Ranking Policy Decisions

Policies trained via Reinforcement Learning (RL) without human intervention are often needlessly complex, making them difficult to analyse and interpret. In a run with n time steps, a policy will make n decisions on actions to take; we conjecture that only a small subset of these decisions delivers value over selecting a simple default action. Given a trained policy, we propose a novel black-box method based on statistical fault localisation that ranks the states of the environment according to the importance of decisions made in those states. We argue that among other things, the ranked list of states can help explain and understand the policy. As the ranking method is statistical, a direct evaluation of its quality is hard.