结合深度强化学习的区块链分片系统性能优化

提高区块链系统吞吐量是广泛应用区块链的关键问题之一。针对以上问题，将分片技术应用到区块链系统中，通过使区块链并行处理事务提高区块链的吞吐量。将区块链分片选择问题建立为马尔科夫决策过程（Markov decision process，MDP），并设计了基于深度强化学习（deep reinforcement learning，DRL）的区块链分片最优选择策略（branching dueling Q-network shard-based blockchain，BDQSB）。所采用的BDQSB算法克服了传统DRL算法行为空间维度高、神经网络难以训练的缺点。仿真实验结果表明，所提出的方法可以有效降低行为空间维度，提高区块链处理事务的吞吐量和可扩展性。

Performance Optimization of Blockchain Sharding System Combined with Deep Reinforcement Learning

Improving the throughput of the blockchain system is one of the key issues for the widespread application of blockchain. In view of the above problems, the sharding technology is applied to the blockchain system, and the throughput of the blockchain is improved by making the blockchain process transactions in parallel. The blockchain shard selection problem is established as a Markov decision process（MDP）, and a blockchain shard optimal selection strategy（BDQSB） based on deep reinforcement learning（DRL） is designed. The adopted BDQSB algorithm overcomes the shortcomings of the traditional DRL algorithm with high behavior space dimension and slow neural network training. The simulation results show that the proposed method can effectively reduce the behavior space dimension and improve the throughput and scalability of blockchain processing transactions.