A Distributed Average Cost Reinforcement Learning approach for Power Control in Wireless 5G Networks-Reference-Cited by-同舟云学术

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A Distributed Average Cost Reinforcement Learning approach for Power Control in Wireless 5G Networks

Published:2022-06-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE World AI IoT Congress (AIIoT)
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Author:

Ornatelli Antonio¹,Giuseppi Alessandro¹,Tortorelli Andrea¹

Affiliation:

1. Antonio Ruberti Sapienza University of Rome,Department of Computer, Control, and Management Engineering,Rome,Italy

Funder

Sapienza University of Rome

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9817098/9817144/09817168.pdf?arnumber=9817168

Reference27 articles.

1. Modular Q- learning based multi-agent cooperation for robot soccer;kui-hong;Robotics and Autonomous Systems,2001

2. Multi-agent systems and social networks;enrico;Handbook of Research on Business Social Networking Organizational Managerial and Technological Dimensions,2012

3. Multi-Agent Reinforcement Learning: A Review of Challenges and Applications;lorenzo;Applied Sciences,2021

4. A comprehensive survey of multiagent reinforcement learning;lucian;IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews),2008

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