Clustered Policy Decision Ranking
Mark Levin Hana Chockler
Policies trained via reinforcement learning (RL) are often very complex even
for simple tasks. In an episode with n time steps, a policy will make n
decisions on actions to take, many of which may appear non-intuitive to the
observer. Moreover, it is not clear which of these decisions directly
contribute towards achieving the reward and how significant their contribution
is. Given a trained policy, we propose a black-box method based on statistical
covariance estimation that clusters the states of the environment and ranks
each cluster according to the importance of decisions made in its states. We
compare our measure against a previous statistical fault localization based
ranking procedure.