Gaussian Processes in Reinforcement Learning

We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learning in continuous state spaces and discrete time. We demonstrate how the GP model allows evaluation of the value function in closed form. The resulting policy iteration algorithm is demonstrated on a simple problem with a two dimensional state space. Further, we speculate that the intrinsic ability of GP models to characterise distributions of functions would allow the method to capture entire distributions over future values instead of merely their expectation, which has traditionally been the focus of much of reinforcement learning.

• Type: Conference
• Year: 2003
• Where: NIPS
• Authors: Carl Edward Rasmussen, Malte Kuss
• Keywords: Gp Models, NIPS 2003, NIPS 2007, Reinforcement Learning, State Space