Clustering and Ranking Reinforcement Learning Policy Decisions for Improved Interpretability
A novel method for clustering and ranking reinforcement learning policy decisions based on their co-variable correlation and impact on reward attainment, enabling the construction of simpler yet high-performing policies.
