SAC: A Novel Multi-hop Routing Policy in Hybrid Distributed IoT System based on Multi-agent Reinforcement Learning-Reference-Cited by-同舟云学术

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SAC: A Novel Multi-hop Routing Policy in Hybrid Distributed IoT System based on Multi-agent Reinforcement Learning

Published:2021-04-07 Issue: Volume: Page:
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Container-title:2021 22nd International Symposium on Quality Electronic Design (ISQED)
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Author:

Zhang Wen,Liu Tao,Xie Mimi,Zhang Jun,Pan Chen

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9424228/9424248/09424255.pdf?arnumber=9424255

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Intelligent Networking for Energy Harvesting Powered IoT Systems;ACM Transactions on Sensor Networks;2023-12-27

2. Deep Reinforcement Learning Multi-Agent System for Resource Allocation in Industrial Internet of Things;Sensors;2022-05-28

3. Joint-optimization of Node Placement and UAV’s Trajectory for Self-sustaining Air-Ground IoT system;2022 23rd International Symposium on Quality Electronic Design (ISQED);2022-04-06

4. Energy Harvesting Aware Multi-Hop Routing Policy in Distributed IoT System Based on Multi-Agent Reinforcement Learning;2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC);2022-01-17

5. Multi-agent reinforcement learning for intelligent resource allocation in IIoT networks;2021 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT);2021-12-12

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