Interpretable Approximation of a Deep Reinforcement Learning Agent as a Set of If-Then Rules

Abstract

In many industrial applications, one of the major bottlenecks in using advanced learning-based methods (such as reinforcement learning) for controls is the lack of interpretability of the trained agent. In this paper, we present a methodology for translating a trained reinforcement learning agent into a set of simple and easy to interpret if-then rules by using the proven universal approximation property of the rules with fuzzy predicates. Proposed methodology combines the optimality of reinforcement learning with interpretability of the theory of approximate reasoning, thus making reinforcement learning-based solutions more accessible to industrial practitioners. The framework presented in this paper has the potential to help address the fundamental problem in widespread adoption of reinforcement learning in industrial applications.