From Explainability to Interpretability: Interpretable Policies in
Reinforcement Learning Via Model Explanation
From Explainability to Interpretability: Interpretable Policies in
Reinforcement Learning Via Model Explanation
Deep reinforcement learning (RL) has shown remarkable success in complex domains, however, the inherent black box nature of deep neural network policies raises significant challenges in understanding and trusting the decision-making processes. While existing explainable RL methods provide local insights, they fail to deliver a global understanding of the model, …