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Content Caching-Assisted Vehicular Edge Computing Using Multi-Agent Graph Attention Reinforcement Learning
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DOI：10.1109/TVT.2024.3479290.png)
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摘要
En 中文
In order to avoid repeated task offloading and realize the reuse of popular task computing results, we construct a novel content caching-assisted vehicular edge computing (VEC) framework. In the face of irregular network topology and unknown environmental dynamics, we further propose a multi-agent graph attention reinforcement learning (MGARL) based edge caching scheme, which utilizes the graph attention convolution kernel to integrate the neighboring nodes' features of each agent and further enhance the cooperation among agents. Our simulation results show that our proposed scheme is capable of improving the utilization of caching resources while reducing the long-term task computing latency compared to the baselines.
Keyword:
Vehicle dynamics
Network topology
Edge computing
Computational modeling
Reinforcement learning
Decision making
Topology
Indexes
Simulation
Roads
Edge caching
graph attention reinforcement learning
multi-agent
vehicular edge computing
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
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