The aim of this paper is to optimize the node selection problem in existing oracle machine schemes in the blockchain environment in order to improve the accuracy and reliability of oracle machine node selection. This paper introduces ORLM (Oracle Reinforcement Learning Modles) , a deep reinforcement learning based middleware for blockchain prophecy machine node selection. This middleware considers the consumption of multiple nodes under different service demands and models the reputation value of the prophecy machine nodes to sick the reputation value of the oracle machine data-providing nodes, thus avoiding the selection of nodes with malicious history as much as possible. With the deep reinforcement learning DQN (Deep Q Network) algorithm, the middleware is able to optimize the process of selecting nodes for better node selection with security. The experimental results show that the oracle middleware is able to better satisfy the service requests of users. And it has high scalability and availability. It proves that the introduction of deep reinforcement learning to optimize the oracle node selection is a feasible direction.

[2024-01-12] Accepted Paper /doc/2023.10.0516/v1