Gaussian Process Self-triggered Policy Search

Abstract

In this paper, we propose a policy search reinforcement learning method with a non-parametric policy model and self-triggered control. We formulate a self-triggered policy search that employs a control policy and an execution length policy to reduce the number of action decisions in a trial. Our method employs sparse Gaussian process as a policy model with a self-triggered control framework, and its update law for maximizing return is derived based on variational Bayesian learning. We conducted simulations for a reaching task in a two-dimensional environment and confirmed the effectiveness of our proposed method.