Application of sequential reinforcement learning to control dynamic systems

The article describes the structure of a neural reinforcement learning controller, based on the approach of asynchronous dynamic programming. The learning controller is applied to a well-known benchmark problem, the cart-pole system. In crucial difference to previous approaches, the goal of learning is not only to avoid failure, but moreover to stabilize the cart in the middle of the track, with the pole standing in an upright position. The aim is to learn high quality control trajectories known from conventional controller design, by providing only a minimum amount of a priori knowledge and teaching information.