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"MetaE2RL: Toward Meta-Reasoning for Energy-Efficient Multigoal ..."

Mozhgan Navardi et al. (2023)

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DOI: 10.1109/MM.2023.3318200

access: closed

type: Journal Article

metadata version: 2023-11-28

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Mozhgan Navardi, Edward Humes, Tejaswini Manjunath, Tinoosh Mohsenin:
MetaE2RL: Toward Meta-Reasoning for Energy-Efficient Multigoal Reinforcement Learning With Squeezed-Edge You Only Look Once. IEEE Micro 43(6): 29-39 (2023)

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