Active Learning of Dynamics Using Prior Domain Knowledge in the Sampling
  Process
Kevin S. Miller Adam J. Thorpe Ufuk Topcu
We present an active learning algorithm for learning dynamics that leverages
side information by explicitly incorporating prior domain knowledge into the
sampling process. Our proposed algorithm guides the exploration toward regions
that demonstrate high empirical discrepancy between the observed data and an
imperfect prior model of the dynamics derived from side information. Through
numerical experiments, we demonstrate that this strategy explores regions of
high discrepancy and accelerates learning while simultaneously reducing model
uncertainty. We rigorously prove that our active learning algorithm yields a
consistent estimate of the underlying dynamics by providing an explicit rate of
convergence for the maximum predictive variance. We demonstrate the efficacy of
our approach on an under-actuated pendulum system and on the half-cheetah
MuJoCo environment.