scholarly journals A single-player Monte Carlo tree search method combined with node importance for virtual network embedding

2020 ◽  
Author(s):  
Guangcong Zheng ◽  
Cong Wang ◽  
Weijie Shao ◽  
Ying Yuan ◽  
Zejie Tian ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Search Method ◽  
Virtual Network ◽  
Tree Search ◽  
Virtual Network Embedding ◽  
Network Embedding ◽  
Monte Carlo Tree Search ◽  
Node Importance

Improved Monte Carlo Tree Search for Virtual Network Embedding

2021 ◽  
Author(s):  
Maxime Elkael ◽  
Hind Castel-Taleb ◽  
Badii Jouaber ◽  
Andrea Araldo ◽  
Massinissa Ait Aba
Keyword(s):  
Monte Carlo ◽  
Virtual Network ◽  
Tree Search ◽  
Virtual Network Embedding ◽  
Network Embedding ◽  
Monte Carlo Tree Search

Hardware Efficient Massive MIMO Detector Based on the Monte Carlo Tree Search Method

2017 ◽  
Vol 7 (4) ◽  
pp. 523-533 ◽  
Author(s):  
Jienan Chen ◽  
Chao Fei ◽  
Hao Lu ◽  
Gerald E. Sobelman ◽  
Jianhao Hu
Keyword(s):  
Monte Carlo ◽  
Massive Mimo ◽  
Search Method ◽  
Tree Search ◽  
Monte Carlo Tree Search

scholarly journals Gap maximum of graphene nanoflakes: a first-principles study combined with the Monte Carlo tree search method

RSC Advances ◽  
2017 ◽  
Vol 7 (60) ◽  
pp. 37881-37886 ◽  
Author(s):  
Zhi-Peng Cao ◽  
Yu-Jun Zhao ◽  
Ji-Hai Liao ◽  
Xiao-Bao Yang
Keyword(s):  
Monte Carlo ◽  
First Principles ◽  
Potential Application ◽  
Energy Gap ◽  
Search Method ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Graphene Nanoflakes ◽  
First Principles Study

The energy gap of graphene nanoflakes is important for their potential application in nano-devices; however, it is still a challenge to perform a systemic search of systems with large gaps due to the presence of numerous candidates.

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