Cooperative service caching and peer offloading in Internet of vehicles based on multi-agent meta-reinforcement learning
In order to reduce computation complexity, a two-layer mutli-RSU (road side unit) service caching and peer offloading algorithm (MPO) was proposed to decouple the optimization problem.In the designed MPO, the outer layer utilized multi-agent meta-reinforcement learning, which established long short-...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2021-06-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021104/ |
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| Summary: | In order to reduce computation complexity, a two-layer mutli-RSU (road side unit) service caching and peer offloading algorithm (MPO) was proposed to decouple the optimization problem.In the designed MPO, the outer layer utilized multi-agent meta-reinforcement learning, which established long short-term memory (LSTM) network as the meta-agent to balance decisions of local agents and accelerate learning progress.The inner layer utilized lagrange multiplier method to achieve optimal decision for RSU peer offloading.Experimental results based on real traffic data in Hangzhou demonstrate that the proposed method outperforms other methods and remains robust under large-scale workloads. |
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| ISSN: | 1000-436X |