Above work was supported by JSPS KAKENHI Grant Number 21H01351. [21H01351]; JSPS KAKENHI
机构署名：
本校为第一且通讯机构
院系归属：
国家数字化学习工程技术研究中心
摘要：
Learning dynamical systems in a sample-efficient way is important for model-based control. Active learning which sequentially selects the most informative data to sample is capable of greatly reducing sample complexity. The active learning problem for dynamical systems is hard as we can not arbitrarily draw samples from the system’s state space under constraints of system dynamics. The existing approaches model the dynamical systems using Bayesian linear regression or Gaussian processes which can not be applied to complex dynamical systems wit...
