Learning to control an inverted pendulum using neural networks

Abstract

An inverted pendulum is simulated as a control task with the goal of learning to balance the pendulum with no a priori knowledge of the dynamics. In contrast to other applications of neural networks to the inverted pendulum task, performance feedback is assumed to be unavailable on each step, appearing only as a failure signal when the pendulum falls or reaches the bounds of a horizontal track. To solve this task, the controller must deal with issues of delayed performance evaluation, learning under uncertainty, and the learning of nonlinear functions. Reinforcement and temporal-difference learning methods are presented that deal with these issues to avoid unstable conditions and balance the pendulum.< >