Learning to Control an Inverted Pendulum with Connectionist Networks

Abstract

An inverted pendulum is simulated and cast as a control task with the goal of learning to avoid a subset of states with no a priori knowledge of the pendulum's dynamics. To solve this task a controller must deal with the issues of delayed performance evaluation, learning under uncertainty, and the learning of nonlinear functions. These issues are addressed by connectionist learing procedures that learn to balance the pendulum.