跳至主導覽
跳至搜尋
跳過主要內容
國立陽明交通大學研發優勢分析平台 首頁
English
中文
首頁
人員
單位
研究成果
計畫
獎項
活動
貴重儀器
影響
按專業知識、姓名或所屬機構搜尋
Multi-tier Collaborative Deep Reinforcement Learning for Non-terrestrial Network Empowered Vehicular Connections
Yang Cao,
Shao Yu Lien
, Ying Chang Liang
智慧系統與應用研究所
研究成果
:
Conference contribution
›
同行評審
9
引文 斯高帕斯（Scopus）
總覽
指紋
指紋
深入研究「Multi-tier Collaborative Deep Reinforcement Learning for Non-terrestrial Network Empowered Vehicular Connections」主題。共同形成了獨特的指紋。
排序方式
重量
按字母排序
Keyphrases
Multi-tier
100%
Deep Reinforcement Learning (deep RL)
100%
Non-terrestrial Networks
100%
Learning-based
50%
Resource Allocation Strategy
50%
Ground Vehicles
50%
Decision Model
50%
Machine Learning Based
50%
Computing Capabilities
50%
Low Earth Orbit Satellites
50%
Resource Allocation
25%
Vehicular Ad Hoc Networks
25%
High Capacity
25%
Moving Speed
25%
Learning Scheme
25%
Comprehensive Simulation
25%
Learning Machine
25%
Resource Allocation Optimization
25%
Computer Science
Deep Reinforcement Learning
100%
Resource Allocation
100%
Terrestrial Network
100%
Decision Model
50%
Machine Learning
50%
Learning System
50%
Decision-Making
25%
Final Decision
25%
Vehicular Network
25%
Starting Point
25%
Learning Scheme
25%
Engineering
Reinforcement Learning
100%
Terrestrial Network
100%
Ground Vehicle
50%
Learning System
50%
Low Earth Orbit
50%
Model Parameter
25%
Learning Scheme
25%
Starting Point
25%
Decision Parameter
25%
Earth and Planetary Sciences
Resource Allocation
100%
Low Earth Orbit
50%
Machine Learning
50%
Decision Making
25%