A Multi-Agent Reinforcement Learning Approach for Blockchain-based Electricity Trading System-Reference-Cited by-同舟云学术

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A Multi-Agent Reinforcement Learning Approach for Blockchain-based Electricity Trading System

Published:2021-12 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE Global Communications Conference (GLOBECOM)
language:
Short-container-title:

Author:

Cao Yifan¹,Ren Xiaoxu¹,Qiu Chao¹,Wang Xiaofei¹,Yao Haipeng²,Yu F. Richard³

Affiliation:

1. Tianjin University,College of Intelligence and Computing,Tianjin,China

2. Beijing University of Posts and Telecommunications,School of Information and Communications Engineering,Beijing,China

3. Carleton University,School of Information Technology,Ottawa,Canada

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9685019/9685006/09685510.pdf?arnumber=9685510

Reference13 articles.

1. Distributed Energy Trading in Microgrids: A Game-Theoretic Model and Its Equilibrium Analysis

2. Resource Trading in Blockchain-Based Industrial Internet of Things

3. Multiagent Actor-Critic Network-Based Incentive Mechanism for Mobile Crowdsensing in Industrial Systems

4. Multiparty Energy Management for Grid-Connected Microgrids With Heat- and Electricity-Coupled Demand Response

5. Bitcoin: A peer-to-peer electronic cash system;nakamoto;Decentralized Bus Rev,2008

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1. Secured Storage of Smart Grid Events: Blockchain Based on Multi Agents Markov Decision Processes;2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM);2024-07-23

2. APBFT: An Adaptive PBFT Consensus for Private Blockchains;GLOBECOM 2022 - 2022 IEEE Global Communications Conference;2022-12-04

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