Adaptive Resource Scheduling in Permissionless Sharded-Blockchains: A Decentralized Multiagent Deep Reinforcement Learning Approach

Yu, G; Wang, X; Ni, W; Lu, Q; Xu, X; Liu, RP; Zhu, L

Adaptive Resource Scheduling in Permissionless Sharded-Blockchains: A Decentralized Multiagent Deep Reinforcement Learning Approach

Yu, G Wang, X

Ni, W

Lu, Q Xu, X Liu, RP Zhu, L

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023, 53, (11), pp. 7256-7268
Issue Date:: 2023-11-01

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Field	Value	Language
dc.contributor.author	Yu, G
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9439-6437
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Lu, Q
dc.contributor.author	Xu, X
dc.contributor.author	Liu, RP
dc.contributor.author	Zhu, L
dc.date.accessioned	2024-04-12T05:04:49Z
dc.date.available	2024-04-12T05:04:49Z
dc.date.issued	2023-11-01
dc.identifier.citation	IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023, 53, (11), pp. 7256-7268
dc.identifier.issn	2168-2216
dc.identifier.issn	2168-2232
dc.identifier.uri	http://hdl.handle.net/10453/177834
dc.description.abstract	Existing permissionless sharded-Blockchains come on the scene. However, there is a lack of systematic formulations and experiments regarding the behaviors of individual miners. In this article, we interpret block mining in a permissionless sharded-Blockchain as a repeated $M$ -player noncooperative game with finite actions, and propose a new multiagent deep reinforcement learning (MADRL) framework to allow the miners to maximize their profits in a decentralized fashion by scheduling their resources across the shards without centralized coordination. We formulate the rewards, and design a two-scale action space for each miner to reduce the action space and expedite convergence. We also propose a new MADRL model, named Rainbow-WoLF-PHC, which allows each miner to learn its resource allocation online and converge fast to a mixed strategy Nash equilibrium. Extensive experiments show the superiority of the Rainbow-WoLF-PHC to its alternatives in terms of convergence, stability, and profitable actions. This work provides a prosperous design of an end-user-friendly permissionless sharded-Blockchain.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Systems, Man, and Cybernetics: Systems
dc.relation.isbasedon	10.1109/TSMC.2023.3296614
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Adaptive Resource Scheduling in Permissionless Sharded-Blockchains: A Decentralized Multiagent Deep Reinforcement Learning Approach
dc.type	Journal Article
utslib.citation.volume	53
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/Strength - CCSP - Centre for Cyber Security and Privacy
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-12T05:04:47Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	53
utslib.citation.issue	11

Abstract:

Existing permissionless sharded-Blockchains come on the scene. However, there is a lack of systematic formulations and experiments regarding the behaviors of individual miners. In this article, we interpret block mining in a permissionless sharded-Blockchain as a repeated $M$ -player noncooperative game with finite actions, and propose a new multiagent deep reinforcement learning (MADRL) framework to allow the miners to maximize their profits in a decentralized fashion by scheduling their resources across the shards without centralized coordination. We formulate the rewards, and design a two-scale action space for each miner to reduce the action space and expedite convergence. We also propose a new MADRL model, named Rainbow-WoLF-PHC, which allows each miner to learn its resource allocation online and converge fast to a mixed strategy Nash equilibrium. Extensive experiments show the superiority of the Rainbow-WoLF-PHC to its alternatives in terms of convergence, stability, and profitable actions. This work provides a prosperous design of an end-user-friendly permissionless sharded-Blockchain.

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http://hdl.handle.net/10453/177834