A Routing Algorithm of Blockchain Network Based on Reinforcement Learning

Aiming at the problems of the current methods of accelerating block propagation by optimizing the blockchain network topology: (1) these methods are based on a fixed network topology, however, the network topology of the blockchain network dynamically changes; (2) these methods take use of the local centralized characteristics, which contradict the decentralized characteristics of the blockchain network, we propose a new blockchain network routing algorithm which changes the blockchain network routing problem to a reinforcement learning problem. The algorithm reacts to the change of network environment and optimizes the route based on the feedback. Therefore, a better propagation path can be obtained by reacting to network change timely, which can ultimately reduce the total propagation delay. The experimental results show that: (1) compared with the Gossip algorithm of the bitcoin network, our proposed algorithm can reduce the total propagation delay by 38% when the number of nodes exceeds 10,000 and the degree of network variation is 10%; (2) compared with Gossip, when the number of nodes reaches 10,000, and the degree of network variation increases from 1% to 10%, our proposed algorithm can reduce the variation range of the total propagation delay from 73% to 14%.