区块链预言机节点选择的深度强化学习中间件

Deep reinforcement learning middleware for blockchain oracle node selection

优化区块链环境中现有预言机方案中的节点选择问题,以提高预言机节点选择的准确性和可靠性。引入了基于深度强化学习的区块链预言机节点选择中间件ORLM(oracle reinforcement learning model)。该中间件考虑了不同服务需求下多个节点的消耗,并建立了预言机节点的声誉值模型来评估预言机数据提供节点的声誉值,从而尽可能避免对具有恶意历史的节点的选择。通过深度强化学习DQN(deep Q network)算法,中间件能够对选择节点的过程进行优化,以在保证安全性的情况下进行更好的节点选择。实验结果表明,所提出的中间件能够更好地满足用户的服务请求,且具有较高的可扩展性和可用性,证明了引入深度强化学习来优化预言机节点选择是一个可行的方向。

This paper aimed 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 introduced ORLM,a deep reinforcement learning based middleware for blockchain prophecy machine node selection.This middleware considered the consumption of multiple nodes under different service demands and modeled 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 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.