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pro vyhledávání: '"Jiahua, Cao"'
We consider a system to optimize duration of traffic signals using multi-agent deep reinforcement learning and Vehicle-to-Everything (V2X) communication. This system aims at analyzing independent and shared rewards for multi-agents to control duratio
Externí odkaz:
http://arxiv.org/abs/2002.09853
Background Inosine 5'-monophosphate dehydrogenase type II (IMPDH2) was thought to be involved in cancer initiation, progression, and treatment, but its biological role and underlying mechanism in pan-cancers are not fully studied. Our goal was to sys
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ab62de19eef64040c595e200bb3d7b38
https://doi.org/10.21203/rs.3.rs-2265147/v1
https://doi.org/10.21203/rs.3.rs-2265147/v1
Autor:
Yuhua Yang, Jiahua Cao
Publikováno v:
Journal of Nuclear Cardiology. 28:3085-3087
Publikováno v:
IEEE Intelligent Transportation Systems Magazine; Jul/Aug2022, Vol. 14 Issue 4, p102-120, 19p
Publikováno v:
Transportation Research Part C: Emerging Technologies. 125:103046
Recent research reveals that reinforcement learning can potentially perform optimal decision-making compared to traditional methods like Adaptive Traffic Signal Control (ATSC). With the development of knowledge through trial and error, the Deep Reinf
Publikováno v:
International Journal of Online Engineering; 2018, Vol. 14 Issue 4, p177-192, 16p