Reinforcement learning with guided policy search using Gaussian processes

Gradient based policy search algorithms benefit largely from the availability of a properly estimated state or state-action value function which can be used to reduce the variance of the gradient estimates. Additionally the use of Gaussian processes for value function approximation provides a fully probabilistic model where - using the uncertainty in the estimated value function - we can assess the amount of exploration required. In this article we present two modalities for adjusting different characteristics of the exploration in on-line learning of control policies for problems with continuous state-action spaces. The proposed methods exploit the fully probabilistic nature of the Gaussian processes and aims to constrain the exploration only to relevant subspaces, thereby speeding up convergence. We present experiments on a simulated control task to demonstrate the validity of our algorithms.