  Today's advanced Reinforcement Learning algorithms produce black-box
policies, that are often difficult to interpret and trust for a person. We
introduce a policy distilling algorithm, building on the CN2 rule mining
algorithm, that distills the policy into a rule-based decision system. At the
core of our approach is the fact that an RL process does not just learn a
policy, a mapping from states to actions, but also produces extra
meta-information, such as action values indicating the quality of alternative
actions. This meta-information can indicate whether more than one action is
near-optimal for a certain state. We extend CN2 to make it able to leverage
knowledge about equally-good actions to distill the policy into fewer rules,
increasing its interpretability by a person. Then, to ensure that the rules
explain a valid, non-degenerate policy, we introduce a refinement algorithm
that fine-tunes the rules to obtain good performance when executed in the
environment. We demonstrate the applicability of our algorithm on the Mario AI
benchmark, a complex task that requires modern reinforcement learning
algorithms including neural networks. The explanations we produce capture the
learned policy in only a few rules, that allow a person to understand what the
black-box agent learned. Source code:
https://gitlab.ai.vub.ac.be/yocoppen/svcn2

Artificial Intelligence;Machine Learning