The lack of interpretability in the field of medical image analysis has significant ethical and legal implications.

In this work, we study the relationship between these objectives.

With high-dimensional state spaces, visual reinforcement learning (RL) faces significant challenges in exploitation and exploration, resulting in low sample efficiency and training stability.

To speed up ACL, this paper proposes a robustness-aware coreset selection (RCS) method.