference on Artificial …, 2024 - ojs.aaai.org
Mobile edge computing (MEC) is a promising paradigm for real-time applications with
intensive computational needs (eg, autonomous driving), as it can reduce the processing …
被引用次数：3 相关文章

Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing

M Tang, L Jin, M Zhang, H Wang - arXiv preprint arXiv:2312.10418, 2023 - arxiv.org
Mobile edge computing (MEC) is a promising paradigm for real-time applications with
intensive computational needs (eg, autonomous driving), as it can reduce the processing …

Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing

L Jin, M Tang, M Zhang, H Wang - arXiv e-prints, 2023 - ui.adsabs.harvard.edu
Mobile edge computing (MEC) is a promising paradigm for real-time applications with
intensive computational needs (eg, autonomous driving), as it can reduce the processing …

Fractional deep reinforcement learning for age-minimal mobile edge computing

L Jin, M Tang, M Zhang… - AAAI Conference on …, 2024 - research.monash.edu
Mobile edge computing (MEC) is a promising paradigm for real-time applications with
intensive computational needs (eg, autonomous driving), as it can reduce the processing …

Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing

L Jin, M Tang, M Zhang, H Wang - arXiv e-prints, 2023 - ui.adsabs.harvard.edu
Mobile edge computing (MEC) is a promising paradigm for real-time applications with
intensive computational needs (eg, autonomous driving), as it can reduce the processing …