Learning Explainable Control Strategies Demonstrated on the Pole-and-Cart System

Abstract

The classical problem of balancing an inverted pendulum is commonly used to evaluate control learning techniques. Traditional learning methods aim to improve the performance of the learned controller, often disregarding comprehensibility of the learned control policies. Recently, Explainable AI (XAI) has become of great interest in the areas where humans can benefit from insights discovered by AI, or need to check whether AI’s decisions make sense. Learning qualitative models allows formulation of learned hypotheses in a comprehensible way, closer to human intuition than traditional numerical learning. In this paper, we use a qualitative approach to learning control strategies, which we demonstrate on the problem of balancing an inverted pendulum. We use qualitative induction to learn a qualitative model from experimentally collected numerical traces, and qualitative simulation to search for possible qualitative control strategies, which are tested through reactive execution. Successful behaviors provide a clear explanation of the learned control strategy.