In recent years, we have seen an increase in the adoption of blockchain-based
systems in non-financial applications, looking to benefit from what the
technology has to offer. Although many fields have managed to include
blockchain in their core functionalities, the adoption of blockchain, in
general, is constrained by the so-called trilemma trade-off between
decentralization, scalability, and security. In our previous work, we have
shown that using a digital twin for dynamically managing blockchain systems
during runtime can be effective in managing the trilemma trade-off. Our Digital
Twin leverages DDDAS feedback loop, which is responsible for getting the data
from the system to the digital twin, conducting optimisation, and updating the
physical system. This paper examines how leveraging DDDAS feedback loop can
support the optimisation component of the trilemma benefiting from
Reinforcement Learning agents and a simulation component to augment the quality
of the learned model while reducing the computational overhead required for
decision-making.

本文研究了如何利用 DDDAS 反馈循环来支持 trilemma 的优化组件，利用强化学习代理和模拟组件来提高学习模型的质量，同时减少决策所需的计算开销。