Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Shanqi Liu"'
Autor:
Pei Wang, Mengdong Yue, Luning Yang, Xiwen Luo, Jie He, Zhongxian Man, Dawen Feng, Shanqi Liu, Chuqi Liang, Yufei Deng, He Huang, Lian Hu
Publikováno v:
Agronomy, Vol 14, Iss 4, p 804 (2024)
The unmanned farm control platform is of great significance in promoting the supervision of farm production with less manpower or autonomous operation of farm machinery and the construction of farm informatization. Addressing the existing control pla
Externí odkaz:
https://doaj.org/article/3a4d9a24a30c42859eb2eb87f512bfe8
Publikováno v:
Minerals, Vol 10, Iss 9, p 737 (2020)
Nickel sulfide minerals, an important type of metal sulfides, are the major component of mantle sulfides. They are also one of the important windows for mantle partial melting, mantle metasomatism, and mantle fluid mineralization. The elasticity play
Externí odkaz:
https://doaj.org/article/fe39716ac7254a14996053ea3cd0a300
Autor:
Shanqi Liu, Hongjun Gao
Publikováno v:
Applicable Analysis. :1-15
Publikováno v:
Geochimica et Cosmochimica Acta. 340:38-50
Autor:
Shanqi Liu, Weiwei Liu, Wenzhou Chen, Guanzhong Tian, Jun Chen, Yao Tong, Junjie Cao, Yong Liu
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. :1-12
Publikováno v:
Neural Computing & Applications; Sep2023, Vol. 35 Issue 27, p19847-19863, 17p
Publikováno v:
IEEE/ASME Transactions on Mechatronics. 26:1846-1857
The number of agents in many multiagent systems in the real world, such as storage robots and drone cluster systems, continually changes. Still, most current multiagent reinforcement learning (RL) algorithms are limited to fixed network dimensions, a
Publikováno v:
Neurocomputing. 449:207-213
Self-play reinforcement learning, where agents learn by playing with themselves, has been successfully applied in many game scenarios. However, the training procedure for self-play reinforcement learning is unstable and more sample-inefficient than (
Publikováno v:
Communications in Nonlinear Science and Numerical Simulation. 121:107203
Publikováno v:
2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP).