Abstract:

This paper considers a stochastic control frame- work, in which the residual model uncertainty of a dynamical system is learned using a Gaussian Process (GP). In the pro- posed formulation, the residual model uncertainty consists of a nonlinear function and state-dependent noise. The proposed for- mulation uses a posterior-GP to approximate the residual model uncertainty and a prior-GP to account for state-dependent noise. The two GPs are interdependent and are thus learned jointly using an iterative algorithm. Theoretical properties of the iterative algorithm are established. Advantages of the state- dependent formulation include (i) faster convergence of the GP estimate to the unknown function as the GP learns which data samples are more trustworthy and (ii) an accurate estimate of state-dependent noise, which can, e.g., be useful for a controller or decision-maker to determine the uncertainty of an action. Simulation studies highlight these two advantages.