You can convert opaque deep RL policies into interpretable decision trees without sacrificing performance, enabling safer deployment in robotics and autonomous systems where explainability matters.
This paper makes deep reinforcement learning interpretable for safety-critical applications by distilling a complex neural network policy into a simple decision tree. Using physics-aware features and custom training data, the authors show that a shallow decision tree can match the performance of a sophisticated TD3 agent on the Inverted Pendulum task while remaining transparent and analyzable.