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

【Author】 Yu, Guangsheng; Wang, Xu; Ni, Wei; Lu, Qinghua; Xu, Xiwei; Liu, Ren Ping; Zhu, Liming

【Source】IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

【影响因子】11.471

【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 permission less 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.

【Keywords】Blockchain; decentralization; multiagent deep reinforcement learning (MADRL); proof-of-work (PoW); resource scheduling; sharding

【发表时间】2023 2023 AUG 2

【收录时间】2023-08-22

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