Intelligent Consensus Layer in Learning-Driven Dynamic Architecture
Most existing blockchain systems adopt a static policy that cannot efciently deal with the dynamic environment in the blockchain system, i.e., joining and leaving of nodes, and malicious attack. Therefore, we propose a novel dynamic sharding-based blockchain framework to achieve a good balance between performance and security without compromising scalability under a dynamic environment. For the framework, a deep reinforcement learning (DRL)-based consensus is designed to acquire optimal sharding policies in a series of dynamic and high-dimensional environment states.
SkyChain: A Deep Reinforcement Learning-Empowered Dynamic Blockchain Sharding System, Best Paper Award Runner Up received in ICPP 2020 (CCF-B).