BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies

The paper introduces BASIL, a novel method for generating fully interpretable, rule-based reinforcement learning policies using online evolutionary search with quality-diversity optimization, ensuring compact and transparent controllers. Empirical results on benchmark tasks demonstrate that BASIL produces symbolic policies comparable in performance to deep reinforcement learning while enhancing interpretability and human oversight.