Deep reinforcement learning based scheduling strategy in blockchain payment channel networks

Publication Type

Journal Article

Publication Date

4-2025

Abstract

With the popularity of blockchains, low transaction throughput has become a significant bottleneck in applications such as cryptocurrencies. Payment channel networks (PCNs) have received attention as a way to improve throughput. However, due to the difficulty of predicting future transactions for nodes, the transactions are prone to failure when the channel balances do not meet required conditions. It has been shown that increasing buffers (queues) in PCNs can increase the success rate of transactions and throughput. Nevertheless, there is no effective transaction scheduling strategy in buffers when transaction values are flexible and variable. To solve this problem, we first formulate the Scheduling Problem in PCNs (named PSP), and then prove it is NP-hard. We design a neural network solver based on the Sequence to Sequence (Seq2Seq) architecture and train the solver using the reinforcement learning method. With the solver, we first give two scheduling strategies to maximize transaction throughput, and then design a PCN simulator for performance evaluation. Extensive experiments are conducted to show the superiority and various performances of our proposal and illustrate that our proposal can get a significant advantage in terms of the transaction throughput compared to the existing works.

Keywords

Blockchain, payment channel networks, off-chain payments, transaction scheduling, deep reinforcement learning

Discipline

Information Security

Research Areas

Cybersecurity

Publication

IEEE/ACM Transactions on Networking

Volume

33

Issue

2

First Page

570

Last Page

582

ISSN

1063-6692

Identifier

10.1109/TNET.2024.3492034

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TNET.2024.3492034

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