Multi-Agent Reinforcement Learning for a Random Access Game
Author:
Affiliation:
1. Department of Electrical and Electronic Engineering, Hanyang University, Ansan, South Korea
2. Department of Information and Communication Engineering, Gyeongsang National University, Tongyeong, South Korea
Funder
Samsung Research Funding & Incubation Center of Samsung Electronics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering
Link
http://xplorestaging.ieee.org/ielx7/25/9857756/09779952.pdf?arnumber=9779952
Reference24 articles.
1. Obey or Play: Asymptotic Equivalence of Slotted Aloha with a Game Theoretic Contention Model
2. Multiplayer Bandits Without Observing Collision Information
3. Multi-Armed Bandit Learning in IoT Networks: Learning Helps Even in Non-stationary Settings
4. Accurate occupancy estimation with WiFi and bluetooth/BLE packet capture
5. Kalman filter estimation of the number of competing terminals in an IEEE 802.11 network
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1. Random Access Protocol for Massive Internet of Things Connectivity in Space–Air–Ground-Integrated Networks;IEEE Internet of Things Journal;2023-12-01
2. Multi-Agent Reinforcement Learning for a Multichannel Uplink Random Access: Congestion Game Perspective;2023 12th International Conference on Awareness Science and Technology (iCAST);2023-11-09
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