Research on Deep Reinforcement Learning Method for Throughput Optimization of Internet of Vehicles Blockchain

The rapid development of Internet of vehicles (IoV) depends on the safe and reliable infrastructure for storing and sharing large amounts of data. Blockchain, a kind of distributed data storage technology that cannot be forged and tampered with, can solve the security and privacy issues of IoV. However, the low throughput of blockchain hinders its wide application in IoV. The current research on blockchain throughput optimization has poor scalability because of its action space explosion. Aiming at the above problems, a blockchain throughput optimi-zation method in IoV based on deep reinforcement learning (DRL) is proposed to maximize the transaction throughput, and optimize the throughput of the blockchain by choosing block producers and consensus algorithms, adjusting block size and block interval while ensuring the decentralization, low delay and high security of the underlying blockchain system. This method introduces the branching dueling Q-network (BDQ) framework in DRL, carries out fine-grained division for action space, and solves the problem of action space explosion of traditional deep reinforcement learning methods. Simulation results show that the proposed method can improve the throughput of blockchain in IoV effectively.