Autonomous Device-Oriented Blockchain Network Acceleration With Reinforcement Learning

The widespread use of consumer autonomous devices generates massive amounts of real-time data, which creates a pressing demand for secure, decentralized, and efficient data management. Blockchain technology has the prospect of solving problems because it has the characteristics of tamper resistance and trustlessness. However, when facing the access of a large number of devices, blockchain often has a relatively large performance bottleneck. The challenges come from the interaction between system parameters and the complex network conditions, and manual tuning is both difficult and inefficient. To solve this problem, we propose a blockchain network performance optimization framework based on reinforcement learning (RL), that is, DC-MADDPG. First, apply Lasso regression to find key performance-sensitive parameters to reduce the dimensionality of the tuning space. Then introduce a dual-critic architecture to suppress value overestimation and increase training stability. Also add a prioritized experience replay mechanism to focus on high-impact training samples to promote convergence. The experimental results show that this framework can increase the system throughput by 343.05% and reduce the latency by 75.05% compared with the default Hyperledger Fabric configuration.In the SmallBank scenario, the throughput of DC-MADDPG is 12.5% higher than that of MADDPG base, and the latency is reduced by 21.4%.