SAFE-RL: Saliency-Aware Counterfactual Explainer for Deep Reinforcement Learning Policies

arXiv:2404.18326v1 Announce Type: new
Abstract: While Deep Reinforcement Learning (DRL) has emerged as a promising solution for intricate control tasks, the lack of explainability of the learned policies impedes its uptake in safety-critical applications, such as automated driving systems (ADS). Counterfactual (CF) explanations have recently gained prominence for their ability to interpret black-box Deep Learning (DL) models. CF examples are associated with minimal changes in the input, resulting in a complementary output by the DL model. Finding such alternations, particularly …

arxiv counterfactual cs.ai cs.lg explainer policies reinforcement reinforcement learning safe type